Corporate Foresight and its Impact on Firm Performance:
A Longitudinal Analysis
Paper accepted for publication in
Technological Forecasting and Social Change
Aarhus University, School of Business and Social Sciences
Department of Management, Bartholins Allé 10, 8000 Aarhus C, Denmark
Department for Business Development & Technology, Birk Centerpark 15, 7400
Email: firstname.lastname@example.org, Tel.: +45 871 64929
MENES ETINGUE KUM
University of Münster
Schlossplatz 2, 48149 Münster, Germany
Corporate foresight is applied with the expectation that it will help firms to break away
from path dependency, help decision makers to define superior courses of action, and ultimately
enable superior firm performance. To empirically test this assumption, we developed a model
that judges a firm’s future preparedness (FP) by assessing the need for corporate foresight (CF)
and comparing it to the maturity of its CF practices. We apply a longitudinal research design in
which we measure future preparedness in 2008 and its impact on firm performance in 2015.
The results indicated future preparedness to be a powerful predictor for becoming an
outperformer in the industry, for attaining superior profitability, and for gaining superior market
capitalization growth. In the article, we also calculate the average bonus/discount that can be
expected by sufficiently/insufficiently future-prepared firms.
Keywords: corporate foresight, future preparedness, firm performance, behavioural theory of
• We developed a model for assessing the future preparedness of a firm.
• We assessed future preparedness by comparing the need for and the maturity of
corporate foresight practices.
• We found that future-prepared firms (vigilant) outperformed the average by a 33%
higher profitability and by a 200% higher market capitalization growth.
• Conversely, firms that had deficiencies in their future preparedness were faced with
a performance discount of 37% to 108%.
The research and practice of strategic foresight (to which we refer as corporate foresight)
has a tradition that reaches back to the late 1940s (Coates, et al., 2010). Such practice in
organizations had already seen a golden age in the 1950s, driven in particular by the “La
Prospective” School of Gaston Berger in France and the works of Herman Kahn of the Rand
Corporation in the US (Rohrbeck, et al., 2015). Since then, many firms have invested in building
corporate foresight (CF) units (Battistella, 2014; Becker, 2002; Daheim & Uerz, 2008),
including Cisco (Boe-Lillegraven & Monterde, 2015), Daimler (Ruff, 2015), Deutsche Bank
(Rollwagen, et al., 2008), Deutsche Telekom (Rohrbeck, et al., 2007), France Telecom
(Lesourne & Stoffaes, 1996), L’Oreal (Lesourne & Stoffaes, 1996), Pepsi (Farrington, et al.,
2012), Siemens (Schwair, 2001), and SNCF (Lesourne & Stoffaes, 1996). The expectation is
that CF will enable these firms to spot trends ahead of competitors, gain deeper insight into how
such trends will affect their organization and identify the most effective response, and
ultimately gain a competitive advantage (Hamel & Prahalad, 1994; Hines & Gold, 2015).
Despite the long tradition of applying CF practices, evidence on their impact on firm
performance is scarce. The case study research has provided us with some insights into the
causal links between corporate foresight practices and firm performance, and anecdotal
evidence has been presented to determine its impact (Rohrbeck, 2012; Ruff, 2006; Ruff, 2015).
The main reason for the scarcity of conclusive evidence on the impact of CF is the difficulties
associated with measuring it. For example, establishing a causal link over time, whereby the
impact can often be expected to play out over several years, is confounded by many other
factors. Industry rivals may eventually find ways to offset the advantages that are gained
through CF, macroeconomic factors may shift again, reducing the impact of CF-triggered
actions, and the rules-of-the-game in the industry might change with the entry of new rivals
(Helfat, et al., 2007).
With this paper, using a longitudinal research design, we investigate the impact of CF on
firm performance. Using data on CF maturity from 2008 and firm performance data from 2015,
we are able to investigate the impact with a time-lag, which can be judged as sufficient for the
impact of CF to play out. In addition, we propose a new construct, which we call future
preparedness and which is built by comparing the CF need with CF maturity.
Our paper is structured into five main sections. In section 2, we conceptualize future
preparedness and introduce the main constructs of our measurement model, CF need, CF
maturity and firm performance. In section 3, we describe our research design. In section 4 we
report our findings. In section 5, we discuss the limitations of our research and suggest future
research trajectories. Finally, section 6 summarizes our contributions.
2 Conceptualizing future preparedness
2.1 Corporate foresight
The interest in CF has always been fuelled by the expectation that CF practices, processes,
and organizational units will boost the ability of a firm to attain superior performance
(Vecchiato, 2015). The early work of Gaston Berger in the 1950s emphasized the need to create
future perspectives that are shared in a management team (Berger, 1964). These representations
can clarify the ultimate aims for which an organization strives and facilitate backward planning
to inform the choice of means (Berger, et al., 2008; Coates, et al., 2010). Hamel and Prahalad
(1994) argue that high profitability is only available for firms that can overcome crises by
“competing for the future”, which they contrasted against firms that compete by restructuring
and downsizing. Rohrbeck (2012) studied 19 cases and concluded that CF serves as an
important translational process that leads to the appropriation of new strategic resources, which
then leads to an enhanced competitive position. Using a cross-sectional sample of 77 firms,
Rohrbeck and Schwarz (2013) reported value creation from acting earlier than competitors and
influencing other actors to act in a way that is favourable to the focal firm. Finally, Gavetti and
Menon (2016) and Peter and Jarratt (2015) drew on behavioural strategy and single-case studies
to propose that CF is a set of practices that enables strategists to identify a superior course of
action and foresee its consequences.
