Disentangling preferences and limited attention: Random-utility models with consideration sets

This paper presents a model of choice with limited attention. The decision-maker forms a consideration set, from which she chooses her most preferred alternative. Both preferences and consideration sets are stochastic. While we present axiomatisations for this model, our focus is on the following identification question: to what extent can an observer retrieve probabilities of preferences and consideration sets from observed choices? Our first conclusion is a negative one: if the observed data are choice probabilities, then probabilities of preferences and consideration sets cannot be retrieved from choice probabilities. We solve the identification problem by assuming that an “enriched” dataset is observed, which includes choice probabilities under two frames. Given this dataset, the model is “fully identified”, in the sense that we can recover from observed choices (i) the probabilities of preferences (to the same extent as in models with full attention) and (ii) the probabilities of consideration sets. While a number of recent papers have developed models of limited attention that are, in a similar sense, “fully identified”, they obtain this result not by using an enriched dataset but rather by making a restrictive assumption about the default option, which our paper avoids.     

Entrepreneurial migrants from/in emerging economies: breaking taboos and stereotypes

Entrepreneurial migration from/in emerging economies, as grand societal and humanitarian challenges that we currently face, underscores the need for scholarly research. In our role as social science researchers, this special issue aimed to stimulate scholars from different social science fields to rethink more broadly about the opportunities for making an impact with our research focus on entrepreneurial migration from/in emerging economies. This article provides an overview of the theoretical, empirical, managerial, and policy implications of entrepreneurial migrants from/in emerging economies research. It puts forward key concepts and measures, explores the relations within the current broader literature on migration and entrepreneurship, and identifies several gaps that represent future research questions. We also introduce eight papers in a special section of this issue, which offer answers to critical gaps and questioning some taboos/stereotypes related entrepreneurial migrants. We conclude by outlining an agenda for engaging the academic community to extend research on entrepreneurial migrants from/in emerging economies. It is the perfect time to “make a difference” through our research, teaching, and interaction with multiple socioeconomic agents to constitute impacts that “endorse a real transformation” for supporting the migrants’ community.

     

Problems in the development and use of managerial financial models

In this paper the author identifies two related problems in financial modelling. First, the problem of implementing financial decision theory in practical decision situations and, second, the need to develop existing managerial financial models. A discussion of organization decision processes indicates possible reasons for the relative failure of management science and financial theory based models in practice and the predominance of accounting based models. This analysis of strategic decision processes suggests key design features for the implementation of financial models. Finally, research orientated models designed using these guidelines are seen as appropriate media for the transfer of knowledge between financial decision theory and financial practice. This is seen as providing a tentative basis for the development of managerial financial models. Throughout the paper the author uses examples from his own experience in model building in instalment credit, and leasing financial intermediaries, i.e. “Finance Houses”.     

Plausibility,not science,has dominated public discussions of the COVID pandemic

COVID-19 put to the test the understanding of the meaning of “science” by the medical profession, the media, and the public. Unfortunately, the vast majority of individuals were misled by those who spoke on behalf of science but who confused plausible stories with scientific explanation. Scientific understanding comes from theories, which generate hypotheses, which are, in turn, confirmed or disconfirmed by empirical evidence that is evaluated using statistical methods. In our daily lives, we may judge the validity of a hypothesis based on its plausibility, and for most trivial cases that is sufficient. But it is a mistake to imagine that science can proceed on that basis. Yet, scientists themselves are often confused about the foundations of the scientific method. “Evidence-based medicine” is now being used to discredit all medical evidence other than randomized controlled trials (RCTs), the supposed “gold standard” of medical research. This insistence on a single method that is deemed “best practice” has the ironic effect of replacing science with plausibility in medicine. RCTs fail to live up to their vaunted status because of frequent insufficiencies in randomization related to confounding errors and their magnitudes. When randomized trials were compared with observational studies in a meta-analysis of thousands of studies, the differences in conclusions were negligible. The entire framework of COVID-19 policy has been based on plausible hypotheses, not backed by genuine scientific evidence. Critics are correct in claiming that COVID-19 policies have been based on politics, not science.     

