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Introduction to the special issue on heuristics


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Heuristics are central to individual and organizational behavior. Yet there is surprisingly little research that directly connects the scholarly debate around heuristics with technological forecasting and social change. In this introduction, we discuss and bring together different perspectives on heuristics, and introduce the five articles included in the Special Issue on ‘Heuristics in Technological Forecasting and Social Change’. We propose different directions for further research and point to important, so far unexplored research questions that are likely to enrich future study of heuristics in the particular context of social and technological change.
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Technological Forecasting & Social Change 186 (2023) 122163
Available online 23 November 2022
0040-1625/© 2022 Published by Elsevier Inc.
Introduction to the special issue on heuristics
Simple rules
Heuristics are central to individual and organizational behavior. Yet there is surprisingly little research that
directly connects the scholarly debate around heuristics with technological forecasting and social change. In this
introduction, we discuss and bring together different perspectives on heuristics, and introduce the ve articles
included in the Special Issue on ‘Heuristics in Technological Forecasting and Social Change. We propose
different directions for further research and point to important, so far unexplored research questions that are
likely to enrich future study of heuristics in the particular context of social and technological change.
1. Introduction
Advancements in the study of heuristics provide evidence of how
heuristics are effective in forecasting and change (e.g., Gigerenzer et al.,
2022; Loock and Hinnen, 2015). Heuristics are cognitive shortcuts when
information, processing capacity and time are limited (Newell and
Simon, 1972; Pearl, 1984). As such, heuristics are regarded as founda-
tional for human adaptive intelligence (Gigerenzer et al., 2011; Tversky
and Kahneman, 1974). Their importance nds recognition in a broad
range of scholarly discussions ranging from economic change (Nelson
and Winter, 1982), medicine (Marewski and Gigerenzer, 2012), mar-
keting (Wübben and Wangenheim, 2008) to strategy (Bingham and
Eisenhardt, 2011, Maitland and Smmartino, 2014), to name only a few.
Heuristics have been postulated to be some of the most effective, and at
times, the only strategy to solve intractable decision-problems (Bettis,
Building on a vast body of knowledge across different disciplines
dedicated to the study of heuristics, this special issue points to recent
progress enabling novel views and explanations on how heuristics
matter in the context of change and forecasting. Past work provided a
more granular understanding of how heuristics work: Heuristics may be
rational at the individual level (Goldstein and Gigerenzer, 2002), stra-
tegically rational in turbulent business environments (Bingham and
Eisenhardt, 2011), and computationally rational in articial intelligence
(Gershman et al., 2015). As such, heuristics need to ‘t to their envi-
ronment, or in other words, be ‘ecological rational (Artinger et al.,
2015; Gigerenzer et al., 1999).
This special issue links the discourse of heuristics with topics and
phenomena relevant to Technological Forecasting and Social Change
(TFSC). Much of the existing research on heuristics considers current
technologies and societal issues. Within these bounds, discussions in the
literature focus on the mechanisms related to individual, team and
organizational level heuristics (see for an overview, Loock and Hinnen,
2015). However, an important element of technological forecasting and
social change lies in its future character, and its developments in the
recent and more distant future. Thus, in the light of technological
change, we expect to see emerging technologies (e.g., such as AI, robotic
surgery or drone warfare) to alter our current realities (e.g., Bailey et al.,
2022; Rauch and Ansari, 2022). Yet, from the existing body of research,
it is less clear how such technological change and tomorrow's emerging
technologies will affect the development and use of heuristics. Also, in
light to societal change, we expect novel behaviors (e.g., the increasing
role of environmental integrity) to affect the development of heuristics.
As we know from the past, technological and societal change often go
hand-in-hand. For example, consider the introduction of the telephone
or the internet, how such technologies have radically altered our
behavior, including of communication patterns. As such, we expect
interactive effects stemming from both forces inuencing heuristics.
Technological and societal change comes with multiple dynamics and
changes on individual, team, organizational and market levels. While
some degree of change has long been acknowledged to be consequential
for organizations, the magnitude of dynamics in the light of techno-
logical and social change requires novel heuristics as strategies to
accommodate these dynamics, while still the necessity to provide suf-
cient guidance for organizational stakeholders (Fig. 1).