For this paper, we define CF as a set of practices that enable firms to attain a superior
position in future markets. However, we also acknowledge that more CF may not always be
better. Day and Schoemaker (2005) argued for a state that they call ‘neurotic’, which occurs
when a firm that has peripheral vision capabilities that exceed its needs. Burt et al. argue that
foresight may trigger a condition in top management teams that they call ‘managerial
hyperopia’, i.e., being too focused on managing distant futures, while failing to attach sufficient
attention to what is close at hand. Hence, our approach will have to move beyond measuring
absolute levels of CF and put them in context with the CF need. We expect that firms can make
use of CF to identify the factors that drive environmental change, foresee future market changes,
and define a course of action that leads towards a superior market position—and subsequently
to superior firm performance.
2.2 Conceptualizing future preparedness
Compared to the previous studies, we advance the conceptualization by introducing the
relative construct future preparedness (FP). This construct is built by comparing the need for
CF with the maturity of the CF of the focal firm. The underlying rationale is that if we want to
determine if, for example, better reflexes increase the likelihood of winning a sports
competition, it will matter if my competitive environment is a game of chess or a game of table
tennis. This importance of aligning the maturity to an environment-induced need has also been
recognized in Day and Schoemaker’s (2005) peripheral vision model. For our conceptual
model, we build a CF need index on the basis of Day and Schoemaker’s (2005) environmental
complexity and environmental volatility scales. The maturity index is based on Rohrbeck’s
maturity model (Rohrbeck, 2010a; Rohrbeck, 2010b). Both indices are converted into a four-
level score, which allows for a direct comparison of both. Therefore our model does not assess
the absolute level of reflexes (the analogy being CF maturity in our model), but the level of
appropriateness of the reflexes for a given competitive environment (FP in our model).
We expect that FP would as a consequence also be a more powerful indicator for judging
a firm’s attractiveness for investors than CF maturity alone. Similarly, an assessment that
indicates a lack of FP would be a strong signal for top management that mid- and long-term
competitiveness is threatened (Hamel & Prahalad, 1994; Tushman & Oreilly, 1996). This view
is also reflected in the German law that governs publicly traded organizations, as it formulates
the firm requirement for such organizations to have a strategic foresight system. However, with
the lack of a transparent indicator, the requirement is difficult to enforce. If FP hence proves to
be measurable across industries,
• for shareholders, it could become a powerful indicator to hold management
accountable to focus sufficiently on the mid- and long-term to ensure a firm’s future
• for policy makers, it could become a formal requirement that ensures that firms
have systems in place that raise the probability of survival and that management
pays sufficient attention to mid-term value creation as opposed to short-term gains;
• for management, it could become a benchmarking tool to ensure that (a) they
develop adequate future preparedness in their organization and (b) that their
corporate foresight systems are competitive when compared with their industry
In the following section we will discuss the literature on which we draw to build our
measurement model. The detailed operationalization of our constructs can be found in Table 3
in the appendix.
2.3 Measuring corporate foresight maturity
Different models have been proposed to measure the foresightedness of a person or
organization. Grim (2009) proposed a model that combines process and leadership elements.
Day and Schoemaker (2005) introduced such a model under the term peripheral vision
capabilities, which includes the categories of leadership orientation, knowledge management
systems, strategy making, organizational configuration, and culture. Hines and colleagues
(Hines, et al., 2017) developed a competency model that can be applied to judge the proficiency
of individuals in performing a futurist role.
For our study, we chose Rohrbeck’s maturity model for three reasons. First, this model
measures CF maturity on the organizational level. Second, it specifies practices that can be
measured both through the descriptive four-level scale of the original model and as a Likert
scale (Jissink, et al., 2015; Paliokaitė & Pačėsa, 2015). Third, the maturity model has already
been used to investigate the relationship between CF and firm performance (Jissink, et al., 2014;
Rohrbeck, 2012; Rohrbeck & Schwarz, 2013). From the original model, we decomposed the
dimension ‘people and networks’ into its two subcomponents. We further added a process layer
(see Figure 1), which facilitates the understanding of how the different practices of the maturity
models contribute to a firm’s ability to transform signals into insights, which inform new
courses of action.
Figure 1: Our CF maturity assessment model, based on (Rohrbeck, 2010a)
In the process layer, we define three process steps:
• Perceiving: Practices that firm use to identify the factors that drive environmental
change. Firm aim to identify (weak) signals ahead of competition to gain a lead-time
advantage (Ansoff, 1980; van der Duin & Hartigh, 2009).
• Prospecting: Practices through which firms engage in sensemaking and strategizing.
Practices include working with analogies, scenario analysis, systems-dynamics
mapping, and back casting (Bezold, 2010; Daft & Weick, 1984; Rhisiart, et al., 2015).
In addition, firms aim to foresee the right time to act by identifying tipping points. The
aim of this phase is to gain an insight advantage, which permits firms to identify a
superior course of action that is distant from the status quo of the industry (Gavetti,
2012; Gavetti & Menon, 2016).
• Probing: Practices through which firms move from what Gavetti and Levinthal called
‘cognitive search’ in the perceiving and prospecting phase to ‘experimental search’ in
the probing phase (Cunha, et al., 2012; Gavetti & Levinthal, 2000; Gavetti & Rivkin,
2007). In particular, in high-speed environments, the need to explore new markets
through experimentation has been acknowledged (Costanzo, 2004; Gavetti & Rivkin,
2007). Probing practices occur either in dedicated accelerator units or in units that
receive the mandate to act. This may include prototyping, R&D projects, consumer
tests, internal venturing, strategic initiatives or external venturing (McGrath, et al.,
2006; Michl, et al., 2012; Rohrbeck, et al., 2009). Probing practices aim at legitimizing
and starting a new course of action and ultimately at gaining a competitive advantage.
