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Why a Good Theory Matters

Aroop Gupta, 5/2/2016

Disruption theories are a causal explanation of a firm’s actions when faced with a competition from a much smaller competitor under a specific circumstance.

On what makes this approach different from the others is that while we use data to construct the current scenario, we do not extrapolate the findings to look at the future. Our belief is that, data is a good way to build the current situation or the historical picture but a good causal theory produces much more accurate and reliable results about the future.

It is important to remember that data is an output of a process or a function. Predicting the future by looking at the patterns of outputs is only co-relative and not causal. To predict the output with high degree of confidence and accuracy, it is important to understand the function and how the function behaves under different circumstances. In other words while data may be co-relative the process or the function producing the data, is causal to the output. Understanding the function (or the causal reason) is much better way of predicting the future than predicting based on data which is only co-related.

An example of this would be the success rate of the marketing campaigns that are driven primarily by demographics data. Due to a data driven approach, majority of these campaigns reach a mere 30% of the total addressable market[i]. A causal approach would be to focus on the intent of the consumer which yields much more consistent and accurate results.

The causal nature of the theories of disruption make it a powerful tool to visualize the future based on the current circumstance. Also the constantly improving and updating nature of these theories by analyzing anomalies and outliers, strengthens the underlying functions periodically.

Often there are claims that disruption is impossible in an industry because of industry specific reasons (regulations, entry costs, operating costs, economic reasons etc.). Which is true but these may be places which are just ripe for disruption and waiting for an innovation. Studies in the field of disruption show that innovations which eventually end up being disruptive find a way to work around these obstacles. Some of the examples are Uber, AirBnB, and Steel producing Mini Mills.

While a good causal theory may help Build and Sustain an Enterprise by:

Understanding what theories are applicable under what circumstance

Understanding the needs of your customer based on a circumstance

  • Identifying potential non-consumption of the firm’s products or services and the competitive set

  • Adapting the business model to cater to non-consumption

  • Approach to take in different phases of a firm (integrated vs modular

  • Keeping the organization flexible in terms of resources, processes and priorities to avoid disruption

    A causal theory may not be able to predict accurately:

      Timeframe in which the future events unfold

      Speed of Disruption - How fast or slow disruption would progress

      Areas of origin – Players or the industries from where disruption may originate

        [i] https://www.thinkwithgoogle.com/articles/why-consumer-intent-more-powerful-than-demographics.html