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After having drawn lessons from the recent COVID-19 pandemic for retailers in the previous chapters, in this last chapter we provide an outline on retailing over a longer time horizon. We start with projections of how the phygitalization trend in retailing will further evolve and what role data plays as a basis for a competitive advantage – on the condition of smart and ethical use. Besides looking at customers (downstream), we address the upstream in the value delivery network, focusing on how to succeed in balancing between efficiency and sustainability in the retail supply chain. Retailers face huge challenges. This chapter contributes to setting the scene for retailers to thrive in the brand-new post-pandemic aftermath.

Critical race theory (CRT) seems to face a never-ending baptism by fire. When the Trump administration sought to ban CRT from American federal training courses in 2020, this may have come as a shock to few (Lang, 2020). Perhaps of greater surprise was that mutual sentiments resonated with the UK Minister for Equalities Kemi Badenoch, a black female, who appears to oppose the teaching of CRT in principle (Thrilling, 2020). The resurgence of such denunciations is problematic in a Western world which is primed for social activism, particularly for scholars in higher education institutions, where CRT has been gaining traction as a guiding framework for research into antiracism, fairness and affirmative action. This chapter suggests that the condemnation of CRT is neither unexpected nor is it altogether absurd. Nevertheless, it aims to provide a balanced metatheoretical ‘criticism’ of CRT and offer a view on the suitability of, and prospects for, its activist research agenda in higher education. Quite often, criticisms of CRT reflect issues with its origin as a troubled bricolage of conveniently assembled ‘tenets’, which do not lend themselves easily to the burden of evidentiary production required in higher education research and practice. In this review, I analyse CRT, through its bricolage-style characteristics, as primarily an explanatory theory, with respect to its application against racialised issues in higher education policy. It is hoped this chapter offers academic and activist researchers a way past the shadow of CRT's bricolage, by defusing some of the misgivings towards its inherent limitations.

We evaluate the performance of financial analysts versus naïve models in making long-term earnings forecasts. Long-term earnings forecasts are generally defined as third-, fourth-, and fifth-year earnings forecasts. We find that for the fourth and fifth years, analysts' forecasts are no more accurate than naïve random walk (RW) forecasts or naïve RW with economic growth forecasts. Furthermore, naïve model forecasts contain a large amount of incremental information over analysts' long-term forecasts in explaining future actual earnings. Tests based on subsamples show that the performance of analysts' long-term forecasts declines relative to naïve model forecasts for firms with high past earnings growth and low analyst coverage. Furthermore, a model that combines a naïve benchmark (last year's earnings) with the analyst long-term earnings growth forecast does not perform better than analysts' forecasts or naïve model forecasts. Our findings suggest that analysts' long-term earnings forecasts should be used with caution by researchers and practitioners. Also, when analysts' earnings forecasts are unavailable, naïve model earnings forecasts may be sufficient for measuring long-term earnings expectations.

Public administration as an aspect of governmental activity has existed as long as political systems have been functioning and trying to achieve program objectives set by the political decision-makers. Public administration as a field of systematic study is much more recent. Advisers to rulers and commentators on the workings of government have recorded their observations from time to time in sources as varied as Kautilya's Arthasastra in ancient India, the Bible, Aristotle's Politics, and Machiavelli's The Prince, but it was not until the eighteenth century that cameralism, concerned with the systematic management of governmental affairs, became a specialty of German scholars in Western Europe. In the United States, such a development did not take place until the latter part of the nineteenth century, with the publication in 1887 of Woodrow Wilson's famous essay, “The Study of Administration,” generally considered the starting point. Since that time, public administration has become a well-recognized area of specialized interest, either as a subfield of political science or as an academic discipline in its own right.

In this chapter, we introduce the history of critical thinking briefly, starting from Socrates to contemporary contributions. Based on this history, we derive several modules for training in critical thinking via practical exercises in critical thinking. Three classic critical thinking models are introduced: Socratic questioning method, Cartesian doubting method, and Baconian empirical method. We discuss their potential for critical thinking as foundational methods. The material in this chapter is distributed in three parts. In Part I, we provide a brief history of critical thinking. In Part II, we design models of critical thinking based on its classic history. In Part III, we list some models of critical thinking based on its history, from the Renaissance period to the current times. In the last section, we also discuss critical thinking in the context of business ethics, by delineating its normative domain, assessing its characteristics, and reviewing its processes.

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

Peer-to-Peer (P2P) lending, which makes borrowers and investors meet directly through online platforms bypassing traditional financial institutions, is an emerging financing market after the traditional financial institutions crushed during the global financial crisis from 2007 to 2009. P2P lending platforms meet the credit demand more efficiently and play a vital role for the credit market and economic activity. This study sheds light on whether the credit spread of P2P lending is well predictive of economic activity compared to the bond credit spread which has been fully investigated in prior studies. Our findings show that the P2P credit spread performs similarly in predicting the economic activity as bond credit spread only during the financial crisis. However, the predictive power of P2P credit spread becomes inverse during the noncrisis periods since P2P lending platforms provide an alternative and easier financing channel to individuals who hardly borrow money for refinancing from traditional financial institutions. This study highlights the alternative role of P2P lending platform in financing and provides the evidence of different predictive powers of P2P credit spread on economic activity in different time periods.

In this chapter we provide analytical and Monte Carlo evidence that Chow and Predictive tests can be consistent against alternatives that allow structural change to occur at either end of the sample. Attention is restricted to linear regression models that may have a break in the intercept. The results are based on a novel reparameterization of the actual and potential break point locations. Standard methods parameterize both of these locations as fixed fractions of the sample size. We parameterize these locations as more general integer-valued functions. Power at the ends of the sample is evaluated by letting both locations, as a percentage of the sample size, converge to 0 or 1. We find that for a potential break point function, the tests are consistent against alternatives that converge to 0 or 1 at sufficiently slow rates and are inconsistent against alternatives that converge sufficiently quickly. Monte Carlo evidence supports the theory though large samples are sometimes needed for reasonable power.