PRA CT IC ES
To measure CF maturity, we build on the existing items of Rohrbeck’s maturity model but
regroup them under the three Ps, see Figure 2. We create the perceiving scale by integrating
items that pertain to information, people, and network (14 items). We create the prospecting
scale by integrating items that pertain to methods and culture (9 items). We create the probing
scale by using the items that pertain to an organization (12 items). The detailed items are
provided in Table 3 of Appendix A.
To assess the overall CF maturity level, we first calculate the average of the items in the
three Ps (aP1, aP2, aP3). The items were scored by the respondents on a 5-point Likert scale. After
calculating the averages for the three Ps, we transform them into our four-step maturity levels
(mlP1, mlP2, mlP3) by applying the following rule:
• (a<2) = (ml=1)
• (3>a>=2) = (ml=2)
• (4>a>=3) = (ml=3)
• (5>=a>=4) = ml=4)
This approach provides us with a measure that is comparable across the three Ps and is
comparable to the four-level scale that we use for the CF need assessment. To aggregate the
maturity-level scores across the three Ps, we apply a minimum function: MIN (mlP1; mlP2; mlP3).
This is in line with other maturity models, which are built on the assumption that the ‘weakest
link determines the strength of the chain’, i.e., in our case the CF maturity of a firm is
determined by the P with the lowest score.
Figure 2: Measuring corporate foresight maturity
3 Ps Firm’s CF
2.4 Measuring corporate foresight need
To take into account that firms in, for example, a very stable environment would have a
lower need for building CF practices, we built FP as a relative measure. To operationalize CF
need, we adapted and reduced Day and Schoemaker’s (2005) scales for the complexity and
volatility of the environment. The transformation from the original Likert-scale scores into need
levels (nlC, nlV) was performed analogous to the approach used for the maturity levels (see
Figure 3: Measuring corporate foresight need
To aggregate the need level scores, we apply a maximum function: MAX (nlC, nlV). This
is to reflect that both complexity and volatility can drive the need independently. This is in line
with most authors and is also supported by our view of how a firm would think about
preparation (Gephart, et al., 2010; Vecchiato, 2012). If, for example, a firm is situated in an
environment that is characterized by a level one complexity but only a level four volatility, we
posit that it would still need to build CF practices on level four. The reasoning is that the high
complexity drives inertia, which in turn can only be counterbalanced by identifying change
early and having CF mechanisms in place that trigger organizational response.
One example could be a car manufacturer facing the disruption of electrically powered
vehicles. While the environmental complexity is low (low number of competitors, competitors
easily identifiable) the environmental volatility is high (extent to which is it affected by external
change, forecastability of technological change). For such a firm, if we then assessed it to be at
Complexity of the
Volatility of the
a level four (volatility) and a level one (complexity), we would obtain a level two for CF need
if we took an average of the two dimensions (volatility and complexity). This case illustrates
that the appropriate level of CF process must match the highest need level in either of our two
2.5 Constructing future preparedness
To determine systematic future preparedness, we propose that a firm has reached the
optimum level of future preparedness if its CF need level (NLCF) is matched by its CF maturity
level (MLCF). A deviation from this optimum can occur when firms have a maturity level below
the CF need level or when firms have a maturity level above the CF need level. We define the
• nl = ml: Vigilant, a firm has CF practices that are adequate for its given environment.
• nl < ml: Neurotic, a firm has CF practices that exceed its needs for a given environment.
• nl > ml (by one level): Vulnerable, a firm that has CF practices that fall one level short
of what would be required to match the need.
• nl >> ml (by two or more levels): In danger, a firm that has CF practices that fall more
than one level short of what would be required to match the need.
By introducing FP as a relative construct, we believe we have found a way to control for
industry differences that may have confounded earlier findings on the impact of CF.
3 Research approach
3.1 Challenges and research design
We argued above that the lack of empirical research on the impact of CF is to a large extent
routed in the difficulties in designing an appropriate empirical-investigation frame. To be more
specific, the two main challenges that we aimed to overcome with our research-design are as
The past research has found case-based and anecdotal evidence suggesting a link of CF
activities (sometimes individual projects) to local outcomes (for example, the repositioning of
a product portfolio, which leads to higher sales) (Battistella, 2014; Rohrbeck, 2012; Ruff, 2015).
However, it has also noted the challenge of complex causal links that may confound the
relationship between CF and firm performance. In particular, competitor actions and industry-
level factors have been reported to play an important role in determining firm performance. In
our study, we addressed this in two ways: First, we controlled for potentially confounding
environmental factors by integrating CF maturity and need into our FP construct to form our
independent variable. Second, we used ‘outperformer’ and ‘underperformer’ clusters, which
are populated on the industry level, to avoid inter-industry-performance differences that might
confound our findings. We built performance clusters by identifying the ‘outperformers’ (top
20%) and ‘underperformers’ (bottom 20%) of each industry and then combining the firms in
cross-industry performance clusters, i.e., creating a sample of all outperformers and
underperformers of all industries. These samples are thus not confounded by strong intra-
industry differences, and a robust classification allows the winners to be differentiated from the
losers across industries.