2. Papers of this special issue
This special issue is clearly addressing a timely phenomenon, for
which we have been able to accept ve manuscripts for publication in
this special issue. We now provide a brief overview of papers by
alphabetical order, illustrating key ndings and the insights in rela-
tionship to the main focus of this special issue. Table 1 depicts an
overview of the contribution to the special issue.
The rst article by Ghezzi on ‘How entrepreneurs make sense of Lean
StartUp Approaches: Business Models as cognitive lenses to generate fast
and frugal heuristics, addressing the question of how entrepreneurs
cognitively approach the application of Lean StartUp approaches to
Contents lists available at ScienceDirect
Technological Forecasting & Social Change
journal homepage:
Received 3 October 2022; Received in revised form 4 November 2022; Accepted 5 November 2022
Technological Forecasting & Social Change 186 (2023) 122163
develop and validate their entrepreneurial ideas. Based on a multiple
case study of three digital start-up, the author illustrates how digital
entrepreneurs rely on business models as a cognitive lens to make sense
and translate abstract guidelines into fast and frugal heuristics.
The second article by Gilbert-Saad, Siedlok and McNaughton titled
‘Entrepreneurial heuristics: Making strategic decisions in highly
uncertain environments adds to this special issue an understanding of
the potential usefulness of heuristics, especially in highly uncertain
entrepreneurial contexts and situations in which individuals cannot rely
on previous experience. Against this backdrop, the author team con-
ducted 27 interviews with (inexperienced) founders of ventures. The
study shows how founders use a different set of heuristics, and introduce
the notion of metacognitive heuristics, i.e., rules that specify cognitive
approaches to options and actions, which are presentative of the
entrepreneurial mindset.
In their paper ‘A research agenda for studying project decision-
behavior through the lenses of simple heuristics, Stingl and Geraldi
propose a conceptual framework for the study of project decision
behavior. By doing so, the authors draw on a set of empirical problems in
project decisions to illustrate the challenges in such a project decision-
making context. The authors argue to start research with the problem
(and not theory) to then explore how the theory can explain behaviors
related to the problem.
The paper by Krawinkler, Breitenecker and Maresch focuses on
heuristic decision-making in the green energy context. The authors
suggest a novel approach to address a dilemma faced by managers by
integrating principles of simple rules and data-driven mathematical
optimization to navigate the dynamic and complex context of green
energy. The authors argue to unleash the potential of heuristics may
require those simples rules to be ‘accountable.
The last paper in this special issue titled ‘Heuristics-in-use: towards a
practice theory of organizational heuristics by Wenzel and Stjerne
presents a conceptual paper addressing the research question of how
actors actually use organizational heuristics ‘in practice. In their con-
ceptual paper, the authors provide a reconceptualization of organiza-
tional heuristics regarding its enactment of rules-of-thumb in response
to situations at hand. The authors introduce the concept of ‘heuristics-in-
Fig. 1. Heuristics in technology forecasting and social change.
Table 1
Contribution to special issue.
Article Research question and context Key ndings Heuristic insight
Ghezzi: How Entrepreneurs make sense of
Lean Startup Approaches: Business
Models as cognitive lenses to generate
fast and frugal Heuristics
RQ: How entrepreneurs cognitively
approach the application of LSAs to develop
and validiate their entrepreneuiral ideas?
Context: Multilpe case study of three digital
To overcome (a) triviliaty trap and (b) obscurity
trap, entrepreneurs resort to use the business
model as a cognitive lens to make sense of
guidelines, which is enacted by two cycles: (1) a
rst sense-making cycle, which helps
entrepreneurs understanding of abstract
guidelines and related rst-order heuristics, and
(2) second specication cycle, which further
translates rst-order heuristcs or simple rules
into second-order ones.
Digital enterpreneurs rely on
business models as cognitive lenses
to make sense and translate abstract
guidelines into fast and frustal
Gilbert-Saad et al.: Entrepreneurial
heuristics: making strategic decisions
in highly uncertain environments
RQ: Understanding of the potential
usefulness of heuristics, especially in highly
uncertain entrepreneurial contexts and in
situations when individuals cannot rely on
prior experience?
