International Journal of Innovation Studies最新文献

Innovation capabilities (ICs) represent a crucial source of competitive advantage for firms. However, the literature on ICs is extensive, leading to a diverse understanding of their nature and measurement. A notable gap exists in delineating the dimensions constituting ICs. This article aims to address this gap by identifying and pinpointing the various dimensions of ICs through a systematic literature review (SLR). The initial step involves identifying the diverse dimensions used in ICs, providing a distinctive insight for assessing their metrics. Notably, this SLR stands out as the only comprehensive analysis of various ICs dimensions, organizing them coherently. Examining 103 articles from the Web of Science and Scopus databases spanning from 2001 to 2022, the results reveal an amalgam of scales and associated approaches for IC measurement. This study contributes to the literature by systematically identifying and analyzing the main dimensions employed by researchers to measure ICs. Additionally, it highlights the foundational theoretical approaches of the identified studies. In practical terms, the study consolidates and presents the identified dimensions and metrics in integrative tables, offering researchers and companies valuable insights into diverse innovation paths that impact performance.

Product innovation is crucial for the future of the publishing industry, and the objective of this study was to understand the role of multi-disciplinarity in publishing innovation teams, typical patterns of interaction between team members, and similarities and differences in multidisciplinary innovation teams across national boundaries (US, Canada, and Germany). This is an exploratory study from Portland State University, Stuttgart Media University and Toronto Metropolitan University based on twenty-one qualitative in-depth interviews with publishing employees involved in the innovation process. The study reveals that multidisciplinary innovation teams are common in all three countries. Using an input-process-output (IPO) model, researchers found three main things: 1) input from outside the company is helpful; 2) corporate culture, familiarity and psychological safety foster creativity during the innovation process; and 3) innovation output is often evaluated by low-cost prototypes presented to the target audience.

The concept of entrepreneurial ecosystems (EE) is gaining increasing attention from academics, professionals, and policymakers because of its potential as a policy tool for promoting economic growth. However, the theoretical foundation for analyzing EE needs further development to comprehensively capture its systemic, complex, and adaptive nature. Although recent studies have made progress in this area by incorporating complexity theory into this field of literature, the multilayer characteristics of an EE have been overlooked in those conceptualizations. We therefore build upon those papers by introducing an understanding of EE as a multilayer network from the perspective of complexity theory. Building upon this understanding, we provide a representative example to illustrate the practical application of our conceptual model via agent-based modeling while outlining a research agenda that suggests new directions for future studies in this field.

This article examines how design thinking and artificial intelligence (AI) work together and what it means for the design sector. The goal is to understand how AI technologies may advance the design process, encourage innovation, and produce more individualized and user-centric solutions. This study intends to shed light on the potential of AI as a catalyst for creativity and the ethical implications of AI-driven design by discovering overlapping ideas and methodologies between design thinking and AI. According to the research, AI can significantly influence the design process by eliminating tedious processes, improving user-centricity, and stimulating creativity. AI may support designers’ decision-making, prototyping, and ideation processes, resulting in more creative and effective design solutions. Addressing bias in AI algorithms and data privacy is imperative to ensure ethical AI integration. Virtual reality, bio-design, and inclusive design are untapped areas where AI can be used.