Different measures are available to measure firm performance. It is widely assumed that
profitability is the main objective of a business firm (Damodaran, 2001). In addition, public-
listed firms may also target the optimization of shareholders’ value, which can be quantified by
market capitalization growth (Damodaran, 2001; Eberhart C., et al., 2004). In our study, we use
both measures independently to test the relationship between FP and firm performance using
• Profitability (EBITDA): operationalized as the earnings before interest, taxes, and
the depreciation adjustment of the firms in 2015.
• Market capitalization growth: operationalized as the market valuation difference
between 2008 and 2015.
The second main challenge is that CF cannot be expected to pay-off in the short term. The first
consequence is that scholars who are willing to study the impact of CF need to adopt a
longitudinal research design (Eberhart C., et al., 2004; Han Chan, et al., 1990; Sheng-Syan, et
al., 2013). In our study, we chose to use a seven-year time-lag relying on future preparedness
data from 2008, which we matched with firm performance data from 2015.
3.2 Sample and data collection
In our research, we focused on CF practices, which were observable on the firm level. The
past research has documented that such firm-level practices can be observed in large
organizations, while small and medium enterprises typically perform foresight on a personal
level or in an ad hoc and noninstitutionalized manner (Becker, 2002; Daheim & Uerz, 2008;
Elenkov, 1997). We hence drew our sample from firms that have annual revenues of above
We focused our research sample on multinational European firms that apply corporate
foresight, which are suitable for our research endeavour. We contacted firms across industries,
including the chemical, financial services, telecom, energy and utilities, healthcare an
pharmaceutical, automotive, manufacturing, retail and consumer business, and transportation
industries. The data were collected in the fall of 2008 during a period of four months. Managers
operating in the areas of innovation, market research, corporate foresight and product
development participated in the study. Potential respondents were contacted by phone to ensure
that the survey was completed by the appropriate manager, who is knowledgeable on corporate
foresight. This process optimized the reliability of our data.
In total, 467 firms were invited to participate in the study, out of which 135 participated.
This represents a response rate of 29%. From the 135 participants, 52 firms provided data that
were either incomplete or inconsistent. These firms were excluded from the sample, which was
reduced to 83 participants.
To collect the future preparedness data, we relied on a questionnaire that measured CF
maturity with 35 items and CF need with 10 items. The questionnaire was created both in
English and German, a measure to boost the response rate from the German firms. The
participants were approached by email, fax, or post. Additionally, an online survey page was
created and distributed more broadly through social networks and through partner
organizations. The participants were re-contacted by phone up to three times. As an incentive,
the participants were offered a tailored benchmarking report.
To collect the firm-performance data, we used the S&P Capital IQ database. As a first step,
for each firm, we collected the profitability and average profitability of the respective industry
in our future preparedness database. Here, we were able to retrieve data from 70 firms. In
addition to profitability we wanted to independently use market capitalization growth as an
additional measure. However, this resulted in a decreased sample size as only 42 of the 70 firms
were publicly listed. For these firms we collected the market capitalization values from 2008
and 2015 and calculated their market capitalization growth.
3.3 Five approaches to study the relationship between future preparedness and firm
To assess the impact of FP on firm performance, we use five different tests. First, we use
our FP clusters, i.e., firms that in 2008 were vigilant, neurotic, vulnerable, or in danger and
observe in which performance groups these firms are represented in 2015, i.e., outperformer,
underperformer. This allows us to avoid confounding effects from inter-industry difference,
which may result in judging a firm to be an outperformer merely because it is in a more
profitable industry than other firms. In our study design, the firms must perform significantly
better than their industry peers to be classified as outperformers. We expect to find that a firm
with a high FP is significantly more likely to be among the outperformers in its industry than
among the underperformers. If this were the case it would be a strong indicator of a positive
relationship between FP and firm performance.
Second, we check if this result is significantly different from the testing the same
relationships in a cross-sectional analysis, i.e., comparing the FP clusters membership in 2008
and performance group membership in 2008. This test helps us to isolate the longitudinal effect
from FP on firm performance from confounding effects. A positive result from our first test
may for example be only due to an inverse causality, e.g., high FP correlates with high
performance, because firms that have high performance have better processes, including CF
and R&D. We expect this test to show that there is no positive relationship between FP and
firm performance, as we did not allow for the time-lag for the impact to play out. It may even
be that we find a negative relationship as the previous studies have shown that firms with high
performance express less of a need to be future prepared (Chen, 2008; Jissink, 2017).
Third, we perform a migration analysis in which we test whether vigilant firms have a
significantly higher likelihood of moving up in the ranking in their industry performance group,
i.e., from underperformer to average, from average to outperformer, or from underperformer to
outperformer in the seven years between 2008 and 2015. We also test the reverse relationship,
i.e., if a firm with FP deficiencies (vulnerable, neurotic, in danger) has a higher likelihood of
moving down in the ranking of performance groups. We expect these two relationships to be
Fourth, we use interviews, public sources and research reports to look for causal evidence
of the impact of FP on firm performance (Harrison & Reilly, 2011; Jick, 1979). For that we
select the firms that we identify in the third test to have followed the predicted pattern, i.e.,
vigilant firms that have moved up in the ranking of performance clusters in their industry and
firms with deficiencies that have moved down. We expect that we will find some evidence that
high FP correlates with strategic moves that the focal firm applied to attain a gain in
performance relative to its industry peers. While this study cannot be conclusive, it adds
qualitative insights, which make our quantitative findings more robust and may even allow us
to uncover some causal relationships (Creswell, 2013; Powell, et al., 2008).
