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This study aims to investigate the effects of open innovation (OI) and big data analytics (BDA) on reflective knowledge exchange (RKE) within the context of complex collaborative networks. Specifically, it considers the relationships between sourcing knowledge from an external environment, transferring knowledge to an external environment and adopting solutions that are useful to appropriate returns from innovation.

This study analyzes the connection between the number of patent applications and the amount of OI, as well as the association between the number of patent applications and the use of BDA. Data from firms in the 27 European Union countries were retrieved from the Eurostat database for the period 2014–2019 and were investigated using an ordinary least squares regression analysis.

Because of its twofold lens based on both knowledge management and OI, this study sheds light on OI collaboration modes and highlights the crucial role they could play in innovation. In particular, the results suggest that OI collaboration modes have a strong effect on innovation performance, stimulating the search for RKE.

This study furthers a deeper understanding of RKE, which is shown to be an important mechanism that incentivizes firms to increase their efforts in the innovation process. Further, RKE supports firms in taking full advantage of the innovative knowledge they generate within their inter-organizational network.

Institutional venture capitalists (IVCs) and corporate venture capitalists (CVCs) deploy analogous activities but adopt different approaches to financing innovation and value creation for venture-backed firms. Thus, this paper aims to investigate their potential ambidexterity as a result of knowledge management (KM) strategies and processes.

After a focused literature review showing evidence of KM behaviors as a source of potential ambidexterity for IVCs and CVCs, descriptive, inferential and discriminant analyses on the 15 most active IVCs and CVCs in the world in 2019 are presented. Correlations between numbers of deals, prevailing entrepreneurial intensity and potential ambidexterity are investigated.

Specific differences are analyzed from a KM perspective, revealing that the number/percentage of operations per round can result as a misleading criterion of knowledge accumulation. Finally, a theoretical model for ambidexterity for venture capitalists is developed.

The study shows that IVCs act with greater investment capacity because of their organizational structure and purpose and focus on financial goals; moreover, they are ambidextrous, although their exploration may more frequently entail exploitation than “real” exploration. CVCs tend to invest in sectors related to their core business, coherent with their strategic purpose and more oriented with KM strategies for accumulating intellectual capital.

This paper aims to study the effects of knowledge acquisition on innovation performance and the moderating effects of human resource management (HRM), in terms of employee retention and HRM practices, on the above-mentioned relationship.

A sample of 129 firms operating in a wide array of sectors has been used to gather data through a standardized questionnaire for testing the hypotheses through ordinary least squares (OLS) regression models.

The results indicate that knowledge acquisition positively affects innovation performance and that HRM moderates the relationship between knowledge acquisition and innovation performance.

With the increasing proclivity towards engaging in open innovation, firms are likely to face some tensions and opportunities leading to a shift in the management of human resources. This starts from the assumption that the knowledge base of the firm resides in the people who work for the firm and that some HRM factors can influence innovation within firms. Despite this, there is a lack of research investigating the link between knowledge acquisition, HRM and innovation performance under the open innovation lens. This paper intends to fill this gap and nurture future research by assessing whether knowledge acquisition influences innovation performance and whether HRM moderates such a relationship.

Business process management (BPM) supports the creation and capture of firm value. In a dynamic context, the current approach to BPM appears to be limited and static in the face of the challenges posed by the firm's open innovation (OI) ecosystem. The main purpose of this paper is to shed light on the value co-creation through the integration of OI principles and mechanisms of value system.

To this aim, the paper suggested a conceptual integration of strategy and operations literature on OI and the firm's value creation system. This analysis adopted BPM lenses with specific attention to the alignment between value creation and value capture. Applying BPM lenses to the process of creating shared value sought the attainment of a comprehensive system of decisions articulated between strategy and operations.

The paper pinpoints key links between strategy models and operational planning, thus proposing a new framework that integrates the characteristics of value system and OI. The paper elaborates a new theoretical framework rooted in the extant literature conducted in BPM, business strategy and business model innovation (BMI) fields.

This paper aims to fill the gap in the literature in which strategy models are separately treated from the operational ones. This conceptual effort contributes to the extant literature by drawing upon a comprehensive frameworkand mapping the complex set of interactions between the firm's value chain and its innovation ecosystem.

The purpose of this paper is to satisfy a clear gap in the main field of open innovation research whereabouts a very little scholarship try to analyze the mechanisms of innovative milieu down smart cities environments by applying through innovative projects that seem to support efficiently the entry of private firms and citizens in public collaborations.

The research performed an exploratory and qualitative evaluation based on the case study method built on the evaluation of organizational behavior and urban boosting innovation through smart city initiatives. In doing so, after a literature review in smart city as well in lean methodology fields, the case of Turin Smart City follows.

As acknowledged by international literature, the paper shows how a lean approach enables local government to define and realize smart projects and initiatives in a faster and more effective way. Particularly, the government in one of the main cities in Italy, id est Turin, combines a lean methodology with the job-to-be done approach, according a new concept of smart initiatives involving a startup mentality for the lead users which enables interesting predictions relating the human aspects of open collaborations.

The specificity of this inquiry highlights valuable insights from double-gate smart cities’ innovation, social and urban as well. The research is largely interpretative and exploratory and while this provides a solid scientific foundation for further research, it does not, itself, subject any hypothesis to statistical testing and validation.

Since the city approached the smart city subject in a lean way, it was able to realize some projects in a faster way. Through specific initiatives, the city acquires the ability to involve more and better all its stakeholders such as citizens, companies, and public employees, among others. In this regard, the paper invigorates managerial debates concerning the urban and social aspects of open innovation ecosystems which represent in our minds a superior level of open innovation, testbeds of positive knowledge, and stimulus of knowledge dissemination process around the city.

