International Journal of Innovation and Technology Management

The objective of this research is to explore the nature and role of analogies as objects for knowledge transfer in cross-industry collaborations. A case study of an organization seeking cross-industry innovation (CII) across two industry sectors was conducted, and the empirical data were analyzed qualitatively. We found that analogies used as knowledge mediation objects could be classified as explanatory or inventive, each expressed as linguistic or visual representations. Explanatory analogical objects help build prior knowledge of a foreign industry domain, thus easing later use of inventive analogical objects to identify how knowledge from one industry can be applied in another industry for innovation purposes. In these roles, the analogies serve as boundary objects. Both explanatory and inventive analogies can also serve as epistemic objects, motivating for further collaborative engagement. Visual representations of analogies help bridge the abstract with the concrete, thereby easing the process of creating analogies. They also enable nonverbal communication, thus helping bypass language barriers between knowledge domains. The reported research expands current research literature on knowledge mediation objects to the context of CII and provides added detailed understanding of the use of analogies in CII.

The paper deals with the analysis of the resources and energy consumption at different levels of economic management. The most effective management tools are characterized. The quantitative and qualitative analysis of the use of different types of resources by agri-food enterprises is carried out. Using the results of the analysis, a model that takes into account the features of general and specific for agri-food enterprises’ types of resources is proposed. The practical significance of this model lies in accumulating the most effective resource-saving and energy-saving technologies of renewable and non-renewable resources of agro-sphere (according to experience in application by different countries).

This paper aims to identify global digital trends across industries and to map emerging business areas by co-word analysis. As the industrial landscape has become complex and dynamic due to the rapid pace of technological changes and digital transformation, identifying industrial trends can be critical for strategic planning and investment policy at the firm and regional level. For this purpose, the paper examines industry and technology profiles of top startups across four industries (i.e. education, finance, healthcare, manufacturing) using CrunchBase metadata for the period 2016–2018 and studies in which subsector early-stage firms bring digital technologies on a global level. In particular, we apply word co-occurrence analysis to reveal which subindustry and digital technology keywords/keyphrases appear together in startup company classification. We also use network analysis to visualize industry structure and to identify digitalization trends across sectors. The results obtained from the analysis show that gamification and personalization are emerging trends in the education sector. In the finance industry, digital technologies penetrate in a wide set of services such as financial transactions, payments, insurance, venture capital, stock exchange, asset and risk management. Moreover, the data analyses indicate that health diagnostics and elderly care areas are at the forefront of the healthcare industry digitalization. In the manufacturing sector, startup companies focus on automating industrial processes and creating smart interconnected manufacturing. Finally, we discuss the implications of the study for strategic planning and management.

This study aims at explaining innovation performance of organizations as a combination of resources and capabilities. This study starts with asking the question how the relationship between firm-specific knowledge and innovation performance can be explained. To answer this question, insights from the resource-based view (RBV) and the dynamic capabilities approach (DCA) are combined. This leads to a set of hypotheses. The first hypothesis states that knowledge-specificity and innovation performance are positively related. The second hypothesis states that organizational learning practices mediate the relationship between knowledge-specificity and innovation performance. Then, two contrasting hypotheses are formulated stating that the relationship between knowledge-specificity and organizational learning practices of organizations is strengthened or weakened by the level of autonomy. Together these hypotheses lead to a mediated-moderation model of knowledge-specificity and innovation performance. The model is tested using a mediated-moderation analysis on a sample of 673 private organizations in the Netherlands. The analyses show that there is a positive relationship between knowledge-specificity and innovation performance and that this relationship is mediated by the extent to which organizations apply learning practices. Hypotheses 1 and 2 are thus supported. Furthermore, the level of autonomy weakens the relationship between knowledge-specificity and organizational learning practices. This study’s main contribution lies in combining theoretical insights from the RBV and the DCA, applying them to the field of knowledge management, and testing them empirically. The analyses lead to two insights for organizations interested in increasing their innovation performance. First, investing in learning capabilities enhances innovation performance. Second, organizations based on general knowledge can grant work autonomy to employees to enhance their ability to learn.

To build high quality datasets and unlock the value of unstructured data, a systematic approach for data capture is necessary. Cognitive automation (CA), that is, automation of processes with artificial intelligence (AI), enables the information extraction from unstructured data to provide relevant insights and further processing with AI. This study provides an overview of this new technology and shows how it can be used to transform existing business models. Our case studies in the insurance auditing, healthcare, and banking industries show the potential managerial impact of CA, which prepares these legacy industries for their digital future’s challenges and opportunities. We present the novel data extraction pipeline for textual and visual data and demonstrate its efficiency in extracting information from the company’s unstructured data. We show its performance in quality, cost, and time compared with current industry standards and provide management insights for business applications using CA.

Digital printing technology (DPT) represents a core innovation that is currently revolutionizing the global decorated apparel market by automating the printing process, facilitating customization, and reducing energy costs and production lead time. However, the fundamental understanding of the emerging DPT market remains unexplored due to its novelty. This study aims to identify DPT diffusion patterns over the past decade in the U.S. market and establish a predictive user profile employing social media-based analytics along with data mining and traditional statistical modeling. A proxy variable is used to measure likely adoption which reflects an S-shaped diffusion curve consistent with Diffusion of Innovations Theory. Additionally, the outcome profile suggests that likely DPT adopters reside in locations that reflect higher levels of education (bachelor’s degrees or higher), relatively young populations (i.e. between 19–34 years of age), proportionately higher incomes generated from art and design occupations, but with lower household annual incomes.

The purpose of this paper is to study the effect of performance-based rewards on radical innovation. In doing so, it explores the role of knowledge acquisition and innovative work behavior (IWB) as possible mechanisms that underlie the relationship between performance-based rewards and radical innovation. The subsequent effect of IWB on promoting radical innovation is explored. The study employs data from 235 unit managers in manufacturing and technology firms in Jordan. Structural equation modeling was used to test for reliability, validity, and the hypothesized relationships. The results indicate that performance-based rewards positively affect knowledge acquisition. In addition, knowledge acquisition mediates the link between performance-based rewards and IWB. IWB is also significant and positive in promoting radical innovation. This paper is one of the first to examine how radical innovation can be promoted through IWB. It offers insights into and contributions to the literature on the role of performance-based rewards in driving radical innovation through the mediating effect of knowledge acquisition and the subsequent impact of IWB on radical innovation.

In innovation management (IM), two perspectives typically come together, characterized with the term ambidexterity: a clear orientation towards the best possible use of resources (exploitation) and, at the same time, completely new ideas and novel approaches (exploration). We consider contextual ambidexterity suitable to address this dilemma in small- and medium-sized enterprises (SMEs). Based on findings from five German SMEs, we argue that training and using ‘conventional’ IM for exploitation and ‘agile’ IM for exploration could be most feasible. We develop a generic conceptual model with high practical relevance for SMEs and their innovation projects.