Published on April 3rd, 2014 | by Irene Ng0
Research Challenges for the Digital Service Economy
Recently, I was invited to participate in a workshop to help define the roadmap for future service research and education for the US National Science Foundation (NSF) and its industrial partners.
To help facilitate the session, participants were tasked to draw up key challenges in our areas of service research, ahead of the workshop in Washington DC on Apr 10 and 11.
With my current work on the Hub-of-All-Things project, I can identify three research challenges that I believe will have implications on the growth of the future digital service economy:
The customer as part of the system
As service research moves from Big Data to many systems methodologies, the customer is often overlooked. Yet from a service-dominant logic perspective, service is co-creation; this means that the customer is part of — and not outside — the system.
There is hence a need to develop methodologies that treat the customer as an entity in the system, whether as a human sensor, a human intelligence, or a meanings/context creator i.e. a resource-integrating and contributing entity.
We need to question the position, mindsets and perspective of the researcher when constructing systems methodologies and research findings. This is becoming increasingly important as customer resources to co-create value evolve into a more structured resource eg. personal data.
As the customer becomes increasingly empowered through technologies, he or she is a driver for future economic opportunities as both a consumer as well as a producer. The application of personal data in co-creating value with a product or service can have a massive multipler effect on the personal data economy and national economies of the future.
The incomplete product
The boundary between a service and a material product is becoming increasingly obscured. As material technologies evolve, a physical product can be designed to be more dynamically reconfigurable to fit in with the diverse and dynamic interactions of actors in their context
Dynamic reconfigurability enables the system to ‘have the capability to modify their functionalities, adding or removing components and modify interconnections between them’. As pervasive digital technology develops, dynamic reconfigurability becomes possible in future products which could have a ‘reprogrammable nature’. This allows products to have new capabilities even after they’ve been designed, manufactured and sold.
This means that products may not need to be ‘finished’ to be transferred to the customer. Instead, they could be designed such that contexts of use could be incorporated into a modular product design, to be ‘finished’ by the consumer using their own resources (e.g. personal data) through pervasive digital technologies.
Products evolve into platforms for service that could provide increasing returns to scale through standardisation, even while they can be deeply and uniquely personalised. For example, the iPhone is fully standardised and enjoys economies of scale yet it can be fully personalised, thanks to the blurring of the boundary between the digital ‘app’ and the material ‘phone’.
New Transaction Boundaries, Economic and Business Models
An economic model is the model of an ecosystem (like a market) that distributes rents (or revenues) either through the pricing mechanism or regulation, according to what the entity (such as a firm) does to stay within the ecosystem. New economic models, often arising from new business models and/or new entrants, redistributes rents within the ecosystem, occasionally resulting in the exit of existing entities (disruption).
With the blurring of boundaries between the material and digital, firm and customer, product and service, there is a need to understand new ways to obtain revenues and the nature of transactions in the future digital service economy.
The value-creating context, as a unit of analysis for service, jointly co-created by the customer and the producer, creates an interesting challenge for modularity and product/service architecture for new innovations. Modularisations create what Baldwin calls ‘new thin crossing points’ where transaction costs are low, and also create opportunities for new boundaries where new transactions and new business models can be created.
These research and innovation challenges also impact on education and skills, as there are increasingly greater overlaps in domain knowledge, particularly between engineering and computer science. To help develop future engineer/technologists and managers, there is a need to move beyond the current reductionistic curriculum.