Editorial: Web3 and service science: perfectly aligned to maximize the values of all stakeholders in the digital economy

Citation

Qiu, R. (2024), "Editorial: Web3 and service science: perfectly aligned to maximize the values of all stakeholders in the digital economy", Digital Transformation and Society, Vol. 3 No. 4, pp. 337-339. https://doi.org/10.1108/DTS-11-2024-102

The world is gradually approaching AI times regardless of how AI-driven life in the information era should be defined or what should be perceived by people in their daily lives. As witnessed, since the introduction of ChatGPT at the end of 2022, generative AI has demonstrated its revolutionary capability of creating new contents and solutions resembling human-generated outputs. A variety of generative AI applications have been emerging, making clear that the world is largely embracing the dawn of AI times. I recalled that I wrote in an early editorial in DTS this year (Qiu, 2024), articulating that this wave of AI phenomenon had already started to substantially enable and empower the on-going digital transformations across industries, businesses, organizations and societies around the world.

Without loss of generality, the following noticeable observations show how AI has been transforming various sectors, focusing on enhancing efficiency, gaining insight and driving innovation:

• (1)

  In healthcare, AI facilitates diagnosing, predicts patient health risks and recommends treatment options by analyzing medical records and patient data.

• (2)

  In the automotive industry, assistive driving or self-driving capabilities are radically empowered by AI.

• (3)

  Through gaining insights into customer preferences and consuming behaviors, retailers, travel and hospitality agencies and entertainment service providers are heavily dependent on AI-driven recommendation tools to provide customers with personalized products.

• (4)

  To deliver a responsive and personal-touch customer experience, AI-powered chatbots, virtual agents and robots are widely adopted in enabling and empowering automated and human-like customer support and services for improved customer engagement and satisfaction.

• (5)

  Although facing numerous challenges in safety, governmental regulation, public acceptance and technology, AI-driven autonomous vehicles have attracted a vast amount of investment and have promisingly progressed toward becoming a mainstream mode of transportation, innovatively transforming city mobility and the future of city life, business and community around the world. Innovations and transformations will make our cities smarter, safer and more sustainable.

Evidently, over the last half century, driven by the fast development and proliferation of digital technologies, mainly including computing, telecommunications, data science, AI and information technology, the great leap from the industrial revolution to the current information era has transformed the world society to a digitalized and well-interconnected world. However, the data rich and information poor (DRIP) situation is still daunting across all the corners of people’s daily lives, given that data and information on certain topics from the Internet can be outdated, incorrect, inaccurate, incomplete and misleading. When mismatched and incomplete data, fake news, political propaganda and conspiracy theory are not logically preprocessed or eliminated, such a trained AI-driven application can not only be unreliable and untrustful but also evilly harmful frequently if it falls on the wrong hands. Therefore, without the implementation of appropriate governance and oversight, this powerful yet unregulated AI technology could put forward risks of aggravating problems like purposed bias, deepfakes, misinformation and unfairness. To ensure that AI will be well adopted in many aspects of people’s lives, feeding the right data and knowledge into an AI system’s training and, more importantly, its domain-based fine-tuning process becomes the most critical task in integrating AI into truly productivity-oriented and/or critical daily life solutions (Qiu, 2024).

Promisingly, the data quality in the future Internet powered by Web 3.0 (or Web3) can be tremendously improved. Different from the current Internet or so-called Web 2.0 largely relying on user-generated contents and data that are stored in centralized platforms (such as service provider systems, social media networks and clouds), Web3 is emerging and quickly evolving. Decentralization, user self-control, transparency, data privacy and interoperability will be the main operations and governance characteristics of the generated contents and data in Web3 and corresponding applications. Because the design and development of a Web3 application will take its content and data quality management into consideration at the very beginning, the future AI-driven productivity-oriented and/or critical daily life solutions derived from Web3 can address well many challenges confronted by the current AI-powered systems discussed above.

Uncovering and releasing the data values could be ultimately embodied with the development of Web3 technology. In other words, the promotion and support of data self-control and management will encourage and enhance user participation and engagement, contributing to the development of high-quality data market in the future. As a result, AI-powered solutions in Web3 will be more trustful, reliable and capable, totally reshaping how the future digital interactions and transactions will be conducted throughout industry, business, public service and people’s daily lives (Qiu, 2025).

The introduction of Service Science was highly stimulated and driven by the quick advancements of computing, networking and communication and Internet technology in general. It is well known that services have made a drastic shift from their traditional perceptions as intangible and secondary to goods to recognition of their essential roles and evolutionary characteristics in the service-oriented and digital economy (Maglio et al., 2019). Driven by the nature of value-cocreation in service, Service Science consistently focuses on a systemic study of service (eco)systems. In other words, Service Science promotes and supports engineering, operations and management that can enable and empower the process of value cocreation in a service lifecycle, focusing on engaging all involved individual stakeholders to optimally maximize their values, respectively (Qiu, 2014).

From the above discussions, promoting the values of all stakeholders is the common goal of Web3 and Service Science. Aligning them well to support the development of digital economy makes a lot of sense. Put it alternatively, Service Science should be further developed and well adopted in Web3 development and applications to support the future development of the digital economy around the world. The following reasons should support this declaration:

• (1)

  Deepservice is the future need of consumers. Deepservice should be understood as a service that is deeply personalized, respected and meets the needs of its customers in a comprehensive manner.

• (2)

  Such a service must be designed, engineered and delivered based on profile and/or data/AI-driven and value-cocreation processes.

• (3)

  In today and the future digital economy, a high-quality service must be people-centric, technology-driven (e.g. AI-empowered) and process-oriented.

• (4)

  Given that personal data must be protected and valued in the digital economy, the evolution of digital technologies such as blockchain, metaverse and privacy preserving plays a key role in ensuring that such a service becomes a truly win-win business or societal activity in human daily life.

In conclusion, we call on all different aspects of research on Web3 and Service Science in support of digitally transforming business, service and society for a brighter and more promising future in AI times.