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Sara Lee Corporation's acquisition binge in the 1980s and 1990s left the company with a portfolio of vastly different businesses operating independently of one another. It had experienced rapid top-line growth, but at the same time cash flows had declined. Sara Lee ignored both internal and external warning signs until a major transformation plan became necessary. This case examines the company's multiple turnaround attempts. The learning objective of the case is to analyze “early stage” turnaround efforts by examining how the company found itself in decline, evaluating its attempts to improve its performance, and assessing the turnaround plan.

(1) Learn to identify a specific challenging moment when reading and analyzing a turnaround plan; (2) address the implementation problems of an early stage turnaround and discuss exit options; (3) evaluate when a change of long-held beliefs and decades-long strategy by a company is warranted; (4) evaluate Sara Lee's marketing strategies in light of the disappointed retail and wholesale customers; and (5) show the similarities in traits between turnaround managers and high-growth entrepreneurs.

The purpose of this paper is to analyse urban transformation as a tourism resource. Tourism is undeniably a powerful motor for urban transformation but in return, urban transformation can represent a resource for actors related to tourism. More precisely this paper focuses on one major transformation of modern cities: gentrification.

The central hypothesis of this paper is that gentrification accompanies tourism, but that gentrification itself may also become an object of the tourist gaze. The paper focuses on local guides and small touristic entrepreneurs in order to identify the tensions that might arise. The presentation of two guided tours – “Subculture Brixton Nightlife Tour” and “Where Brooklyn At?” – will enable us to explore how the gentrification of Brixton (London) and Brooklyn (New York) may be used as a tourism resource for local private entrepreneurs.

Results presented here are based on ethnographic methods such as observation as well as content analysis and semi-directive interviews. Mobilising the historical concept of “slumming”, this paper proposes an extended conceptual framework, “neo-slumming”, to analyse evolving tourism practices in modern cities, practices that are considered here as tourism’s new frontiers.

However, as tourism transforms cities, the process itself is now of interest to tourists and thus becomes a resource for sector businesses (Naef, 2018). Yet studies about the touristification of urban transformation are still quite rare. This analysis aims to fill this gap by looking at the way a process, such as some spectacular, rapid or radical transformation of the urban fabric, can become a touristic resource associated with specific narratives and representations. In this context, the tourist gaze (Urry, 2002) is directed on a resource characterised by its ongoing change.

We examine the Stein-rule shrinkage estimator for possible improvements in estimation and forecasting when there are many predictors in a linear time series model. We consider the Stein-rule estimator of Hill and Judge (1987) that shrinks the unrestricted unbiased ordinary least squares (OLS) estimator toward a restricted biased principal component (PC) estimator. Since the Stein-rule estimator combines the OLS and PC estimators, it is a model-averaging estimator and produces a combined forecast. The conditions under which the improvement can be achieved depend on several unknown parameters that determine the degree of the Stein-rule shrinkage. We conduct Monte Carlo simulations to examine these parameter regions. The overall picture that emerges is that the Stein-rule shrinkage estimator can dominate both OLS and principal components estimators within an intermediate range of the signal-to-noise ratio. If the signal-to-noise ratio is low, the PC estimator is superior. If the signal-to-noise ratio is high, the OLS estimator is superior. In out-of-sample forecasting with AR(1) predictors, the Stein-rule shrinkage estimator can dominate both OLS and PC estimators when the predictors exhibit low persistence.