Technovation最新文献

Relationships between universities and industry are a source of entrepreneurial activity and innovation. Considering the complexity of university-industry (U–I) collaboration though, the underlying activities require formalisation mechanisms to help overcome ambiguity, mismatched objectives, demands and expectations to ensure joint innovation outcomes. Fairness perceptions have also been found to determine the success of inter-organisational relationships. However, to date fairness has not been explored in stakeholder contexts, such as those prevalent during U–I collaboration. This research extends knowledge on U–I collaboration processes by exploring if formalisation mechanisms increase fairness perceptions and joint innovation outcomes. We adopt a survey methodology to explore the experiences of German professors engaging in U–I collaboration. Our findings identify that formalising U–I collaborations, through mechanisms such as clear procedural guidelines and contracts which agree responsibilities and outcome expectations, leads to enhanced perceptions of fairness and trust. Furthermore, both procedural and distributive fairness were found to increase the positive effect of formalisation on joint innovation outcomes. These findings advance U–I collaboration and strategic alliances literature through identifying the importance of perceived fairness, which is often deemed to be as important as material outcomes and will impact upon join innovation outcomes being achieved. Furthermore, this research proves the positive relationship formalisation has on innovation outcomes.

Collaborations between incumbent and startup firms are hailed for enhancing incumbent innovation capability. Yet, these collaborations often face challenges, notably cognitive distance, highlighting the need to balance potential novelty and mutual understanding between partner firms. Drawing on prior research that indicates a non-linear relationship between cognitive distance and innovation outcomes, we focus on collaboration controls—tools and practices that guide and motivate managers responsible for startup collaborations— serving as critical mechanisms for incumbents to exploit cognitive distance. We hypothesize that the use of collaboration controls mediates the non-linear relationship between cognitive distance and realizing enhanced innovation capabilities. Empirical findings from a survey of 239 French incumbents confirm an inverted U-shaped relation between cognitive distance and enhanced innovative capabilities. Notably, we find that use of collaboration controls mediates this relationship, highlighting the importance of controls in exploiting cognitive distance. This research contributes to unveiling the intricacies of successful collaborations between incumbents and startups by uncovering a critical role for collaboration controls in realizing novelty potential.

Researchers often turn to linear mediation models to understand the complex causal processes inherent within innovation and entrepreneurship phenomena. However, these models are not always the most appropriate methods for increasing our understanding of these phenomena. This is because linear models depend on the principle of reductionism – which separates causal processes into their independent components – and overlooks systemwide attributes. To advance research findings that do not adequately address complex causal processes, we advocate using set-theoretic mediation models that offer analytical features better suited for holistically uncovering interdependent and intervening pathways. This method enables investigating complex causal processes associated with the conjunction, equifinality, and asymmetry that can occur with multiple interdependent variables. We provide researchers with practical guidance on constructing and testing set-theoretic mediation models using widely available software while demonstrating these procedures with an illustrative analysis. In doing so, we seek to guide researchers interested in integrating these models into their studies and recommend best practices for implementation. We argue that set-theoretic meditation models can be utilized in various contexts, as they offer new research opportunities for exploring unified necessity and sufficiency relational systems in ways existing methods have yet to address.

Customer involvement plays a critical role in firms' new product development (NPD), especially in the digital context. Prior research has distinguished between two forms of customer involvement: customers as an information source (CIS) and customers as co-developers (CIC). We build on this stream of literature and investigate the impact of CIS and CIC on firms’ NPD performance in the digital context by considering both the benefits and costs of these two forms of customer involvement. Furthermore, we explore the moderating role of NPD stage. Based on survey data collected from 263 informants, we show that whereas CIS has a positive effect on NPD performance, CIC has an inverted U-shaped relationship with NPD performance. More interestingly, the positive relationship between CIS and NPD performance is more pronounced in the ideation stage than in the product development stage, and the inverted U-shaped relationship between CIC and NPD performance is steeper in the product development stage than that in the ideation stage. In our additional analyses, we further divide the NPD stage into four stages and found similar results as our main analyses. Our study enriches the customer involvement literature and offers practical implications regarding whether, how, and when to involve customers in the NPD process.

User engagement is widely recognised as beneficial for the innovation efforts of firms scarce with internal resources but our knowledge about the relevance of user types and engagement strategies is scarce and mostly limited to the front-end phases of the innovation process. To fill this gap, we use data from over 10,000 companies operating in four emerging innovation ecosystems in Central Europe and examine the contribution of users to product development, distribution, market positioning and the commercialisation of innovations. The results show that user engagement through customisation and co-creation promotes the success of the innovation process, with the impact of co-creation being much stronger. Consumers, intermediary users (businesses) and public sector users play different roles at different stages of innovation. These findings provide important insights for firms seeking to optimise their user engagement strategies in product innovation management.

