Digital Transformation: Shaping the Future Economy
The Second Economy: A Digital Revolution
Arthur (2011) highlights how digitization is creating a vast, automatic, and invisible second economy, representing the most significant change since the Industrial Revolution.
A new wave of technology is quietly transforming the economy, bringing forth new social classes and a different business landscape. This goes beyond mere computer use, social media, and online commerce. It’s about business processes, once conducted by humans, now executed electronically. This seemingly subtle shift is creating a digital, second economy.
This unseen, digital interaction involves servers communicating with each other, satellites, and computers. Complex conversations, triggered by our actions, occur remotely among servers, switches, routers, and other devices, constantly updating and transferring information. What was once manual is now a series of digital conversations.
Processes from the physical economy are entering the digital realm, engaging in constant communication with other digital processes. This involves multiple servers and semi-intelligent nodes updating, querying, checking, and readjusting, ultimately connecting back to the physical world. This second economy is vast, silent, connected, unseen, and autonomous, designed by humans but operating independently. It’s concurrent, with everything happening in parallel, and self-configuring, constantly adapting, self-organizing, and self-healing.
The digital economy is projected to equal the physical economy in size within two decades and surpass it in two to three. This isn’t a minor addition; it’s a fundamental shift. While it doesn’t produce tangible goods, it manages a significant portion of the economy. All advanced economies, particularly in Europe and Japan, are undergoing this deep transformation.
This digital realm acts as a neural layer for the physical economy. Individual machines (servers) function like neurons, connected by communication pathways (axons and synapses), enabling constant interaction and appropriate action. This represents a profound qualitative change, bringing intelligent, automatic responses to the economy. The second economy is steadily creating a new world.
The Downside: Job Displacement
A significant concern is the adverse impact on jobs. Increased productivity means either producing more with the same workforce or achieving the same output with fewer people. Overall, national output requires fewer workers as physical jobs transition into the digital realm. While human judgment and interaction remain necessary, the second economy, while driving growth and prosperity, may not provide sufficient jobs, leading to prosperity without full access. The main economic challenge is shifting from producing prosperity to distributing it equitably.
Ubiquitous Computing: The Invisible Technology
Weiser (1991) emphasizes that the most profound technologies are those that disappear, becoming indistinguishable from daily life, like the Internet. He introduces the concept of “embodied virtuality,” drawing computers out of their electronic shells.
Most computers involved in embodied virtuality will be invisible, integrated into everyday objects like cars and stereos. Weiser envisions different forms of ubiquitous computing devices:
- Tabs: Tiny networked computers.
- Pads: “Scrap computers” for ubiquitous use, lacking individual identity.
- Boards: Interactive displays for various purposes, like bulletin boards or flip charts.
Ubiquitous computing requires cheap, low-power computers with convenient displays and a network connecting them. Falling display prices and rising quality, driven by the popularity of laptops and notebooks, support this trend.
Social Implications of Ubiquitous Computing
Ubiquitous computing raises social concerns, particularly regarding privacy. With numerous computers capable of scanning and intercepting messages, privacy may diminish, and information could fall into the wrong hands.
However, ubiquitous computers, residing in the human world, don’t necessarily hinder personal interactions. Their transparent connections across locations and time might even strengthen communities. They promise to make tasks faster and easier, reducing strain and mental effort. Ubiquitous computers can also help combat information overload, making computer use as refreshing as a walk in the woods.
Computer Science Challenges in Ubiquitous Computing
Weiser (1993) defines ubiquitous computing as enhancing computer use by making many computers available throughout the physical environment while remaining invisible to the user. This presents a new research framework across computer science.
Ubiquitous computing aims to create an environment where individuals constantly interact with hundreds of nearby, wirelessly interconnected computers. The goal is to achieve the most effective technology, one that is essentially invisible. This differs from Personal Digital Assistants or autonomous computer agents. Ubiquitous computing focuses on empowering the user directly, making them stronger rather than relying on an assistant.
Weiser categorizes ubiquitous computing devices into boards, notepads, and tiny computers. The initial phase involves constructing, deploying, and learning from an environment comprising these devices. The focus shifts from the computer to the person and their daily life.
Key Challenges
- Hardware: Low power consumption, wireless interaction, and pens for large displays.
