FUTURES & FORESIGHT SCIENCE最新文献

I welcome the publication of Alessandro Fergnani and Thomas J. Chermack's article: “The resistance to scientific theory in futures and foresight, and what to do about it”. A discussion of the central importance of theory for advancing the science and practice of futures and foresight is long overdue, as is an appreciation of how a lack of reflexivity on the nature and role of theory and theorizing is potentially undermining attempts to establish the scientific credibility of futures and foresight practices and processes in organizations and, indeed, the credibility of the field as a legitimate focus of social scientific inquiry.

Theorizing generative mechanisms that enable futures and foresight processes and tools to deliver their intended effects, and enriching understanding of the mechanisms that detract from this endeavor, surely has to be a sensible way forward, to the benefit of science and practice alike. Addressing these fundamental issues, however, demands attention to a wider-ranging assortment of mechanisms than the ones identified by Fergnani and Chermack; for the remedies they propose will only partially bridge the academic-practitioner divides at the heart of their analysis, which are unfortunately as apparent in the field of futures and foresight as they are in applied psychology and business and management studies (among many other fields), as has been documented extensively elsewhere (see, e.g., Anderson, Herriot, & Hodgkinson, 2001; Bartunek & Rynes, 2014; Healey & Hodgkinson, 2008; Hodgkinson, 2002; Hodgkinson & Starkey, 2011; Huff, 2000; Kieser, Nicolai, & Seidl, 2015; Starkey & Madan, 2001; Tranfield & Starkey, 1998).

Fergnani and Chermack's account of what constitutes bona fide scientific theory, and what such theory is not, is expertly crafted, as is their analysis of how scientific theory typically progresses in traditional fields of study, and I agree with their assessment that critical realist philosophers (e.g., Bhaskar, 1998, 2008, 2011) have laid the essential ontological and epistemological foundations for theorizing futures and foresight practices and processes. However, it should also be noted that they are by no means the first futures and foresight scholars to have advocated critical realism for this purpose (see, e.g Derbyshire, 2019; Frith & Tapinos, 2020; Hodgkinson & Healey, 2018; Patomäki, 2006).

I particularly welcome the fact that the central problems identified by Fergnani and Chermack are analyzed systemically, and that the attendant remedies they propose are targeted similarly at the level of the wider social systems in which futures and foresight practices, academic researchers, and practitioners are variously embedded, entirely in keeping wit

It is sobering to realize that the age-old human imperative to “make sense of the future” is much older than the institutional manifestations of science. One could even argue that there is an evolutionary demand for the kinds of capabilities that are fostered by foresight and futures studies.

Thus, regardless of the extent to which these capabilities are scrutinized scientifically (or through the lens of social sciences, as promoted by Fergnani and Chermack (2021)), it lies in the interest of individuals and societies that the processes of building and harnessing these capabilities are guided by an accumulating body of knowledge. As noted by Fergnani and Chermack, this body of knowledge is best built by developing and testing adequate scientific theories. On this point, I tend to agree with them and warmly welcome their contribution.

Yet foresight and futures studies are not alone in having faced criticisms concerning the lack of theoretical soundness in offering decision and policy advice. Analogous arguments have been raised in macroeconomics, for example. Specifically, Colander (2011) addresses reasons why some researchers have concluded that macroeconomics does not exhibit the attributes which are required of fundamental science. One of these reasons is that the scientific understanding of a phenomenon as complex as the real macro economy remains limited. Furthermore, Colander argues that the standard macroeconomics has confounded fundamental science and policy applications in ways that undermine both. Indeed, juxtaposing these two papers shows that there are notable parallels between the concepts “macroeconomics” and “models” in Colander (2011) and “futures studies” and “scenarios” in Fergnani and Chermack (2021), respectively.

One implication from Colander (2011) is that if the complexity of the macro economy (as a real phenomenon) is why macroeconomics (as a scientific discipline) has encountered difficulties in ensuring its soundness, then it should not be surprising that futures studies should encounter difficulties, too, because macro economy is but a part of the world at large about which futurists make future-oriented policy relevant statements.

A trend in many countries is that researchers are increasingly encouraged and even pressured to derive policy implications from their research and to communicate these to policy makers and stakeholders in society. For instance, the Strategic Research Council of the Academy of Finland has been established as a funding instrument which stresses policy impacts. All projects funded by this instrument need to carry out well-planned efforts to exert demonstrable impact on policy and society. Embedded in this requirement is the possibility that the crucial steps in deriving policy recommendations from research are less sound than the execution of the underlying research proper. In part, this is because the tra

In this short methodological paper, we introduce four issues regarding the process of a horizon scanning exercise. During our horizon scanning project about the future of higher education, we met with several methodological challenges that influenced the data gathering and analysis of our research. The four issues were as follows: (1) finding the right template of data gathering, (2) identifying the right focus of our exercise, (3) the effect of the chosen target group on the process, and (4) the issue of blind spots of the analyzed discourse. By making our research dilemmas and our answers transparent, we would like to highlight how these issues and our decisions shaped the process and the output. In this manner, we demonstrate the iterative aspect of data-gathering and analysis phases in horizon scanning processes. By discussing these four challenges, we also attempt to emphasize that methodological decisions mutually affect each other and in turn the process itself. Furthermore, this way we could provide methodological insights for researchers who encounter similar decision points during their horizon scanning.