ASSESSING THE REGIONAL ECONOMIC IMPACTS OF DEFENSE ACTIVITIES: A SURVEY OF METHODS

Defense activities exercised in a specific region may alter the region's economic performance. An accurate assessment of the potential economic impacts of defense activities is a valuable undertaking to enable regional planners to prepare for changes. The variety in the methods (among others, input–output models, economic base models, Keynesian regional multipliers, fixed‐effects estimators, and case‐study approaches) inspired by geography, sociology, and political science can pose a dilemma. We detail the historical and theoretical background of each method, as well as select exemplary cases where these methods were applied. By examining old and “new” methods, we aim to construct a typology that could be valuable to all stakeholders. In this sense, defense economics can also contribute to the allied social sciences by outlining evaluation methods that may be applicable to other fields.     

Racing To Invest? The Dynamics of Competition in Ethical Drug Discovery

Recent advances in the theoretical literature have greatly expanded our understanding of the forces that shape the competitive dynamics of research and development, but a paucity of sufficiently detailed empirical data has left these insights relatively untested. We draw on unusually detailed qualitative and quantitative infernal data provided at the research program level by 10 major pharmaceutical firms to explore the usefulness of the modern literature as a source of insight into the dynamics of competition in ethical drug discovery. Our analysis focuses on one particularly compelling aspect of the literature: the suggestion that in “winner take all situations,” competition in R&D becomes a Prisoner's Dilemma, leading to overinvestment in research. Without adequate measures of the social return to innovation, we can say nothing about whether there is “too much” or “too little” research undertaken by the industry, but our results do not support the suggestion that R&D investment in drug discovery is driven by the “tit-for-tat” or simple reaction function models hinted at by the institutional literature. First, R&D investment is only weakly correlated across firms once common responses to exogenous shocks are accounted far, and second, rivals' R&D results are positively correlated with own research productivity, which we interpret as evidence for extensive R&D spillovers rather than the depletion externality implied by “winner take all” models. These results are not, of themselves, sufficient to reject the hypothesis that investment behavior in the industry is driven by strategic considerations since there is theoretical support for a wide variety of observable behavior as equilibrium outcomes of strategic interaction. Nonetheless, they suggest that the more extreme forms of rent dissipation identified in the literature are probably poor characterizations of the reality of competition in pharmaceuticals. Our results point both to the need to develop theories that incorporate richer models of possible payoff structures, adjustment costs, and firm heterogeneity and to the need to collect empirical data that is comprehensive enough to enable one to test them.     

Don't know,don't care: An exploration of evidence based knowledge and practice in human resource management

Over the past two decades the integrity (alignment of words and deeds) of the Human Resource Management (HRM) profession has been questioned by scholars who have identified a gap between the rhetoric of ‘people are our most important asset’ and the reality of ‘impersonal economic rationalism’. In a more recent, and as yet unconnected, stream of research there has been concern about a research-practice gap in HRM. This article draws on both streams of research to explain why HRM Does not implement evidence based practice. It focuses on research indicating that HRM practitioners are not incentivized to learn about evidence based practice and develops theory proposing that their satisfaction with the status quo reflects a value proposition based on utilitarian instrumentalism. Further to this, it is proposed that management's focus on the short-term drives and obfuscates current approaches. It concludes that neither academia nor HRM practitioners are incentivized to change current practice with negative consequences for employees, organizations, and HRM practitioners. Arguments are supported and illustrated with High Performance Work Practices and solutions are proposed to implement evidence based practice.     

Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?

Simple, transparent rules are often frowned upon while complex, black-box models are seen as holding greater promise. Yet in quickly changing situations, simple rules can protect against overfitting and adapt quickly. We show that the surprisingly simple recency heuristic forecasts more accurately than Google Flu Trends (GFT) which used big data analytics and a black-box algorithm. This heuristic predicts that “this week’s proportion of flu-related doctor visits equals the proportion from the most recent week.” It is based on psychological theory of how people deal with rapidly changing situations. Other theory-inspired heuristics have outperformed big data models in predicting outcomes, such as U.S. presidential elections, or other uncertain events, such as consumer purchases, patient hospitalizations, and terrorist attacks. Heuristics are transparent, clearly communicating the underlying rationale for their predictions. We advocate taking into account psychological principles that have evolved over millennia and using these as a benchmark when testing big data models.     