Fifth, we estimate the average bonus or discount of the different FP clusters in the seven-
year time period. We expect that vigilant firms should have both a higher profitability and
higher market capitalization growth. Similarly, we expect that firms with FP deficiencies to
have lower profitability and market capitalization growth when compared with vigilant firms.
Applying these five tests we are able to boost the robustness of our findings. In the tests
that apply descriptive statistics techniques we judge a percentage difference higher than 15%
to be significant, which is in line with other studies that employ descriptive statistics.
4.1 State of future preparedness in 2008
We first reported on the overall level and distribution of future preparedness in our 2008
sample. Our results showed (Figure 4) that 62% of the surveyed firms had a strong to very
strong level of CF need. This result indicated that the majority of firms had the need to apply
more sophisticated CF practices. However, on the maturity side, the majority of firms did not
reach above a level two. Only 2% of the firms had a maturity level of four, whereas 27% of the
firms attained a level two, and 71% of the firms had a moderate to low CF maturity (level one-
two). Thus, our results confirmed the limited implementation of systematic CF practices across
industries (Rohrbeck & Schwarz, 2013). Although we are witnessing the rising adoption of CF
within firms, its application seems to, on average, still lack comprehensiveness, continuity, and
institutionalization (Daheim & Uerz, 2008; Rohrbeck, et al., 2015).
Figure 4: State of future preparedness in our sample in 2008 (n = 83)
The even more relevant question is how CF maturity fits the CF need of respective firms
(Slaughter, 1996). Our results showed that only 36% of the firms were vigilant, applying an
adequate level of CF according to their CF need. The remaining 64% of the firms had
deficiencies that limited their responsiveness to change and their ability to proactively shape
future markets. A total of 16% of the firms were neurotic, applying CF practices that may trigger
managerial hyperopia that would paralyze them in the execution of rewarding business models.
A total of 48% of firms were either vulnerable or in danger and hence insufficiently equipped
for scanning, interpreting, and building new business models.
4.2 Relationship between future preparedness in 2008 and firm performance in 2015
To test whether there is a positive relationship between FP and firm performance, we
determine the representation of the FP clusters created in 2008 in the performance clusters
created in 2015. We find that the share of vigilant firms in the outperformers cluster, with 63%,
and the share of vigilant firms in the underperformers cluster, with 24%, is significant and
supports our expectation of the positive relationship between FP in 2008 and firm performance
in 2015 (see Figure 5). In addition, there are neither neurotic nor in-danger firms among the
outperformers in their industry.
for Corporate Foresight
Figure 5: Profitability outperformers/underperformers and their future preparedness level
To test the robustness of this finding, we also examined the market-capitalization
outperformers vis-à-vis the underperformers and found a similar picture (Figure 6). We find
that the share of vigilant firms in the outperformer cluster was significantly higher than the
share of vigilant firms in the underperformer cluster. In market capitalization, the share of the
in-danger firms in the underperformer cluster was even higher, at 22%.
Figure 6: Market-capitalization outperformers/underperformers and their future preparedness level
4.3 Relationship between future preparedness in 2008 and firm performance in 2008
The longitudinal design that was applied in the first test has already reduced the risk of
receiving results confounded by inverse causation. Still, we could be negatively affected by a
tautological relationship in which we measure the same phenomenon with two constructs and
find correlation. In our case, a firm could lead on practices and performance simply by being
well run, and not, as we suggested, because it is more successful at systematically building
superior positions in future markets. To check for this risk in our research, we examined the
Corporate Foresight Preparedness & Profit
RESULTS – PREPAREDNESS & PROFIT
67% OF PROFIT OUTPERFORMERS ARE VIGILANT AND NONE OF THEM ARE IN DANGER
Neurotic*Vigilant* In Danger*Vulnerable*
Data (n=70) * Future preparedness data from 2008
+profitability cluster data from 2015
Corporate Foresight Preparedness & Market Capitalization Growth
RESULTS – PREPAREDNESS & PROFIT
67% OF PROFIT OUTPERFORMERS ARE VIGILANT AND NONE OF THEM ARE IN DANGER
Data (n=42) * Future preparedness data from 2008
+market capitalization cluster data from 2015
relationship between our two constructs at a single point in time, in our case, 2008 (see Figure
Figure 7: Time-lag evidence of the impact of future preparedness on profitability
The results show a significant relationship in the opposite direction. Explained differently,
we observed that the outperformers were, on average, less well-prepared than the
underperformers. To explain this odd finding, we can draw on the research on R&D investments
and firm performance. For example, Chen (2008) found evidence that firms that face a future
in which the returns are projected to be below aspirations increase their R&D investments,
while firms that are doing well invest less in R&D (Chen, 2008). Translated to our context, we
can speculate that the cause behind our findings is that the firms with good performance did not
find it necessary to systematically prepare for the future, even though they would have
deficiencies in their future preparedness and may face the future risk of being displaced from
their favourable position in the industry by more future-prepared firms.
The result also indicates that the time-lag between FP and firm performance is vital for a
positive relationship. This also strengthens the results from the first test as it shows that there
is no positive cross-sectional correlation between FP and firm performance.