This paper examines corporate angels, small businesses that provide equity capital directly to unquoted ventures and become minority shareholders. Corporate angels have been largely ignored in entrepreneurial finance research. This novel study examines corporate angels' investment practices, goals and challenges by means of a combination of qualitative cases and a survey of 87 corporate angels. From our analysis, we conclude that due to their knowledge and experience of technology and industry, combined with their active involvement in the investments, corporate angels can be an important source of seed and early-stage financing for technology-based start-ups, particularly with regard to technological and market knowledge and experience. Corporate angels’ investment activity also leads to knowledge inflows to the corporate angel firms in terms of organizational and cultural development of the firms. With this paper we shed light on a new and unexplored investor group that is important in the changing landscape of entrepreneurial finance.

With the development of big data analytics, consumers' online reviews are becoming increasingly useful for product innovation with hidden innovative ideas that can be extracted. However, these ideas may be only hidden in a small part of the massive reviews. This study aims to investigate the potential of using anomaly detection technology to identify unique reviews for more effective innovation generation. Three classical anomaly detection approaches (including both machine and deep learning) were explored, namely, isolation forest, density-based cluster analysis, and autoencoder methods. Using the consumer reviews on Dyson Vacuum cleaner from Xiaohongshu (one of the most popular social media platforms in China), we tested and compared the application of these three approaches in detecting innovation-relevant reviews. The results indicate that the two machine learning approaches, aka., density-based cluster analysis and isolation forest are too sensitive to the length of the reviews. The deep learning method, autoencoder, on the contrary, shows good stability and capability to detect the unique reviews from the whole dataset. Furthermore, the experts’ rating also confirms the effectiveness of autoencoder in identifying innovation-relevant reviews. Future studies and implications are then discussed.

In dynamic market environments, companies look beyond organizational boundaries to pursue competitive advantages. When they collaborate, organizations might develop innovative service offerings and stay relevant in the market over time, enabled by advances in technologies. However, the collaborative dynamics underpinning strategies for continuous service innovation remain underexplored, such that extant research predominantly adopts an organization-centric perspective and fails to offer a comprehensive view of collaborative innovation initiatives that integrate consecutive projects across technology life cycles. To bridge these gaps, the current research applies an action research approach to the supply chain of organizations dedicated to complex technology delivery and maintenance in the maritime industry. The findings specify not just collaborative strategies for continuous service innovation but also the influential factors and operational service attributes crucial to their success. Therefore, this study proposes collaborative strategies for continuous service innovation, with the assertion that collaboration opportunities are integral to developing and managing novel services. Furthermore, this article introduces a technology life cycle perspective to the management of continuous innovation systems, which demand a collective approach to accommodate multiple technology frequencies (e.g., upkeep, update, upgrade), particularly in multi-technology contexts.

This study investigates the consequences of digital transformation (DT) over performance in the case of innovative Small and Medium-sized Enterprises (SMEs). SMEs represent a heterogeneous kind of company in terms of capabilities. By focusing on their innovative character as a boundary condition, we increase our understanding of this relationship. We further research the mediating role of business model innovation (BMI) in the relationship between DT and performance. A sample of 434 innovative Spanish SMEs is used to test the research model through Partial Least Squares Structural Equation Modelling (PLS-SEM). We find that BMI partially mediates the positive relationship between DT and performance. This finding extends our understanding of BMI as an alignment tool and a blueprint for innovative SMEs channeling their investments in DT towards the improvement of performance. Finally, we put forward managerial implications and propose future avenues of research.

The role of big data as a catalyst for the knowledge-based economy is widely recognized, serving as a backbone for powering algorithms. However, prior work has predominantly focused on the supply side of having the “right” datasets or considering data as a “ready-to-use” asset. This approach has resulted in a limited perspective on how to enable value creation from data. There is a need to transition from viewing data as a “ready-to-use” resource to engaging in the co-design of data-based products and services and explore how data-driven practices impact current business practices and organizations. This is why we invite researchers to delve deeper into data-driven design and management practices. The goal is to move ‘beyond data fads’ to better understand the opportunities unveiled by data and explore what management of data-driven organizations entails. This editorial aims to introduce this viewpoint, outline the articles featured in this special issue, and propose directions for future research. The insights garnered from the special issue highlight the multifaceted role of data in enhancing organizational capabilities, spanning from design thinking and disruptive innovation to supply chain management, as well as the importance of innovation intermediaries and ecosystems.