Digital Strategy Formulation: An Investigation with Design Sprints and Deep Learning

Since the invention of transistors, digital technologies have continued to have a profound impact on the global economy. Relentless performance improvements combined with convergence of digital technologies such as artificial intelligence, internet of things, and cloud computing has led to a surge in scale and importance as a source for competitive advantage. However, in 2019, only around 16% of companies managed to realize a significant improvement in business performance from digital transformation (DT). The challenges that organizations face in succeeding at DT can be traced back to strategy formulation and execution. Therefore, the aim of this research is to develop insights and tools to enhance the understanding and practice of digital strategy formulation. A comprehensive review of the literature demonstrated that DT, as an emerging body of knowledge, is lacking an in-depth and applied investigation of digital strategy formulation. The main knowledge gaps are: (1) a lack of guidance on digital strategy formulation process activities and outcomes; (2) limited consideration of the iterative nature of digital strategy formulation and validation; and (3) limited empirical investigation of digital strategy archetypes to guide the formulation process. Addressing this research gap was accomplished over three stages. First, an in-depth exploratory case study was conducted by investigating digital strategy formulation process with active participation research over six months. This investigation identified key process activities and highlighted the role of roadmapping in integrating the outcomes. Second, the findings were supplemented with literature review to design a conceptual framework for agile roadmapping to facilitate the digital strategy formulation process. This framework was then tested and calibrated over three pilot studies with companies across Europe attempting to start their DT journey. Finally, deep learning and natural language processing techniques were employed to empirically investigate the digital strategy of Fortune 500 companies from earnings call transcripts. This empirical investigation identified four digital strategy archetypes that are being employed by companies across various sectors. The findings from this research contribute to a better understanding of digital strategy formulation. It was identified that digital strategy formulation is an ongoing search process for an adequate strategic response to the DT of the economy. Specifically, incorporating agility into the formulation process is an effective way of managing the associated uncertainty of DT. Moreover, the findings demonstrated that proactively iterating between strategy formulation and validation can accelerate the realization of the emergent digital strategy. The proposed framework and the digital strategy archetypes provide a baseline for DT professionals toward a more robust digital strategy formulation.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2020

Author dc:creator

• Al-Ali, Ahmed

Advisor dc:contributor.advisor

• Phaal, Rob

• digital transformation
• digital strategy
• design sprints
• deep learning
• natural language processing

Licence

• https://www.repository.cam.ac.uk/bitstreams/d6af6bfa-33ec-4bdb-8652-53a7001424a3/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.59291

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/312199