4.4 Migration analysis (third test)
For the migration analysis, we assess the migration of firms from one performance cluster
(outperformer, average, underperformer) to another in the time between 2008 and 2016. We
control again for industry differences by relying on the industry-specific performance
categorization. We first assess the vigilant firms, which we expected to migrate towards higher
performance clusters, i.e., enhancing their performance vis-à-vis industry rivals. Figure 8 shows
a strong upward migration. In our sample, 40% of the vigilant firms improved their profitability
Data (n=41) * Future prepared ness data from 200 8
+profita bility cluster data from 2008
performance cluster position, 55% were able to maintain it and only 5% (1 firm) decreased their
Figure 8: Migration analysis of vigilant firms
Next, we analysed the migration of the firms with FP deficiencies. Figure 9 shows that only
9% of the firms with corporate foresight deficiencies improved their profitability performance
position. A total of 91% of the firms with corporate foresight deficiencies either decreased or
maintained their position. These findings confirm the positive longitudinal relationship between
FP and firm performance.
Figure 9: Migration analysis of firms with deficiencies
Performance cluster of
vigilant firms in 2008
Performance cluster of
vigilant firms in 2016
40% of vigilant firms upgraded
5% of vigilant firms downgraded
55% of vigilant firms stabilized
Performance cluster of
deficient firms in 2008
Performance cluster of
deficient firms in 2016
9% of deficient firms upgraded
24% of deficient firms downgraded
67% of deficient firms stabilized
4.5 Qualitative investigation into the relationship between future preparedness and
firm performance (fourth test)
Building on our third test, we use a qualitative research approach to study the relationship
between CF practices and performance. We expect to find indications that superior future
preparedness allowed the firms to embark on a superior course of action and increased their
competitive advantage. Similarly, we expect that deficiencies in FP will have led firms to miss
opportunities and threats, resulting in an inferior course of action and a loss of competitive
Table 1 reports our findings on the sample of firms that were vigilant and that were
identified in the third test as having enhanced their performance cluster position.
Table 1 Observation of vigilant firms that attained or maintained their position as outperformers between
2008 and 2015
Corporate Foresight practices
Superior course of action,
increased competitive advantage
• Global foresight unit,
scouting and integrating
market insights in strategic
• Venturing unit integrating
start-ups to develop future
• Leading player launching
technology oriented services in
emerging countries (e.g., malaria
detection apps, pharmacy finder,
• Considerable sales and market
share growth contributed by
technology driven services in
• Customer and supplier
workshops to perceive and
prospect organic food and
• Customer insights clinics and
platforms to perceive
• Merger with an organic producer of
chocolate ingredients and
successful market entry in the
sugar-free chocolate business
• Personalization of packages, as key
differentiator towards customer
needs, which led to sales increase
• Customer insights clinics to
perceive future customer taste
• Set-up of joint research and
• Leading in development of sugar
free and organic product-lines
which heavily contributed to
• Optimization of new pet food taste,
balancing pet appetite and smell, as
a key customer requirement
• Central foresight unit,
identifying future customer
preferences in the fragrance
• Launch of new successful
fragrance product lines addressing
emerging customer requirements
around sustainability, organic
ingredients and value chain
• Set-up of a global scouting
team with focus on screening
• Venturing unit, incubating
start-ups to complement value
• Leader in the integration of new
car features such as personalized
infotainment, autonomous drive
and parking, or context based
search boosting sales performance
• Global scouting teams in the
Silicon Valley, Asia and
Berlin to anticipate
preferences of young
• Integration of foresight team
in corporate strategy, product
development, R&D and
• Development of new car models
attractive for the young-driver
segment as well as boosting
• Successful execution of marketing
campaigns on social media, with
appropriate teasers appealing to
key customer needs
• Institutionalized foresight
activities, integrated in
strategy, R&D and marketing
• Collaboration with start-ups,
by steadily integrating
valuable start-up ideas into
the value chain
• First mover in the development of
car-sharing business models
• Leader in the development of
electric and hybrid motors
• Efficiency gains through integrated
start-up’ technologies in the
• Institutionalized foresight
activities, scanning new
technologies and customer
• Integration of foresight
insights within portfolio
• Leader in the digitalization of the
customer experience, as a key
requirement for banking services
• First mover in new markets such as
digital wallet, mobile payment,
online finance planer, boosting
recent sales performance
• Extension of global scouting
hotspots from Silicon Valley
to future markets (e.g., China,
other Asian regions)
• Collaboration with external
market research and customer
observation firms providing
insights into future customer
• Launch of country specific digital
banking solutions, leveraging
insights from customer research
• Successful player in new solutions
addressing customer needs, such as
mobile wallet, payment, location-
based services and analytics,
• Continuous involvement of
global scouting teams in key
• Collaboration with start-ups
to build and integrate a new
• Launch of a leading operating
system addressing customer needs
around usability and collaboration
• Strong revenue contribution of the
new operating system
While the observations that are reported in Table 1 cannot provide conclusions on causal
links, they still provide some insights into how FP is translated into superior courses of action
and an enhanced competitive position. The ten vigilant firms have all built a meaningful
portfolio of corporate foresight practices, which in most cases are also institutionalized through
a unit structure and processual links to processes such as R&D, marketing, and strategic
decision-making. In addition, it is interesting to note that many have set up mechanisms to
integrate start-ups, which suggests the use of venturing for the purpose of research.
