Application of Cognitive Automation to Structuring Data, Driving Existing Business Models, and Creating Value between Legacy Industries

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.

Drivers, Processes, and Outcomes of the STI and DUI Modes of Innovation: A Systematic Review

Two promising streams of research in innovation involve the innovation stemming from the interaction of multiple actors (i.e. innovation networks) and innovation modes [the modes focused on science, technology, and innovation (STI) and learning by doing, using, and interacting (DUI)]. However, scholars have not exerted much effort in cross-referencing these two literature streams. Following a protocol to conduct a systematic review of the literature, through Scimago journals ranking (Q1 and Q2 classification) available at the Web of Science and B-On databases, this study considered 44 articles for eligibility. Moreover, it systematically considers the key features of innovation emerging from DUI networks versus STI networks. Finally, it contributes to future innovation research by comprehensively reviewing the drivers, processes, and outcomes of the STI and DUI innovation emerging from networks.

Effective Recruitment of Engineers From Other Companies: Whether to Pull Individuals or Teams?

This study empirically analyzes effective conditions for cross-border “learning by hiring” in the electronics industry. Many previous studies have indicated that the mobility of engineers serves as a conduit for knowledge diffusion and that knowledge is more likely transferred when the geographical distance is short, that is, when the conduit is short. However, the relationship between conduit thickness and density and the knowledge spillover effect has only rarely discussed. The findings of this study suggest that it is more effective to hire multiple people simultaneously for learning by hiring from companies in other countries.

Mapping the Wave of Industry Digitalization by Co-Word Analysis: An Exploration of Four Disruptive Industries

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.

Profiling Digital Printing Technology Adoption in the Fashion Industry: A New Approach to Exploring Innovation Diffusion

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.

See What I Mean? Analogical Objects for Knowledge Mediation in Early Phases of Cross-Industry Innovation

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.

Raiders of the Lost Ark — A Review About the Roots and Application of Artificial Intelligence

Artificial Intelligence (AI) receives prominent attention within the innovation context. It is the most promising technological invention in information technology. Nevertheless, Innovation and Technology Management (ITM) so far could not structure the AI field, which offers a disruptive innovative potential. Thus, this paper reviews and analyzes the ITM literature and explains the underlying structure of AI. The findings present two main streams of AI literature and, furthermore, explain how to categorize AI use cases. With our results, we assist ITM in explaining and adopting AI to business, which is a huge challenge for companies.

Knowledge Management and Innovation Performance a Mediated-Moderation Model

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.

Critical Factors in the Formation and Development of Technology-Based Firm Networks in Incubation Environments in Brazil

This research aims to identify the critical factors that influence the formation and development of the interorganizational networks that have emerged within incubators of technology-based firms in Brazil. For this purpose, semi-structured interviews were conducted with startups form two networks of incubated companies (Education Network and Technology, Information and Knowledge Companies Network) and two incubators (Center of Innovation, Entrepreneurship and Technology — CIETEC — and Incubator of Technology-Based Companies of Itajubá — INCIT). As a result, this study found eight critical factors: Actions by entrepreneurs, leadership, shared spaces, facilitation, network management, financial and brokerage, which can be framed in four characteristics: heterogeneity of the firms, lack of cooperation, interactions and the actions of the incubator. As a contribution, this research allows reflection on the effectiveness of the incubator, in addition to highlighting the complementarity of networks in the incubation processes. The study analyses different models of incubated firm networks that have been little explored as an object of study in the incubation literature and networks and what is the role of the incubator in each of these models.