Scenario Planning and Newspaper Industry Disruption
Forecasting is useful when leaders
need a reasonable estimate of what will occur if present conditions persist.
Conventional quantitative forecasting projects historical trends on the
assumption that the forces shaping past data will continue in a similar manner,
while scenario-based methods also consider plausible disruptive events and
alternative futures (Alikhani et al., 2025).
The newspaper industry is a
suitable example since its difficulties were not the result of one faulty
forecast. On the contrary, the evidence indicates that over a long period of
time, digital technology brought about changes in distribution, in advertising,
in customer behavior, and in the economics of news, while several of the
established newspapers examined in these studies kept on attempting to maintain
or adjust the old model. The studies discussed here do not establish that each
newspaper formally relied only on forecasting or rejected scenario planning.
What they do show is a delay in adapting, a reliance on familiar assumptions
regarding revenue, and difficulty in changing their beliefs about how value
would be created and captured (Cozzolino & Verona, 2022; Gilbert, 2006). I
think that is precisely the kind of situation in which scenario planning would
have been useful.
Why Scenario Planning Matters
The reason why scenario planning
promotes innovation is that it provides leaders with a systematic method of
preparing for a number of possible future situations rather than focusing on a
single expected result. In trend impact analysis, a quantitative forecast first
sets out the general trend; possible driving forces are then identified,
evaluated in terms of their likelihood and impact, and used to create different
scenarios (Alikhani et al., 2025). In simple terms, the forecast looks at what
would occur if the current patterns continue, while scenario planning goes on
to consider what might happen if they do not.
Rather than attempting to predict
a single correct future, leaders can check whether their current strategy would
still be viable under different circumstances and also be able to spot the decisions
that need to be made early, the signals that should be watched for, and the
options that should remain open. Alikhani et al. (2025) acknowledge that
planners are unable to identify every possible future event or to accurately
estimate every probability; the advantage is that uncertainty is addressed
directly rather than being concealed within a single forecast.
The Newspaper Industry Case
Cozzolino and Verona (2022)
followed six Italian newspapers between 1995 and 2019 as they adjusted to
Internet distribution. The Internet did not remove the value of journalism
itself; reporting, editorial judgment, and analysis still proved to be useful.
The things that changed were the assets employed in the delivery and generation
of revenue from this work, such as printing presses, physical distribution, and
the traditional arrangements for selling advertising. The researchers term this
a complementary-asset discontinuity, by which they mean that the knowledge
involved in producing the product remained valuable while the supporting
systems used for its distribution and sale lost value.
According to Cozzolino and Verona
(2022), early adoption depended on whether leaders thought that digital
technology could be combined with their existing strengths. In order to achieve
a more thorough adaptation, it was necessary to experiment with customers and
with other organizations in the digital environment, showing that it was not
sufficient simply to change the technology; organizations also had to think
again about what readers valued and how the company could keep generating
revenue.
In a study that followed a
newspaper company over time as it adapted to Internet publishing, Gilbert
(2006) identified a similar issue. At various times, the leaders portrayed the
new technology as either an opportunity or a threat, and these different framings
had an effect on the allocation of resources and the organization’s responses.
The case illustrates how an organization can realize that change is taking
place yet still struggle to hold competing interpretations of that change at
the same time. The organization could have benefited from scenario planning,
since that approach would have included the various possible futures in the
planning process rather than requiring leaders to select one main
interpretation too early.
Forces Driving the Disruption
Technological Force: Internet
Distribution
One major force was technological
change; Internet-based publishing, content management systems, mobile devices,
search engines, social media, and digital advertising platforms altered the
ways in which news could be produced, distributed, measured and sold. As
Cozzolino and Verona (2022) noted, these technologies diminished the value of
the physical systems which newspapers had taken years to build, at the same
time as they reduced the barriers to entry for new digital competitors. The
effects were therefore greater than a simple change in the method of
distribution since they changed the structure of the newspaper industry.
A regular forecast, one that is
mainly based on previous circulation or advertising trends, might detect a
decline but would not necessarily illustrate how various parts of the system
could change simultaneously. Instead, a scenario approach could have examined
what would occur if readers switched to online formats sooner than expected, if
digital platforms took on a greater share of the advertising value, or if
printing and distribution ceased to give a competitive advantage.
Economic and Consumer Force:
Revenue and Demand Shift
Another important factor was
revenue and readership patterns. Chyi and Jeong (2024) examined 18 U.S.
metropolitan newspapers from 2016 to 2022 and discovered that digital
subscriptions rose during the COVID-19 period but did not compensate for the
financial loss associated with print subscriptions. The median amount charged
for a digital subscription was much less than that for a print subscription,
and digital subscription income accounted for just 4.9% of the total
subscription revenue for the median newspaper in the sample. Between 2019 and
early 2022, the newspapers acquired digital subscribers but lost far more print
subscribers.