Context: 27 semi-structured intervies with
(inexperienced) founders of ventures
Founders use (1) Organizational heuristics, (2)
projective heuristics and (3) empathic
Introduce notion of metacognitive heuristics, i.
e., rules that specify cognitive approaches to
options and actions.
Founders use metacognitive
heuristics, whose contents are
presentative of the entrepreneurial
Stingl and Geraldi: A research agenda for
studying project decision-behavior
through the lenses of simple heuristics
RQ: How can we research decision-making
behavior of individuals in projects through
the lens of simple heuristics?
Context: Conceptual article drawing on
empirical problems in project decisions
Challenges of project decision-making context:
Static vs. dynamic view on simple heuristics,
and problem framing, information use and
identication of options.
Framework of research agenda; for
the study of project decision
Insight: Start research with the
problem (not the theory), and then
explore how the theory can explain
behaviors related to the problem.
Wenzel and Stjerne: Heuristics-in-use:
Towards a practice theory of
organizational heuristics
RQ: How actors actually use organizational
heuristics in practice?
Context: Conceptual article drawing on
practice theory
Reconceptualization of organizational
heuristics regarding enactment of rules-of-
thumb in response to situation at hand.
Introduction of heuristics-in-use, i.e.,
situationally enacted rules-of-thumb that actors
produce and recreate in and through practical
Heuristics as a practice-based
Insight: Heuristics are in constant
ux and come into being through
their (re)-production ‘in practice
Krawinkler, et al.: Simple rules in
heuristic decision-making processes
supported by data-driven mathematical
optimization applied on a real-world
green energy scenario
RQ: How to managers unleash the potential
of simple rules?
Context: Green Energy Scenario using
simulation technique
Managers face dilemmas when developing
simples rules. To address this dilemma, the
authors suggest a novel approach to combine
the principle of simple rules and data-driven
mathematical optimization
Heuristics are required to be
‘accountableto unleash their
M. Rauch et al.
Technological Forecasting & Social Change 186 (2023) 122163
use, i.e., situationally enacted rules-of-thumb that actors produce and
recreate in and through practical knowledge. By drawing on practice
theory, the authors illustrate heuristics as a in constant ux and come
into being through their (re)-production of ‘in practice.
3. Opportunities for future research on heuristics
3.1. Addressing specics of technological forecasting and social change
Despite the vast knowledge and research on heuristics, the TFSC
research landscape stretches beyond the situation and context in which
heuristics have been studied so far (e.g., Phillips, 2019; Phillips and
Linstone, 2016). Current and future challenges point to novel ways of
doing business for which it remains less clear on how, and to which
extent heuristics can contribute. Due to the increased and novel types of
uncertainty, more research is needed on the efcacy of heuristics. More
broadly, given the uncertainties and many ‘unknowns, we may require
a new theory of heuristics in its own right. Subsequently, we will present
an array of potential research question but not limited to the specied
topics and questions, which we deem to be of relevance in light of future
Much of the work on heuristics outlines the relationship between
heuristics and their environment (e.g., Bingham and Eisenhardt, 2011;
Ehrig and Schmidt, 2019). Environments affected and pertaining to
technological forecasting and societal change are however often envi-
ronments that are not stable. As such, current and future research may
necessitate a shift away from focusing on stable environments and focus
even more on the dynamic, changing and sometimes unpredicateble
manner in which changes occurs. Such environments are either chang-
ing and/or are uncertain (e.g., in energy markets in which diffusion of
large shares of renewable energy in many countries, change salient
foundations of energy markets). For example, climate change is one of
such environments, for which it is not clear how the natural environ-
ments will evolve, and its consequences and multitudes. As such, there is
a evident need to understand how heuristics work in the light of change
and uncertainty.
