Yann Truong, Dirk Schneckenberg, Martina Battisti and Rachid Jabbouri
Jörg B.A. Haller, Vivek K. Velamuri, Dirk Schneckenberg and Kathrin M. Möslein
Firms increasingly integrate a wide range of actors in the early ideation and concept creation phases of innovation processes leading to the collection of a large number of ideas…
Abstract
Purpose
Firms increasingly integrate a wide range of actors in the early ideation and concept creation phases of innovation processes leading to the collection of a large number of ideas. This creates the challenge of filtering the most promising ideas from a large number of submissions. The use of external stakeholders into the evaluation and selection of submissions (i.e. open evaluation (OE)) might be a viable alternative. The purpose of this paper is to provide a state-of-the-art analysis on how such OE systems are designed and structured.
Design/methodology/approach
Since OE is a new phenomenon, an exploratory qualitative research approach is adopted. In all, 122 instances of OE in 90 innovation contest cases are examined for their design elements.
Findings
This research reveals that OE systems are configured in many different ways. In total, 32 design elements and their respective parameters are identified and described along the six socio-technical system components of an OE system. This study allows for a comprehensive understanding of what OE is and what factors need to be taken into consideration when designing an OE system.
Practical implications
Scholars and professionals may draw insights on what design choices to make when implementing OE.
Originality/value
The comprehensive analysis performed in this study contributes to research on open and user innovation by examining the concept of OE. In particular, it extends knowledge on design elements of OE systems.
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The purpose of this paper is to discuss the potential of Web 2.0 technologies for knowledge management and to explore how corporate governance models influence the adoption of Web…
Abstract
Purpose
The purpose of this paper is to discuss the potential of Web 2.0 technologies for knowledge management and to explore how corporate governance models influence the adoption of Web 2.0 for organisational learning and knowledge exchange.
Design/methodology/approach
The paper begins with a literature review to understand the phenomenon of Web 2.0. It introduces the opposing governance models of hierarchical pyramids and flat pancakes to assess barriers and leverage factors for the implementation of Web 2.0 technologies as a knowledge management system which is based on collaboration and the flow of information in networks; this discourse includes concepts for the nature of knowledge and decision‐making processes. Finally, the potential of Web 2.0 to drive empowerment of knowledge workers is discussed.
Findings
The potential of Web 2.0 technologies to act as a lever for organisational learning and knowledge exchange depends on the degree of openness, freedom, and employee empowerment in corporate environments. Work structures and communication processes differ between employees in corporate settings and peers in web communities. Peers enjoy a high degree of personal freedom and autonomy in their participative behaviour. Employees are on the contrary tied to policies of power, control, and interdependencies within business units.
Originality/value
This article links a discussion of Web 2.0 to ideas for corporate governance and the nature of knowledge. Particular attention is paid to decision‐making policies and organisational structures which pre‐determine the successful application of Web 2.0 technologies for knowledge management.
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The purpose of this paper is to inquire how large multinational firms can develop and implement knowledge-sharing measures that move their corporate strategy towards the open…
Abstract
Purpose
The purpose of this paper is to inquire how large multinational firms can develop and implement knowledge-sharing measures that move their corporate strategy towards the open innovation paradigm, since open innovation becomes increasingly important as source for competitive advantage.
Design/methodology/approach
We review the literature on open innovation and combine it with a single case study of one multinational firm that is gradually implementing its open innovation strategy. We pay special attention to the development and usage of a collaborative IS infrastructure that is deployed to create a culture of openness and to support knowledge networking amongst the workforce.
Findings
The in-depth case study demonstrates that managers have to balance a complex interplay of human and IT components to make open innovation happen. Measures taken to foster openness and knowledge exchange inside the firm include developing managerial innovation capabilities, creating communities and networks around strategic topics and leveraging the adoption of the collaborative IS infrastructure through piloting use of cases in innovation projects.
Research limitations/implications
The findings of this case study remain limited to the characteristics of large firms in multinational markets.
Practical implications
This article offers valuable insights for corporate strategists, IT specialists and change managers who want to open up corporate innovation. We present a range of institutional measures that help to overcome silo mentalities and knowledge-sharing barriers and establish an open innovation culture within large firms operating in multinational markets.
Originality/value
Complementing previous research, this article highlights how large firms can use a combination of strategic, cultural and technological measures to bring open innovation from strategic vision to organisation-wide reality. We identify in addition factors which either inhibit or foster the implementation of knowledge sharing and open innovation practices inside large firms.
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Santo Raneri, Fabian Lecron, Julie Hermans and François Fouss
Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting…
Abstract
Purpose
Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting entrepreneurs in their day-to-day operations. In addition, extant models from the product design literature, while technically promising, fail to propose methods suitable for opportunity development with high level of uncertainty. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. Such an approach aims at harnessing AI-based predictive technologies while keeping the ability to respond to the unexpected.
Design/methodology/approach
Based on effectuation theory, this study identifies an AI-based, predictive phase in the “build-measure-learn” loop of Lean startup. The predictive component, based on recommendation algorithm techniques, is integrated into a framework that considers both prediction (causal) and controlled (effectual) logics of action. The performance of the so-called active learning build-measure-predict-learn algorithm is evaluated on a data set collected from a case study.
