Correction Constants for Present Values of Seasonal Cash Flows

In a recent Decision Sciences article, McMath (1990) developed the correction constants approach for eliminating the end-of-year bias in the present value of streams with subannual cash flows. A limitation of this approach is that it assumes subannual cash flows are level. In many types of businesses, subannual cash flows follow a predictable seasonal pattern and, consequently, a present value estimate based upon a level correction constant is biased. This article derives a general formula for determining correction constants for seasonal cash flow patterns, examines the direction and magnitude of the seasonal bias, and applies seasonal correction constants to a capital budgeting problem.     

A future agenda for research on climate change and human mobility

In the past 15 years, research activities focusing on the interlinkages between climate change and human mobility have intensified. At the same time, an increasing number of actors and processes have sought to address human mobility in the context of climate change from a policy perspective. Hitherto, research has been limited in terms of geographical preferences as well as conceptual and methodological focus areas. This paper argues that to address the evolving policy space, future research on climate change in the context of human mobility needs to become more differentiated, integrated and generalized. This includes concerted efforts to better integrate researchers from the global South, improved cross-linkages between different datasets, approaches and disciplines, more longitudinal and comparative studies and development of innovative qualitative and quantitative methods.     

The origin of semilingualism: Nils-Erik Hansegård and the cult of the mother tongue

‘Semilingualism’ is one of the most questionable theories produced in the language sciences. Yet, little is known about its origins. We present a critical account of the history of semilingualism, tracing its roots in the work of Nils Erik Hansegård, (1918–2002), inaugural chair of Sámi at Umeå University (1975–1979), who developed a theory of semilingualism (halvspråkighet) in the 1960s. We show how Hansegård theorized semilingualism using ideas from Nazi German linguistics, producing an unforgiving theory of linguistic pathology directed at minoritized bilinguals in Sweden's far north.     

Statistical evidence and surprise unified under possibility theory

Sander Greenland argues that reported results of hypothesis tests should include the surprisal, the base-2 logarithm of the reciprocal of a p-value. The surprisal measures how many bits of evidence in the data warrant rejecting the null hypothesis. A generalization of surprisal also can measure how much the evidence justifies rejecting a composite hypothesis such as the complement of a confidence interval. That extended surprisal, called surprise, quantifies how many bits of astonishment an agent believing a hypothesis would experience upon observing the data. While surprisal is a function of a point in hypothesis space, surprise is a function of a subset of hypothesis space. Satisfying the conditions of conditional min-plus probability, surprise inherits a wealth of tools from possibility theory. The equivalent compatibility function has been recently applied to the replication crisis, to adjusting p-values for prior information, and to comparing scientific theories.     

The stratified win statistics (win ratio,win odds,and net benefit)

The win odds and the net benefit are related directly to each other and indirectly, through ties, to the win ratio. These three win statistics test the same null hypothesis of equal win probabilities between two groups. They provide similar p-values and powers, because the Z-values of their statistical tests are approximately equal. Thus, they can complement one another to show the strength of a treatment effect. In this article, we show that the estimated variances of the win statistics are also directly related regardless of ties or indirectly related through ties. Since its introduction in 2018, the stratified win ratio has been applied in designs and analyses of clinical trials, including Phase III and Phase IV studies. This article generalizes the stratified method to the win odds and the net benefit. As a result, the relations of the three win statistics and the approximate equivalence of their statistical tests also hold for the stratified win statistics.     

Stability analysis using mixed models: A critique of tolerance interval methods and a probabilistic solution

Recently, tolerance interval approaches to the calculation of a shelf life of a drug product have been proposed in the literature. These address the belief that shelf life should be related to control of a certain proportion of batches being out of specification. We question the appropriateness of the tolerance interval approach. Our concerns relate to the computational challenges and practical interpretations of the method. We provide an alternative Bayesian approach, which directly controls the desired proportion of batches falling out of specification assuming a controlled manufacturing process. The approach has an intuitive interpretation and posterior distributions are straightforward to compute. If prior information on the fixed and random parameters is available, a Bayesian approach can provide additional benefits both to the company and the consumer. It also avoids many of the computational challenges with the tolerance interval methodology.     

Institutional theory-based research on corporate social responsibility: Bringing values back in

Research applying institutional theory to corporate social responsibility (CSR) has experienced remarkable momentum. Institutional theory-based CSR research illustrates the role of values in guiding both agentic choices for CSR and the influence of institutional structures on CSR agency. Although values have been explored in this literature, systematic studies of values that seek to gain insights into the mutual relationship between agentic choices and structures are lacking. Such insights are crucial for exploring whether and how CSR is enabled or constrained. We thus ask two interrelated questions: (1) What is the role of values in institutional theory-based CSR research? (2) How and along which avenues should future institutional theory-based CSR research that focuses on values be mobilised? Based on our analysis of this line of literature from 1989 until 2021, first, we take stock of established institutional theory perspectives on CSR and disentangle what role values have played in this literature. Second, we outline how to mobilise values in future institutional CSR research based on four promising but under-investigated areas. From our literature analysis, two central functions emerge (which we label ‘bridging’ and ‘referencing’) that values can perform in the institutional analysis of CSR. Based on these two functions, our values-focused framework will help scholars examine the moral foundations that inform business–society interactions as well as understand how companies can responsibly manage those interactions with societal stakeholders.     

Why a Bayesian approach to safety analysis in pharmacovigilance is important

Large databases of routinely collected data are a valuable source of information for detecting potential associations between drugs and adverse events (AE). A pharmacovigilance system starts with a scan of these databases for potential signals of drug-AE associations that will subsequently be examined by experts to aid in regulatory decision-making. The signal generation process faces some key challenges: (1) an enormous volume of drug-AE combinations need to be tested (i.e. the problem of multiple testing); (2) the results are not in a format that allows the incorporation of accumulated experience and knowledge for future signal generation; and (3) the signal generation process ignores information captured from other processes in the pharmacovigilance system and does not allow feedback. Bayesian methods have been developed for signal generation in pharmacovigilance, although the full potential of these methods has not been realised. For instance, Bayesian hierarchical models will allow the incorporation of established medical and epidemiological knowledge into the priors for each drug-AE combination. Moreover, the outputs from this analysis can be incorporated into decision-making tools to help in signal validation and posterior actions to be taken by the regulators and companies. We discuss in this paper the apparent advantage of the Bayesian methods used in safety signal generation and the similarities and differences between the two widely used Bayesian methods. We will also propose the use of Bayesian hierarchical models to address the three key challenges and discuss the reasons why Bayesian methodology still have not been fully utilised in pharmacovigilance activities.