We encourage scholars to utilize the insights presented in this special
issue to work on how heuristics are able to explain and solve some of the
future problems facing mankind associated to technological forecasting
and social change. For example, to study if and how heuristics in social
change and technology change through social aggregation and how they
relate or differ to other organizational features (e.g., norms, rules and
learning). While organizational routinization can bring rules and arti-
facts into being, and thus empower individual heuristics, it is of interest
to understand how team and organizational heuristics affect individual
How do heuristics relate to organizational strategy? It is well known
that some heuristics can be strategic rational (Bingham and Eisen-
hardt, 2011) and thus heuristics might be seen as strategy. Yet, we need
to uncover more about the process by which some heuristics become
strategic rationalwhile other remain lessstrategic. Hence studying
the strategic relevance of heuristics and potential change of that rele-
vance along technological and social change is an important eld of
future research. On a related note, the processes in which heuristics nd
their way into organizations is of interest as well. Studies of capability
creation point to a process from the individual to the collective (Bing-
ham et al., 2019). As such, we need to further explore potential alter-
natives and specics in light of social and technological change.
What are outcomes and what impact do heuristics have? Heuristics
are associated with organizational processes, including rm's path
development (Sydow et al., 2009). Some heuristics are used to repro-
duce the current business with its operational processes. Other heuristics
are deeply related to strategic features of the company or the strategy
development process for creating the future business. Transforming the
current business into the future requires change initiatives and related
knowledge as to how managing change, which is based on change
heuristics. In that regard, it is of interest how existing heuristics may
help to redene purpose of and within organizations and change over
3.2. Questions regarding to structure, content and governance of
How are organizational heuristics structured and how does the
structure change throughout social and technological change? Heuris-
tics can be differentiated according to their embedded background
knowledge into process heuristics and content heuristics. Some heuris-
tics may appear to be applied ‘mindlesslyas they merely dene the
process for getting solutions. Other heuristics also transmit knowledge
for problem solving or also explanations of the purpose of the heuristic's
application. Technological forecasting and social change are based on
portfolio of heuristics drawing on signicant background knowledge as
strategic decisions require expertise in order to dene a rm's future (i.e.
providing the rationale for business transformation). How do the
different types of heuristics (e.g., process, content, explanatory) vary
over time and impact different processes and organizational outcomes?
For instance, how can demand curves, technological developments,
or learning curves be better predicted based on heuristics? How are
heuristics effective within particular dynamics of social change (con-
trasted to the empirical and mostly non-social environments in the
current debate on heuristics)? In sustainability transitions such as the
energy transition novel challenges emerge. In such a changing business
context, it is of interest to understand which heuristics endure over time
and how heuristics are able to accommodate the requests from social
change and the economy (Smith, 2007)? What heuristics are required to
solve problems related to the energy transition and other grand chal-
lenges of our time? What heuristics enable effective solutions to the
climate change? What heuristics (e.g., grow the business by emitting
) hinder effective solution? How do individuals, groups, organiza-
tions and societies choose and develop heuristics over time? How do
heuristics relate to some of the established constructs in transition
research? How do business models as congurations of heuristics (Loock
and Hacklin, 2015) impact technological development and social
change, and vice versa, how do social change and technological trends
impact business models and other heuristics? How is the emergence and
change of heuristics impacted by events or changes in the technical,
social and business environment?