We explore the impact of a specific knowledge management framework on the quality of innovation, the geographic distribution of R&D and the cross-regional integration. We use directly observed indicators of cross-regional knowledge application within the firm as well as examine collaborative mechanisms that firms may use to promote such knowledge translation.

Our analysis is based upon successful patents in biotechnology sector applied for during 2011–2014. The empirical assessment follows a mixed method approach. The sample used for testing the empirical hypotheses is composed of 130,720 patents from 860 large US firms. The sample of patents was obtained from USPTO and NBER dataset.

The idea of this paper was to introduce a model specifically developed for the process of knowledge translation. This research contributes to the literature related to the emergent and new issue namely collaborative knowledge translation (CKT), especially emphasizing the key role of the knowledge translation practices and tools for the internationalization of R&D teams and supporting the quality of innovations in different ways.

This research is conceptually based on the broader concepts of spiral of knowledge of Nonaka and Takeuchi. In spite of the increasing research in innovation, few studies have been done about the diverse contexts' role in the knowledge flows supporting the innovation development. In the attempt to cover this gap, the objective of this research is to answer the following main research question: How to support and manage the process of knowledge translation in innovation processes occurring in collaborative teams?

A paucity of studies has used a microfoundation lens to examine servitization processes in internationalized knowledge-intensive business service (KIBS) companies. The research aims to bridge this gap by considering knowledge sharing as a form of both codified knowledge and informal feedback knowledge; it also assesses whether the adoption of knowledge transfer and translation practices in a servitization process positively moderates the effect of knowledge transformation on knowledge sharing for internationalized KIBS companies.

By adopting a microfoundation lens, the research offers an empirical analysis to identify the relations between codified and tacit knowledge in servitization processes within internationalized KIBS companies. The study is based on 326 respondents from 30 KIBS companies. A multiple regression analysis was used for hypotheses testing.

The authors found significant relations among the use of electronic documents in the servitization process (formal codified knowledge), personal advice in servitization (informal feedback knowledge) and knowledge sharing in internationalized KIBS companies. Findings also support the indirect effect assumed in the hypothesis between knowledge transformation and knowledge sharing in internationalized KIBS companies, which is positively moderated by the adoption of cross-cultural knowledge practices in the servitization process.

To the best of the authors’ knowledge, this research provides the first conceptual model of the use of a microfoundation lens to examine knowledge sharing in internationalized KIBS companies. The micro level features individual knowledge sharing in the servitization process, while the meso level focuses on knowledge transformation in KIBS companies and the adoption of knowledge transfer and translation practices in the servitization process.

The World Health Organisation estimates that 92 per cent of the world’s population does not have access to clean air. The World Bank in 2013 estimated that only air pollution (AP) was responsible for a $225bn cost in lost productivity. The purpose of this paper is to contribute to the current scholarly debate on the value of Big Data for effective healthcare management. Its focus on cardiovascular disease (CVD) in developing countries, a major cause of disability and premature death and a subject of increasing research in recent years, makes this research particularly valuable.

In order to assess the effects of AP on CVD in developing countries, the city of Bangalore was selected as a case study. Bangalore is one of the fastest growing economies in India, representative of the rapidly growing cities in the developing world. Demographic, AP and CVD data sets covering more than 1m historic records were obtained from governmental organisations. The spatial analysis of such data sets allowed visualisation of the correlation between the demographics of the city, the levels of pollution and deaths caused by CVDs, thus informing decision making in several sectors and at different levels.

Although there is increasing concern in councils and other responsible governmental agencies, resources required to monitor and address the challenges of pollution are limited due to the high costs involved. This research shows that with developments in the domains of Big Data, Internet of Things and smart cities, opportunities to monitor pollution result in high volumes of data. Existing technologies for data analytics can empower decision makers and even the public with knowledge on pollution. This paper has demonstrated a methodological approach for the collection and visual representation of Big Data sets allowing for an understanding of the spread of CVDs across the city of Bangalore, enabling different stakeholders to query the data sets and reveal specific statistics of key hotspots where action is required.

This research has been conducted to demonstrate the value of Big Data in generating a strategic knowledge-driven decision-support system to provide focused and targeted interventions for environmental health management. This case study research is based on the use of a geographic information system for the visualisation of a Big Data set collected from Bangalore, a region in India seriously affected by pollution.

By considering the challenges of Industry 5.0, the purpose of this study is to analyze the role of heuristic factors in the technical qualities and emotions of Millennials and Generation Z (Gen Z) to assess their acceptance of the use of artificial intelligence (AI) devices such as robots. For this purpose, this paper uses the innovative AI device use acceptance (AIDUA) framework. This research evaluates the implications of human–machine interactions for the usage of robots and AI in daily life.

The proposed AIDUA model is tested using data collected from Millennials and Gen Z. First, a principal components analysis technique is used to validate each measure. Second, a multiple regression analysis using IBM SPSS 26.0 is conducted.

The results of this study suggest that human–machine interaction is a part of a complex process in which there are different elements determining individuals’ acceptance of the use of AI devices during daily life. This paper outlines both the theoretical and practical implications. This study enriches the AIDUA model by connoting it with features and emotions belonging to the younger generation. Additionally, this research offers technology companies suggestions for addressing future efforts on technical performance and on the alignments of the expectations of young people in Society 5.0.

First, the originality of this paper lies in highlighting the binary role of emotions in triggering the use of AI devices and robots. Second, the focus on Millennials and Gen Z offers a new lens for the interpretation of longitudinal phenomena in the adoption of AI. Finally, the findings of this paper contribute to the development of a new perspective regarding a “heartly collaborative” approach in Society 5.0.