Selective Hearing: Physician‐Ownership and Physicians’ Response to New Evidence

Physicians, acting in their role as experts, are often faced with situations where they must trade off personal and patient welfare. Physicians’ incentives vary based on the organizational environment in which they practice. We use the publication of a major clinical trial, which found that a common knee operation does not improve outcomes for patients with osteoarthritis, as an “informational shock” to gauge the impact of physicians’ agency relationships on treatment decisions. Using a 100% sample of procedures in Florida from 1998 to 2010, we find that publication of the trial reduced procedure volume, but the magnitude of the decline was smaller in physician‐owned surgery centers. Incentives affected physicians’ reactions to evidence.     

Election forecasting: Too far out?

We consider two criteria for evaluating election forecasts: accuracy (precision) and lead (distance from the event), specifically the trade-off between the two in poll-based forecasts. We evaluate how much “lead” still allows prediction of the election outcome. How much further back can we go, supposing we tolerate a little more error? Our analysis offers estimates of the “optimal” lead time for election forecasts, based on a dataset of over 26,000 vote intention polls from 338 elections in 44 countries between 1942 and 2014. We find that optimization of a forecast is possible, and typically occurs two to three months before the election, but can be influenced by the arrangement of political institutions. To demonstrate how our optimization guidelines perform in practice, we consider recent elections in the UK, the US, and France.     

Bridging the research-practice divide: Harnessing expertise collaboration in making a wider set of contributions

Understanding how we develop research contributions which go beyond conversations in the academic field is an enduring challenge. While much has been written on the importance of academic-practitioner relationships in the research process more is needed on conceptualizing how we develop a wider set of contributions. In this paper, we call for researchers to be reflective as to how different forms of expertise can be drawn on during collaborative relationships to bridge the research – practice divide. We develop a framework which combines different levels of expertise with varying forms of academic-practitioner collaboration to widen the impact of our research. Four strategies are proposed by which academics may leverage their expertise in collaborative relationships with practitioners to develop Research Impact and Contributions To Knowledge (RICK). These include: maintaining critical distance, promoting deeper engagement, developing prescience, and achieving hybrid practices. We discuss implementation approaches for each of these RICK strategies and suggest writing genres to help increase engagement by practitioners in research contributions.     

Future business and the role of purchasing and supply management: Opportunities for ‘business-not-as-usual’ PSM research

The raison d'être for this article is simple: traditional ways of researching, theorizing, and practicing purchasing and supply management (PSM) are no longer sufficient to ‘meet the moment’. Scholars need to advance a “business-not-as-usual” footing approach to their work, if they are to make a meaningful contribution to addressing the current and future emergencies, as highlighted by recent extreme weather and the COVID-19 pandemic. Yet, what can this, or should this, mean for a field rooted in traditional business thinking? This article builds on the Journal of Purchasing and Supply Management's (JPSM) 25th Anniversary Special Issue editorial (2019); members of the JPSM's editorial team advance their unique perspectives on what “business-not-as-usual” means for PSM. Specifically, we advocate both thinking much more widely, in scope and ambition, than we currently do, and simultaneously building our ability to comprehend supply chains in a more nuanced and granular way. We explore whether the bias toward positivist work has omitted potentially interesting findings, and viewpoints. This leads to a call to re-think how we approach our work: should the key criteria always be to focus on theory development or testing? Should academics “think bigger”? Turning to specific research themes, illustrations of how our current thinking can be challenged or broadened by addressing the circular economy, and role of purchasing and innovation. Specifically, the focus on the PSM function as an intrapreneur within the larger organization, and the role of innovation and technology in PSM work. Taken together, we hope the ideas and arguments presented here will inform and inspire ambitious and novel approaches to PSM research with significant and enduring impact on the transformation of business.