Beyond these important questions, we also encourage advancements
of methodology to advance the study of heuristics. While experimental
studies are prevalent in many studies, especially drawing on a psy-
chology tradition (e.g., Gigerenzer et al., 2011), they often run the
criticism of stylized problems set and the difculty to transfer insights to
more complex settings, such as the strategy context. As a consequence,
strategy scholars often draw on qualitative approaches, such as multiple
case studies (e.g., Bingham and Eisenhardt, 2011). Yet, also case studies
have limitations and potential methodical issues when studying heu-
ristics (Vuori and Vuori, 2014). For example, how to code for heuristics
given they build on the assumption that heuristics need for articulation
in interviews? One potential way of capturing less observable heuristics
might be the study of solicited or unsolicited diaries (Amabile and
Kramer, 2011; Rauch and Ansari, 2022). ‘Personal diaries are intimate
journals in which individuals record their lived experiences and their
personal reections and opinions(Rauch and Ansari, 2021: 9). As such,
diaries allow for a deeper engagement with the thought process of in-
dividuals with the potential to also bridge different level of analysis and
allow for a processual understanding of heuristics. Also the usage of
mixed methods seems to be a promising avenue for the future of heu-
ristics, such as the combination of (eld) experiments with qualitative
M. Rauch et al.
Technological Forecasting & Social Change 186 (2023) 122163
4. Conclusion
The purpose of this Special Issue was to offer a place for discourse
and further exploration on how heuristics are shaped by technological
forecasting and societal change, and vice versa. The ve papers within
this Special Issue put forth important contributions to the evolving
research agenda. This introduction builds on and outlines further op-
portunity for work in this important eld. We hope to see more articles
in the future building on what we know about heuristics and extending it
towards what we need to know about heuristics to advance and support
the particular challenges in social and technological change. To do so,
we need to build on an ambitious research agenda forward bridging
across disciplines and methodological divides to open up to new
Data availability
No data was used for the research described in the article.
We would like to thank participants at the heuristics-track at EGOS in
2018 in Tallinn and 2019 in Edinburgh for highly valuable discussions
and input. We also thank Vincent Mangematin and the editorial team at
Technological Forecasting and Social Change for the editorial support
throughout the review process.
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Madeleine Rauch
, Moritz Loock
, Wolfgang H. Güttel
Copenhagen Business School, Kilevej 14, 2000 Frederiksberg, Denmark
University of St. Gallen, Mueller-Friedberg.Strasse 6/8, 9000 St. Gallen,
TU Wien (Vienna University of Technology), Getreidemarkt 9, 1060
Vienna, Austria
Corresponding author.
E-mail addresses: (M. Rauch), (M.
Loock), (W.H. Güttel).
M. Rauch et al.
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
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The heuristics strategists use to make predictions about key decision variables are often learned from only a small sample of observations, which leads to a risk of inappropriate generalization when strategists misjudge regularities. Building on the statistical learning literature we show how strategists can mitigate this risk. Strategies to learn heuristics that accept a bias, that is, a systematic deviation of predictions from actual outcomes can outperform unbiased strategies because they can reduce the variance component of prediction error: The degree to which random fluctuations in observational data are inappropriately generalized. We demonstrate how strategists who are aware of the trade-off between bias and variance can learn heuristics more effectively if they are also aware of the relevant characteristics of their learning environment. We discuss the implications of our results for our understanding of heuristics, (dynamic) capabilities and managerial cognitive capabilities, and we outline opportunities for empirical work.
Technologies are changing at a rapid pace and in unpredictable ways. The scale of their impact is also far-reaching. Technologies such as artificial intelligence, data analytics, robotics, digital platforms, social media, blockchain, and 3-D printing affect many parts of the organization simultaneously, enabling new interdependencies within and between units and with actors that many organizations have typically considered to be outside their boundaries. Consequently, today’s emerging technologies have the potential to fundamentally shape all aspects of organizing. This article introduces the special issue “Emerging Technologies and Organizing.” We treat these new technologies as “emerging” because their uses and effects are still varied and have yet to stabilize around a recognizable set of patterns and because the technologies themselves are, by design, always changing and adapting. To theorize the relationship between emerging technologies and organizing, we draw on relational thinking in philosophy and sociology to develop a relational perspective on emerging technologies. Our goal in doing so is to create a new way for organizational scholars to incorporate the ever-increasing role of technology in their theorizing of key organizational processes and phenomena. By developing a relational perspective that treats emerging technologies not as stable entities, but as a set of evolving relations, we provide a novel way for organizational scholars to account for the role of technology in their topics of interest. We sketch the outlines of this relational perspective on emerging technologies and discuss the implications it has for what organizational scholars study and how we study it.
Heuristics are fast, frugal, and accurate strategies that enable rather than limit decision making under uncertainty. Uncertainty, as opposed to calculable risk, is characteristic of most organizational contexts. We review existing research and offer a descriptive and prescriptive theoretical framework to integrate the current patchwork of heuristics scattered across various areas of organizational studies. Research on the adaptive toolbox is descriptive, identifying the repertoire of heuristics on which individuals, teams, and organizations rely. Research on ecological rationality is prescriptive, specifying the conditions under which a given heuristic performs well, that is, when it is smart. Our review finds a relatively small but rapidly developing field. We identify promising future research directions, including research on how culture shapes the use of heuristics and how heuristics shape organizational culture. We also outline an educational program for managers and leaders that follows the general approach of “Don't avoid heuristics—learn how to use them.”
