Refine
Document Type
- Conference proceeding (62)
- Journal article (12)
- Book chapter (6)
- Book (1)
Is part of the Bibliography
- yes (81)
Institute
- Informatik (77)
- ESB Business School (2)
- Life Sciences (2)
Publisher
- Springer (36)
- IEEE (8)
- Gesellschaft für Informatik (7)
- Association for Information Systems (4)
- RWTH Aachen (4)
- University of Hawai'i at Manoa (4)
- University of Hawaii at Manoa (4)
- Elsevier (3)
- IGI Global (2)
- SciTePress (2)
Assistant platforms
(2023)
Many assistant systems have evolved toward assistant platforms. These platforms combine a range of resources from various actors via a declarative and generative interface. Among the examples are voice-oriented assistant platforms like Alexa and Siri, as well as text-oriented assistant platforms like ChatGPT and Bard. They have emerged as valuable tools for handling tasks without requiring deeper domain expertise and have received large attention with the present advances in generative artificial intelligence. In view of their growing popularity, this Fundamental outlines the key characteristics and capabilities that define assistant platforms. The former comprise a multi-platform architecture, a declarative interface, and a multi-platform ecosystem, while the latter include capabilities for composition, integration, prediction, and generativity. Based on this framework, a research agenda is proposed along the capabilities and affordances for assistant platforms.
Digital assistants like Alexa, Google Assistant or Siri have seen a large adoption over the past years. Using artificial intelligence (AI) technologies, they provide a vocal interface to physical devices as well as to digital services and have spurred an entire new ecosystem. This comprises the big tech companies themselves, but also a strongly growing community of developers that make these functionalities available via digital platforms. At present, only few research is available to understand the structure and the value creation logic of these AI-based assistant platforms and their ecosystem. This research adopts ecosystem intelligence to shed light on their structure and dynamics. It combines existing data collection methods with an automated approach that proves useful in deriving a network-based conceptual model of Amazon’s Alexa assistant platform and ecosystem. It shows that skills are a key unit of modularity in this ecosystem, which is linked to other elements such as service, data, and money flows. It also suggests that the topology of the Alexa ecosystem may be described using the criteria reflexivity, symmetry, variance, strength, and centrality of the skill coactivations. Finally, it identifies three ways to create and capture value on AI-based assistant platforms. Surprisingly only a few skills use a transactional business model by selling services and goods but many skills are complementary and provide information, configuration, and control services for other skill provider products and services. These findings provide new insights into the highly relevant ecosystems of AI-based assistant platforms, which might serve enterprises in developing their strategies in these ecosystems. They might also pave the way to a faster, data-driven approach for ecosystem intelligence.
Platforms have become pivotal business models and involve a different logic than traditional pipeline business models. Important factors for understanding their emergence and growth are externalities such as network effects and complementarities. At present, these concepts are focused on the effects on a single platform, but with the diffusion of platforms and their maturity, platforms are increasingly linked to each other. This interconnection of multiple platforms towards multi-platform ecosystems poses two key challenges. First, their networked structure exceeds traditional analytical approaches that are based on dyadic relationships. Second, individual choices drive externalities in these ecosystems, giving rise to emergent structures. To address these issues, the present research proposes a network science-based methodology that augments existing approaches to understand and visualize ecosystems (“ecosystem intelligence”). It presents a network conceptualization that captures the structure of multi-platform ecosystems and proposes a method for data collection and detailed network modeling. Among the main findings are three new types of externalities referred to as higher-order externalities. These include remote externalities that indicate value creation across platforms, transitive externalities representing chains between platforms, and polyadic externalities capturing value creation in n-ary relationships. They contribute to the understanding and management of the intricacies of multi-platform ecosystems, which can open new avenues in ecosystem intelligence.
Platforms feature increasingly complex architectures with regard to interconnecting with other digital platforms as well as with a variety of devices and services. This development also impacts the structure of digital platform ecosystems and forces providers of these services, devices, and services to incorporate this complexity in their decision-making. To contribute to the existing body of knowledge on measuring ecosystem complexity, the present research proposes two key artefacts based on ecosystem intelligence: On the one hand, complementarity graphs represent ecosystems with an ecosystem's functional modules as vertices and complementarities as edges. The nodes carry information about the category membership of the module. On the other hand, a process is suggested that can collect important information for ecosystem intelligence using proxies and web scraping. Our approach allows replacing data, which today is largely unavailable due to competitive reasons. We demonstrated the use of the artefacts in category-oriented complementarity maps that aggregate the information from complementarity graphs and support decision-making. They show which combination of module categories creates strong and weak complementarities. The paper evaluates complementarity maps and the data collection process by creating category-oriented complementarity graphs on the Alexa skill ecosystem and concludes with a call to pursue more research based on functional ecosystem intelligence.
Assistants and platforms based on artificial intelligence (AI) have become a new general-purpose technology (Helpman, 1998) in the digital economy, with chatbots and virtual personal assistants as examples. AI-based assistants and platforms provide seamless and intuitive access to digital services and devices. They free humans from the burden of acquiring domain knowledge and resources, allowing them to focus on more complex tasks. AI-based assistants also create business value by automating processes, intensifying user interaction, reducing errors, and speeding up interactions. While the concept of digital assistants is not new, the diffusion of general-purpose assistants, such as Amazon’s Alexa, Apple’s Siri, or Google’s Assistant (Këpuska & Bohouta, 2018), has fundamentally changed the presence of assistants. The technology is constantly advancing with developments in voice processing (Sivapriyan et al., 2021) and generative technologies, such as ChatGPT, Google Bard (Gozalo-Brizuela & Garrido-Merchan, 2023), and large language models like Bloom (Scao et al., 2022).
Current advances in Artificial Intelligence (AI) combined with other digitalization efforts are changing the role of technology in service ecosystems. Human-centered intelligent systems and services are the target of many current digitalization efforts and part of a massive digital transformation based on digital technologies. Artificial intelligence, in particular, is having a powerful impact on new opportunities for shared value creation and the development of smart service ecosystems. Motivated by experiences and observations from digitalization projects, this paper presents new methodological experiences from academia and practice on a joint view of digital strategy and architecture of intelligent service ecosystems and explores the impact of digitalization based on real case study results. Digital enterprise architecture models serve as an integral representation of business, information, and technology perspectives of intelligent service-based enterprise systems to support management and development. This paper focuses on the novel aspect of closely aligned digital strategy and architecture models for intelligent service ecosystems and highlights the fundamental business mechanism of AI-based value creation, the corresponding digital architecture, and management models. We present key strategy-oriented architecture model perspectives for intelligent systems.
Assistant platforms are becoming a key element for the business model of many companies. They have evolved from assistance systems that provide support when using information (or other) systems to platforms in their own. Alexa, Cortana or Siri may be used with literally thousands of services. From this background, this paper develops the notion of assistant platforms and elaborates a conceptual model that supports businesses in developing appropriate strategies. The model consists of three main building blocks, an architecture that depicts the components as well as the possible layers of an assistant platform, the mechanism that determines the value creation on assistant platforms, and the ecosystem with its network effects, which emerge from the multi-sided nature of assistant platforms. The model has been derived from a literature review and is illustrated with examples of existing assistant platforms. Its main purpose is to advance the understanding of assistant platforms and to trigger future research.