- Networking: Wireless media access, wide-bandwidth range, real-time capabilities for multimedia, and packet routing.
- Interaction Substrates: Addressing challenges with small interaction areas (pads), large physical size (liveboards), and window system limitations (X window system).
- Applications: Locating people and shared drawing, while preserving privacy.
Technological solutions alone cannot address privacy; social issues must be considered. Computer science labs are striving to create privacy-enabled systems that empower individuals.
Technology and Economic Dynamics
Dosi, Orsenigo, and Sylos Labini (2002) investigate the relationship between technological learning and economic dynamics, drawing insights from innovation economics, industrial economics, economic sociology, and the history of technology.
The paper identifies drivers of technological change, their social and institutional roots, and the dynamic coupling between technological learning, corporate organization, and economic evolution. The economic impact of technological innovation depends on the “matching” or “mismatching” between:
- Opportunities and constraints of available technologies.
- Structures and behaviors of business firms.
- Characteristics of institutions governing labor, finance, and product markets.
Stylized Facts
Since the Industrial Revolution, diverging income patterns have emerged from relatively similar pre-industrial levels. The income gap between the richest and poorest countries was smaller before the Industrial Revolution. Afterward, the dominant trend is increasing differentiation and overall divergence.
There are significant links between innovative activities and GDP per capita, although measuring innovation is challenging. The connection between innovation and GDP has strengthened over time, particularly between 1913 and 1970.
Technological Trajectories
Each body of knowledge (paradigm) shapes and constrains the rates and direction of technical change, leading to regularities and invariances in the pattern of technical change.
Science and Technology
In this century, major innovations are increasingly science-based. Factors influencing scientific and technological knowledge include:
- Knowledge as new “properties of nature.”
- Development of new design tools.
- Training of applied researchers.
Technology contributes to science by:
- Providing new scientific challenges.
- Addressing scientific questions more efficiently.
A concern is that scientific research may become overly dependent on immediate economic interests, potentially undermining the ethos of science.
New Paradigms
The genesis of new paradigms should be separated from the processes leading to their dominance. For instance, the microelectronic paradigm was initially shaped by military requirements. Many factors influence paradigm development.
There’s little evidence that tighter appropriability regimes (patents, secrecy) foster innovation; in fact, they may inhibit it. Excessive intellectual property rights (IPR) can lead to too many owners, slowing down research due to potential blockages. Paradigms and dominant designs act as sources of variation-generation and “blindness-reduction.”
Social and political forces play a crucial role in selecting among potential paradigms and shaping the trajectories explored within each paradigm. The social shaping of technology is paramount.
There are profound reciprocal influences between technological, economic, and social factors. The accumulation of technological knowledge entails an inner logic and constraints that social or economic drivers can hardly overcome in the short term.
This co-evolutionary perspective presents “windows of opportunity” for social action and binding constraints inherited from history or available technologies. Micro-paradigms exhibit considerable invariances across countries, but their combination in broader national innovation systems displays variety, shaped by country-specific institutions, policies, and social factors. An evolutionary microfoundation is a fruitful starting point for larger contexts (national/international paradigms).
Complexity in Discrete Innovation Systems
Hirooka (2006) clarifies the complexity of innovation systems using a specific procedure. The innovation paradigm comprises three logistic trajectories: technology, development, and diffusion, each with a non-linear nature. There is no single logistic equation. In the electronics innovation paradigm, the development trajectory follows Moore’s Law.
Innovation diffusion obeys a logistic equation (the system is nonlinear). The innovation paradigm is a discrete system formed by knowledge transfer, making it complex. It has a latent period of technological development before product diffusion, which accelerates over time.
Describing Innovation Diffusion
Innovation diffusion can be described by a logistic equation. The diffusion of new products is often influenced by economic turbulence (recession, war). However, in a sound economy, Hirooka demonstrates that the diffusion of innovation products follows a logistic equation.
Key observations:
- New product diffusion obeys a simple logistic equation during sound economic periods.
- Diffusion is easily disturbed by economic turbulence, causing demand to decrease and diverge from the original trajectory.
- After a recession, product diffusion resumes with the same slope as before, indicating an inherent trajectory with an intrinsic diffusion coefficient.