Digital Payday: market value implications of digital transformation

Is it possible to develop a more objective, scientific understanding of the digital transformation and its impact on market value for shareholders in big companies? This question is the inspiration for the study Digital Payday: Towards Better Understanding the Market Value Implications of Digital Transformation for Large Corporates. In an attempt to find an answer, we also explored these prime, pertinent questions: 

• How can we come up with an exhaustive definition, a transversal conceptual framework and a common language for analyzing digital transformation? 
• What methodologies (subjective and objective) should we use to evaluate the direct and indirect outcomes of DT? 
• Can DT, as we describe in our framework and empirically analyze, affect the market value of companies’ assets and future earnings? If so, to what extent? 
• In light of this new framework, what conclusions can we draw on the implications of DT in terms of market value for corporates? 

The aim of the study is to demonstrate that it is possible to determine, on a scientific basis, a statistically significant market value “payday” that can capture investments in DT initiatives.  

Both academic and business research on digital transformation present two major shortcomings. The first is that they are not based on empirical evidence, but rather on subjective experiences and general outcomes, without any scientifically substantiated proof of the financial impact of DT. The second is that as a starting point they look to successful start-ups, holding up the unique features of a few specific cases as a descriptive model for the vast and varied landscape of digital platforms. Either that, or they used anecdotal evidence and anonymous surveys, in any case with insufficient factual evidence.  

Digital Payday: Towards Better Understanding the Market Value Implications of Digital Transformation for Large Corporates underscores the limitations of these approaches, which risk distorting the correct interpretation of the context and undermining the possibility for future growth for big companies.  

The data are unequivocal: in 2019, investments in digital transformation were seen as extremely risky, and topped the list of concerned cited by managers and CEOs.  This was more than justified if we consider that 70% of all the digital transformation initiatives fell short of their targets that year. In fact, of the $1.3 trillion spent on DT, around $900 billion ended up being wasted. 

With these observations as a takeoff point, we formulated a new, more complete definition of digital transformation. On one hand, DT can be proactive or reactive, to acquire or mitigate the drivers of supply and demand (that are already visible or expected) which are associated with technology. On the other, DT supports new ways of doing business based on real strategic options for developing new skills and processes and building relationships inside the company or interfaces with other companies. 

The theoretical model we propose integrates a pragmatic, empirical approach with advanced analytics, striking a balance between managerial perspective and scientific vision. The end result is a framework based on (financial) data, which is anchored in experience and exploits recent advances in natural language processing and analysis. Once we established this new theoretical framework, we tested it out on four working hypotheses relative to the impact of DT on corporates’ market value. Our findings substantially confirm all our hypotheses. 

• More and more often, large listed corporates include “replicable references” in their reports on DT; this refers to elements which are non-sequential but can impact one another. 
• These companies opt to use a conservative approach in reporting these references on digital transformation, including more qualitative than quantified statements. 
• The degree of relevance of replicable references regarding DT in large listed corporates is specific to the firm context. 
• For these organizations, the reports on replicable reference regarding DT show a relationship to financial value. This relationship is positive for market capitalization and includes future earnings. The positive relationship for market capitalization is stronger when replicable references are more concrete, and this relationship is not universal, but sensitive to the business context. 

The study doesn’t offer definitive formulas that are valid in every case, but does suggest some recommendations for management practices and for further research. Although we acknowledge that a universally positive impact on market value of DT is not empirically observable, it can still generate value if it’s done at the right time, in the right context, with the right mix of transformation ingredients. In conclusion, even if there is no such thing as a one-size-fits-all approach, applying the proper elements of DT at the most appropriate time is often the key to obtaining concrete, measurable benefits.  

A massively enhanced version of this research will be available as a globally launched and promoted Wiley business book “Digital Transformation Payday” already this winter.