The situation was a tough mismatch
because readers were moving towards digital access, but the new product did not
automatically carry over the revenue model of the previous one. This is
significant when it comes to scenario planning because if one simply assumes
that print usage will decline while digital usage grows, that assumption fails
to address the most important question: what occurs if digital use increases
but digital revenue stays weak?
Scenario Model for the Newspaper
Industry
The four possible future scenarios
illustrated in Figure 1 are based on these two uncertainties. The horizontal
axis shows the speed at which audiences shift to digital news, and the vertical
axis indicates whether digital revenue becomes strong enough to take the place
of falling print revenue. Rather than predicting one future, the model
encourages leaders to consider several combinations before committing to one
business assumption.
Figure 1
Scenario Matrix for Newspaper Industry Planning
If the change in its audience was
slow but digital revenue was strong, it might be reasonable to go ahead with a
managed transition. In the case of a quick shift in audience along with strong
digital revenue, earlier investment in digital areas and a redesign of the
business model could be justified. When there is a slow change in the audience
and weak digital revenue, this could indicate continued cost pressure and
gradual decline. The riskiest scenario would be one involving a rapid move in
the audience together with weak digital revenue, since the traditional business
model could break down before the new one became financially viable. Evidence
from the industry shows that weak digital revenue alongside continued migration
away from print was not just a theoretical possibility (Chyi & Jeong,
2024).
Social Impact of Change
While the decline of newspapers
has an effect on employees and customers, it can also have an impact on
communities which rely on local journalism. Matherly and Greenwood (2024)
looked at the closures of major daily newspapers in the United States and
discovered that such closures were associated with increases in federal
corruption cases, charges, and defendants. Although their findings do not prove
that the loss of a newspaper directly causes corruption, they do indicate that
a reduction in local reporting and investigative ability may have repercussions
beyond the organization.
The matrix itself focuses on
audience and revenue, so a complete scenario plan should also consider who will
be affected in each possible future situation rather than merely asking whether
the company will remain profitable. In the case of newspapers, this would
involve journalists whose positions could vanish, readers who might lose access
to local coverage, and communities which might then have less oversight of
public institutions. I believe this matters because a technically or
financially sound change can still create poor social outcomes.
Using Scenario Planning in Future
Innovation Efforts
I see myself employing scenario
planning as a practical way of checking things over before making major
decisions regarding technology or learning initiatives. Since my present role
involves supporting learning strategy and artificial intelligence (AI) and
machine learning activities, there is a tendency to concentrate on the most
likely scenario: the tool functions, the users take up the tool, and the
organization achieves benefits. For example, what if adoption is slower than
anticipated? What if the technology does work but the workflow ends up being
more difficult? What if there are high levels of participation but the expected
improvement in performance does not show up?
While forecasting is helpful in
dealing with routine needs so long as past patterns remain relevant,
contingency planning becomes important whenever staffing, priorities, or
operating conditions might change. I would approach future innovation efforts
in a similar manner by starting with the most probable forecast, identifying
the factors that could alter it, preparing a small number of plausible
scenarios, and then looking for decisions that are still reasonable in more
than one possible future.
Above all, I would incorporate the
social effects into that process right from the start. In the case of a
learning or AI project, this would involve considering workload, trust, access,
changes in roles, and whether the technology improves the work or merely shifts
the burden to somewhere else.
Summary
The most important lesson for me
is that scenario planning does not take the place of forecasting; instead, it
makes forecasting more useful by considering what might cause the anticipated
course to change. When it comes to future innovation activities, I would apply
this method in order to check our assumptions at an early stage, detect the
important forces, and take into account both the organizational and social
implications before a forecast is turned into a plan.
Alikhani, A., Hosseini Golkar, M., Sharifi, H., Najafi, F., &
Haghdoost, A. A. (2025). Study protocol for applying trend impact analysis in
health futures studies: A methodological approach illustrated by HIV/AIDS
forecasting in Iran. Health Science Reports, 8(4), e70670. https://doi.org/10.1002/hsr2.70670
Chyi, H. I., & Jeong, S. H. (2024). Unraveling US newspapers’
digital and print subscriptions in the context of price, 2016–2022. Media
and Communication, 12, Article 7482. https://doi.org/10.17645/mac.7482
Cozzolino, A., & Verona, G. (2022). Responding to
complementary-asset discontinuities: A multilevel adaptation framework of
resources, demand, and ecosystems. Organization Science, 33(5),
1990–2017. https://doi.org/10.1287/orsc.2021.1522
Gilbert, C. G. (2006). Change in the presence of residual fit: Can
competing frames coexist? Organization Science, 17(1), 150–167. https://doi.org/10.1287/orsc.1050.0160
Matherly, T., & Greenwood, B. N. (2024). No news is bad news:
The internet, corruption, and the decline of the fourth estate. MIS
Quarterly, 48(2), 699–714. https://doi.org/10.25300/MISQ/2023/17869
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