Next, we investigate the firms that had future preparedness deficiencies in 2008 and which
either stayed in or dropped into the underperformer category in their industry. Table 2 presents
Table 2 Observation of firms with FP deficiencies that fell towards or stayed in their position as
underperformers between 2008 and 2015
Firm performance observations
• Failed in setting-up
effective sensors for market
• Focus on the core product
line (graphite electrode)
accounting for 40% of the
• Poor attention on future
technologies and trends
• Failed to anticipate that their core
business of graphite electrode
will become commoditized
• Lack of investment in low-cost
• Loss of a large proportion of its
• No systematic corporate
• Ad hoc market research
based on top management
• No major improvement on the
product line, focussing on
traditional retail banking services
• Missing of new opportunities in
banking businesses such as
digital banking, mobile wallet,
analytics, reflected in current
poor sales performance
• No systematic scouting of
market trends or
• Focus on incumbent
telecom services and
• Stagnating revenues in the
incumbent telecom market and
increasing network maintenance
costs, resulting in profitability
• No substantial portfolio extension
with products and services
beyond the core business
• No systematic integration of
gathered insights within
• Execution of general market
• Old and less innovative product
lines, leading to continuous
market share decrease
• No tangible value of corporate
foresight, beyond the
documentation of market
• Short-sighted firm culture,
with a strong management
focus on short-term
• No foresight team
• Poor understanding of market
shifts occurring in their industry,
e.g., the demand for integrated
services beyond their traditional
ATM machine business
• Lack of portfolio extensions
towards new attractive adjacent
markets such as security,
maintenance, analytics service of
ATM machines or new services
on digital payment
• Continuous loss of market share
The investigation into the firms that ended in the underperformer group in 2015 reveals a
lack of CF practices and a lack of capabilities to translate insights into the future into
organizational responses. All of the firms displayed an overt focus on the existing business,
ultimately failing to renew their offerings and their competitive advantage. All of the firms were
unable to alter their course of action or to pursue strategies that were distinct from the status
quo in their industry. The result of these firms ending up in the underperformer groups appears
to be a logical consequence.
4.6 Estimating the bonus/discount of high/low future preparedness (fifth test)
In our final analysis, we investigated the extent to which a firm could expect a performance
bonus from upgrading its future preparedness. This analysis is important to justify investment
in upgrading CF practices. To find a first proxy for the quantitative benefit of future
preparedness, we calculated the average profitability of the different future preparedness
Our findings (Figure 10) show that vigilant firms achieved, on average, 16% profitability,
which surpassed the overall industry average profitability of 12% and made vigilant firms 33%
more profitable than the average. The value of future preparedness became even more obvious
when examining the discounts that the firms with deficiencies needed to assume. The neurotic
and vulnerable firms had 37% lower profitability when compared to the profitability of the
vigilant firms. in the in-danger firms realized a 44% lower profitability.
ALL FIRMS VIGILANT VULNERABLE IN DANGERNEUROT IC
-37% -37% -44%
Figure 10: Average profitability of firms in the future preparedness levels
Next, we calculated the impact of future preparedness on market capitalization growth.
Figure 11 shows that the average growth in our sample over the seven years (from 2008 to
2015) was 25%. The vigilant firms achieved over the same period on average a 75% growth
in market capitalization, or 200% additional growth.
Figure 11: Average market capitalization growth in future preparedness levels
Interestingly, the worst performing group was the neurotic firms, which had, on average, a
negative growth of 6%. An explanation could be that neurotic firms lack the persistence to build
sustainable growth or simply that they fail in the execution of new courses of action.
Our study aimed to find evidence of the longitudinal effect of FP on firm performance. We
drew on a proprietorial dataset of 83 multinational firms that we surveyed in 2008 to establish
their level of future preparedness. When matching these firms with their performance data in
2015, we could not acquire data on the entire sample. Our reduced sample was, for the
profitability analysis, 70 firms and, for the market capitalization growth analysis, 42 firms.
While this is an important limitation of our research, we were able to boost the robustness of
our findings by relying on the five different tests described in section 3.3. In particular, the
usage of the migration analysis in which we could track the relative competitive position of a
firm vis-à-vis its peers in the industry provided us with a dependent variable that can be judged
to be highly robust.
To assess future preparedness, we relied on the corporate foresight maturity model
(Rohrbeck, 2010a; Rohrbeck, 2010b); however, we introduced the process level, which we
believe to have been an important innovation to improve the measurability of the construct.
ALL FIRMS VIGILANT VULNERABLE IN DANGER NEUROT IC
-49% -101% -108%+200%
While the earlier version, with its five dimensions, made it easy to link measurement to
improvement steps, our new, three-process-step (3Ps) version (see Appendix A, Table 3), which
employs Likert scales, has advantages when measuring large populations of firms. In our
research, we proposed the dimensions of perceiving, prospecting, and probing, which are
inspired by Choo’s ‘knowing organization’ framework as well as Daft and Weick’s model of
organizations as interpretation systems (Choo, 1996; Daft & Weick, 1984). Our 3P model
permits us to derive a more meaningful overall assessment and makes it possible to compare
the maturity with the need level.
To construct the need level, we relied on Day and Schoemakers’ (2005) scales on the
complexity and volatility of the environment. This allowed us to control for industry
differences. For future research, we recommend further enhancement of the measurement of
CF need by adding two additional dimensions. First, we would follow the suggestions by Day
and Schoemaker (2005) to include a “nature of strategy” dimension. However, our
conceptualization of preference would not be to the scale of Day and Schoemaker, but rather
the strategic orientation scale proposed by Miles and Snow, in which they differentiated a
prospector, analyser, defender, and reactor strategy type (Miles, et al., 1978). This would allow
us to also control for inter-firm differences that occur between, for example, two firms in one
industry of which one is an innovation leader and the other uses an innovation follower strategy.