On Technological Forecasting & Social Change's 50th birthday, the journal's second and current Editor-in-Chief remarks on TF&SC's progress, the changes in the technological, cultural, and geopolitical environments in which the journal operates, TF&SC articles' changing topics and origins, and where future TF&SC volumes may lead.
Research Summary While much research suggests that capabilities are critical for firms, little is known about the individual‐level origins (“microfoundations”) of capabilities. Using in‐depth nested case studies, we explore how firms develop an internationalization capability. The setting is six entrepreneurial firms from three culturally distinct countries. Our data show that executives begin by seeding the process with imperfect heuristics and then managers continue development by elaborating their understanding of what task to perform and how to perform it. Importantly, managers across hierarchical levels support the development of their firm’s internationalization capability by abstracting key heuristics away from any one experience such that the capabilities become less routine over time. Overall, we contribute to the microfoundations movement in strategy and to literature on organizational learning. Managerial Summary Firm capabilities are not just important to strategy, but often are the strategy of firms, especially in dynamic markets. Popular examples include Cisco’s acquisition capability, Hewlett‐ Packard’s alliance capability Starbuck’s internationalization capability and Apple’s product development capability. Unfortunately, it is often unclear to executives how to build a firm capability. We explore how entrepreneurial firms develop their own internationalization capability over time. Our data show that these capabilities develop through a process of seeding, elaborating, and abstracting key heuristics for internationalization. Importantly, we show that this process is shaped by extensive communication within and across multiple hierarchical levels. In this way, heuristics move from individual‐level “rules of thumb” for action to firm‐level understandings for fueling growth and creating competitive advantage.
Recently, academics have shown interest and enthusiasm in the development and implementation of stochastic customer base analysis models, such as the Pareto/NBD model and the BG/NBD model. Using the information these models provide, customer managers should be able to (1) distinguish active customers from inactive customers, (2) generate transaction forecasts for individual customers and determine future best customers, and (3) predict the purchase volume of the entire customer base. However, there is also a growing frustration among academics insofar as these models have not found their way into wide managerial application. To present arguments in favor of or against the use of these models in practice, the authors compare the quality of these models when applied to managerial decision making with the simple heuristics that firms typically use. The authors find that the simple heuristics perform at least as well as the stochastic models with regard to all managerially relevant areas, except for predictions regarding future purchases at the overall customer base level. The authors conclude that in their current state, stochastic customer base analysis models should be implemented in managerial practice with much care. Furthermore, they identify areas for improvement to make these models managerially more useful.
To enable a better understanding of the underlying logic of path dependence, we set forth a theoretical framework explaining how organizations become path dependent. At its core are the dynamics of self-reinforcing mechanisms, which are likely to lead an organization into a lock-in. By drawing on studies of technological paths, we conceptualize the emergent process of path dependence along three distinct stages. We also use the model to explore breakouts from organizational path dependence and discuss implications for managing and researching organizational paths.
There is no theory in strategic management and other related fields for identifying decision problems that cannot be solved by organizations using rational analytical technologies of the type typically taught in MBA programs. Furthermore, some and perhaps many scholars in strategic management believe that the alternative of heuristics or “rules of thumb” is little more than crude guesses for decision making when compared to rational analytical technologies. This is reflected in a paucity of research in strategic management on heuristics. I propose a theory of “organizational intractability” based roughly on the metaphor provided by “computational intractability” in computer science. I demonstrate organizational intractability for a common model of the joint strategic planning and resource allocation decision problem. This raises the possibility that heuristics are necessary for deciding many important decisions that are intractable for organizations. This possibility parallels the extensive use of heuristics in artificial intelligence for computationally intractable problems, where heuristics are often the most powerful approach possible. Some important managerial heuristics are documented from both the finance and strategic management literatures. Based on all of this, I discuss some directions for theory of and research on organizational intractability and heuristics in strategic management.