Findings
The results show that the algorithm can predict the desirability level of newly implemented product design decisions (PDDs) in the context of a digital product. The main advantages, in addition to the prediction performance, are the ability to detect cases where predictions are likely to be less precise and an easy-to-assess indicator for product design desirability. The model is found to deal with uncertainty in a threefold way: epistemological expansion through accelerated data gathering, ontological reduction of uncertainty by revealing prior “unknown unknowns” and methodological scaffolding, as the framework accommodates both predictive (causal) and controlled (effectual) practices.
Originality/value
Research about using AI in entrepreneurship is still in a nascent stage. This paper can serve as a starting point for new research on predictive techniques and AI-based infrastructures aiming to support digital entrepreneurs in their day-to-day operations. This work can also encourage theoretical developments, building on effectuation and causation, to better understand Lean startup practices, especially when supported by digital infrastructures accelerating the entrepreneurial process.
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Francesco Schiavone, Maria Cristina Pietronudo, Annamaria Sabetta and Fabian Bernhard
The paper faces artificial intelligence issues in the venture creation process, exploring how artificial intelligence solutions intervene and forge the venture creation process…
Abstract
Purpose
The paper faces artificial intelligence issues in the venture creation process, exploring how artificial intelligence solutions intervene and forge the venture creation process. Drawing on the most recent literature on artificial intelligence and entrepreneurship, the authors propose a set of theoretical propositions.
Design/methodology/approach
The authors adopt a multiple case approach to assess propositions and analyse 4 case studies from which the authors provide (1) more detailed observation about entrepreneurial process phases influenced by artificial intelligence solutions and (2) more details about mechanics enabled by artificial intelligence.
Findings
The analysis demonstrates artificial intelligence contributes alongside the entrepreneurial process, enabling mechanisms that reduce costs or resources, generate new organizational processes but simultaneously expand the network needed for venture creation.
Originality/value
The paper adopts a deductive approach analyzing the contribution of AI-based startup offerings in changing the entrepreneurial process. Thus, the paper provides a practical view of the potentiality of artificial intelligence in enabling entrepreneurial processes through the analysis of compelling propositions and the technological ability of artificial intelligence solutions.
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Guglielmo Giuggioli and Massimiliano Matteo Pellegrini
While the disruptive potential of artificial intelligence (AI) has been receiving growing consensus with regards to its positive influence on entrepreneurship, there is a clear…
Abstract
Purpose
While the disruptive potential of artificial intelligence (AI) has been receiving growing consensus with regards to its positive influence on entrepreneurship, there is a clear lack of systematization in academic literature pertaining to this correlation. The current research seeks to explore the impact of AI on entrepreneurship as an enabler for entrepreneurs, taking into account the crucial application of AI within all Industry 4.0 technological paradigms, such as smart factory, the Internet of things (IoT), augmented reality (AR) and blockchain.
Design/methodology/approach
A systematic literature review was used to analyze all relevant studies forging connections between AI and entrepreneurship. The cluster interpretation follows a structure that we called the “AI-enabled entrepreneurial process.”
Findings
This study proves that AI has profound implications when it comes to entrepreneurship and, in particular, positively impacts entrepreneurs in four ways: through opportunity, decision-making, performance, and education and research.
Practical implications
The framework's practical value is linked to its applications for researchers, entrepreneurs and aspiring entrepreneurs (as well as those acting entrepreneurially within established organizations) who want to unleash the power of AI in an entrepreneurial setting.
Originality/value
This research offers a model through which to interpret the impact of AI on entrepreneurship, systematizing disconnected studies on the topic and arranging contributions into paradigms of entrepreneurial and managerial literature.
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Value creation based on artificial intelligence (AI) can significantly change global healthcare. Diagnostics, therapy and drug discovery start-ups are some key forces behind this…
Abstract
Purpose
Value creation based on artificial intelligence (AI) can significantly change global healthcare. Diagnostics, therapy and drug discovery start-ups are some key forces behind this change. This article aims to study the process of start-ups' value creation within healthcare.
Design/methodology/approach
A multiple case study method and a business model design approach were used to study nine European start-ups developing AI healthcare solutions. Obtained information was performed using within and cross-case analysis.
Findings
Three unique design elements were established, with 16 unique frames and three unifying design themes based on business models for AI healthcare start-ups.
Originality/value
Our in-depth framework focuses on the features of AI start-up business models in the healthcare industry. We contribute to the business model and business model innovation by systematically analyzing value creation, how it is delivered to customers, and communication with market participants, as well as design themes that combine start-ups and categorize them by specialization.
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Chang Hwa Baek, Seong-Young Kim, Sung Uk Lim and Jie Xiong
This paper aims to develop a quality evaluation model for artificial intelligence (AI)-based products/services that is applicable to startups utilizing AI technology. Although…
Abstract
Purpose
This paper aims to develop a quality evaluation model for artificial intelligence (AI)-based products/services that is applicable to startups utilizing AI technology. Although AI-based service has risen dramatically and replaced many service offerings, in reality, startups are rarely to develop and evaluate AI services. The features of AI service are fundamentally different from the properties of existing services and have a great influence on the customer's service selection.
Design/methodology/approach
This paper reviews startups' development process, existing quality evaluation models and characteristics of services utilizing AI technology, and develops a quality evaluation model for AI-based services. A detailed analysis of a survey (application of the model) on customer satisfaction for AI speakers is provided.
Findings
This paper provides seven key features and 24 evaluation items for evaluating AI-based services.
Originality/value
This paper contributes to the growing need for methodologies that reflect the new era of AI-based products/services in quality evaluation research.