In addition, we believe that CF need is also driven by environmental hostility, or, as Michael
Porter would call it, “rivalry” (Porter, 1979). As an environmental hostility scale we suggest
the scale of Calantone et al. (1997).
Another important avenue for future research is simply to use larger samples. This would
not only allow the replication of our study design, but it would also control for additional
variables such as R&D intensity, past firm performance, investments, and ownership structures
that influence firm performance.
Our study was motivated by the lack of evidence on the impact of CF on firm performance.
We explained the challenges associated with investigating the relationship, including the many
other factors that influence the relationship and the time lag that must be expected between the
exercising of superior CF practices, the adoption of a superior course of action and the effect
on firm performance. To account for the time lag, there have been noteworthy single-case
studies in which, for example, Gavetti and Menon (2016) show how Charles Merrill exercised
strategic foresight to create the financial supermarket business model, revolutionize the
industry, and attain a superior position in this transformed market.
To our knowledge, our study is the first to be able to report on the strategic foresight
maturity of companies and its impact on firm performance in a large dataset. Our study has
produced strong evidence for the positive impact of CF on firm performance. We used a seven-
year time-lag to allow for the translational processes from corporate-foresight insight, to action,
to value appropriation. We also used five different assessment approaches to overcome the
limitations of one with the strengths of another technique. The analysis revealed that future
prepared firms (vigilant) had a significantly higher likelihood of making it to the group of
industry outperformers. We further calculated the positive performance impact of being future
prepared. This analysis revealed that vigilant firms had a 33% higher profitability and a 200%
higher market capitalization growth when compared with the sample average. The firms with
future preparedness deficiencies had to accept a profitability discount (when compared to
vigilant firms) of 37% to 44%. The discount effect for the firms with deficiencies was even
greater on market capitalization and ranged from -49% to -108%.
An additional contribution for future research is the introduction of the ‘future
preparedness’ construct, which is robust against industry-related confounding effects, and with
the addition proposed above of the strategic orientation scale from Miles et al. (1978), it can
also control for firm-specific confounding effects. We also proposed new scales and an
enhanced model for measuring CF maturity and CF need. We hope that this will ease future
research that is designed to further validate the positive effect of CF on firm performance. Our
research also emphasizes the need to engage in multi-modal research, which complements
large-scale survey data with migration analysis and qualitative analysis on the level of the
We hope that our study can also enhance our understanding about how firms need to
prepare to address disruptive change in the environment, to become more resilient and to be
able to drive more long-term transformational strategies, which we also need to make our
economies and societies more sustainable.
7 Appendix A
Table 3 Questionnaire and scale origin
Based on Day and Schoemaker (2005) (original 8 items)
1. Does your company have a high number of competitors?
2. Are your company's competitors easily identifiable?
3. Are the actions of your competitors, contractors and customers predictable?
4. How strongly is your company affected by governmental decisions?
Based on Day and Schoemaker (2005) (original 12 items)
1. How strongly has your company been affected by major changes in the corporate
environment in the past three years?
2. How strongly is your company affected by financial markets?
3. How strongly is your company influenced by the world economy?
4. How well can the speed of technological change be estimated in your industry sector?
5. How well can the direction of technological change be forecasted in your company's industry
6. How well can the behaviour of your company's stakeholders (competitors, contractors,
customers, etc.) be forecasted?
Based on Rohrbeck (2010b)
1. We are scanning current and adjacent businesses, as well as in unrelated areas.
2. We are scanning the technological environment.
3. We are scanning the political environment.
4. We are scanning the economic environment.
5. We are scanning the socio-cultural environment.
6. We are proactively scanning in both the long and medium term.
7. We use a large variety of sources.
8. We are using restricted or exclusive sources, such as personal contacts which yield a
Based on Rohrbeck (2010b)
1. We use methods that allow integrating market and technology perspectives as well as
different time horizons.
2. We use methods that strongly support internal communication.
3. We use methods that strongly support external communication.
4. We select each of our SF methods to solve a specific problem.
5. Our methods have been chosen to reflect the specific context of our company (e.g., volatility
of the environment).
Based on ‘people’ items from the ‘people and networks’ scale
from Rohrbeck (2010b)
1. Foresighters in our company have a broad knowledge reaching beyond their own domain.
2. Foresighters in our company have a strong internal network.
3. Foresighters in our company have a strong external network.
Based on the ‘networks’ items from the ‘people and networks’
scale from Rohrbeck (2010b)
1. SF insights are rapidly diffused throughout the company.
2. SF insights are diffused mostly in a formal manner.
3. SF insights are diffused mostly in an informal manner.
4. What are the main obstacles faced by foresighters in your company?
Based on Rohrbeck (2010b)
1. Our SF activities are issue driven (i.e., directed by a specific question).
2. There are continuous SF activities in place (e.g., scanning for emerging technologies with
3. Our SF activities are triggered top-down (e.g., by top management).
4. Our SF activities are triggered bottom-up.
5. Our SF activities are linked to corporate development.
6. Our SF activities are linked to strategic management.
7. Our SF activities are linked to innovation management.
8. Our SF activities are linked to R&D.
9. Our SF activities are linked to strategic controlling.
10. Our SF activities are linked to marketing.
11. In our company every employee is responsible for detecting weak signals.
12. There are incentives in place that reward scanning for change.
Based on Rohrbeck (2010b)
1. In our company, information is shared freely across functions and hierarchical levels.
2. Our company encourages building and maintaining an external network.
3. Most people in our company are actively scanning the periphery.
4. Basic assumptions are explicitly and frequently challenged.
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