From disruptively digital to proudly analog: A holistic typology of digital transformation strategies

Recently booming academic interest in digital transformation aims to provide continuous support to managers in dealing with this important issue. However, as with all new fields, the scholarly literature is characterized by increased variability and diversity of topics covered, constructs used, and the relationships between them, resulting in an unclear and blurry understanding of the whole of digital transformation. Our article closes this gap by identifying distinctive digital transformation strategies in terms of two critical dimensions: usage of digital technologies and readiness of a business model for digital operation. The result is a typology of four generic digital transformation strategies that essentially differ in the primary motivation and target of transformation, leadership style, importance of skills like creativity and entrepreneurial spirit among employees, risks and challenges faced in the process, consequences of potential failure, and available tactics for improvement. By providing heuristics and a systematic basis for comparison of different strategies, it is our belief that the proposed framework will be useful to researchers and managers responsible for and interested in digital transformation.     

From AI to digital transformation: The AI readiness framework

Strategies and means for selecting and implementing digital technologies that realize firms’ goals in digital transformation have been extensively investigated. The recent surge in artificial intelligence (AI) technologies has amplified the need for such investigation, as they are being increasingly used in diverse organizational practices, creating not only new opportunities for digital transformation but also new challenges for managers of digital transformation processes. In this article, I present a framework intended to assist efforts to address one of the first of these challenges: assessment of organizational AI readiness—that is, an organization’s ability to deploy AI technologies to enable digital transformation, in four key dimensions: technologies, activities, boundaries, and goals. I show that this framework can facilitate analysis both of an organization’s current sociotechnical AI status and of the prospects for the technology’s fuller value-adding, sociotechnical deployment. The AI readiness framework invites fuller theorizing of the roles that AI can—and will—play in digital transformation.     

Designing an emotional strategy: Strengthening digital channel engagements

The emergence of new technologies has revolutionized the way companies interact and build relationships with customers. The channel–customer relationship has traditionally been managed via a push approach in communication (“What can we sell customers?”) with the hope of cultivating customer loyalty. However, emotional understandings of customers and how they feel about a product, service, or business can drastically alter consumers’ engagement, behavior, and purchasing preferences. This rapidly evolving landscape has left managers at a loss, and what they are experiencing is likely the beginning of a tectonic shift in the way digital channels are designed, monitored, and managed. In this article, digital channel relationships are examined, and useful concepts for clarifying and refining the emotional meaning behind company strategy and their relationship to corresponding digital channels are detailed. Using three case study examples, we discuss the process and impact of such emotionally aware digital channel designs. Recommendations are made regarding how companies can select, design, and maintain digital engagements based on their strategy and industry needs.     

Managing for competency with innovation change in higher education: Examining the pitfalls and pivots of digital transformation

Digital transformation recently converged on organizations as a new paradigm—a must-have exemplar—to enable competitive advantage. While the effects of digital transformation and their analytics, along with platform technologies, are becoming pronounced in companies, there is still a need to examine their implications on higher education. In light of the dynamics of digital transformation, how can higher education better manage the shift toward newer competencies and the need for innovation presented by the emergence of digital technologies? In this article, I examine the issues around the need for this balance—often defined in strategy as ambidexterity, or the need to address both competency with innovation—by outlining the historical trajectories that led to this problem in higher education, identifying three common pitfalls that higher education programs and administrators face, and tying these issues to higher education’s absorptive capacity. To resolve these pitfalls, this article builds upon absorptive capacity frameworks for education practitioners and strategies as a prospective change management tool.     

Making a first impression as a start-up: Strategies to overcome low initial trust perceptions in digital innovation adoption

High failure rates of digital innovations by start-ups indicate that consumers' initial trust perceptions are make-or-break for their survival. Hence, start-ups have to design adequate business models to manage consumers' initial trust perceptions of digital innovations. Five experiments explore how start-ups can signal trustworthiness in order to overcome low initial trust perceptions and boost adoption. We find three specific design strategies of start-ups' digital business models – customer ratings, benefit communication, and revenue model – to be effective to overcome low initial trust perceptions and to increase adoption of digital innovations. The findings demonstrate that initial trust serves as a critical mediator in the relationship between these design strategies and consumers' adoption intentions. Additionally, the chosen revenue model has differential effects on privacy concerns, which mediate the relationship between revenue model and initial trust. The present empirical insights help start-ups to craft business model design strategies for successful digital innovation launch.