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Collaboration with universities is an important innovation strategy for enterprises. However, currently very little research has focused on how such university-industry collaborative innovation activities should be managed. The paper aims to discuss this issue.

This paper introduces the university-industry collaborative innovation practices of Zhejiang NHU Company in China. By using a case study as the method, this paper aims to illustrate the mechanism of university-industry collaborative innovation and how to manage the collaborative innovation activities efficiently.

Zhejiang NHU Company established a university-industry collaborative innovation link through three innovation platforms: the technology R&D center, the ZJU-NHU joint-research center, and the national engineer center. Zhejiang NHU Company manages its collaborative relationships with universities through this innovation network.

NHU Company managed the collaborative relationship efficiently with the institutions, representing an effective degree of university-industry collaborative innovation management.

Innovation ecosystem is an emerging and popular concept in both academic and industrial circles. It offers a new perspective for enterprise strategy positioning. A business can create more value through a healthy innovation ecosystem. The paper aims to discuss these issues.

In this paper, the authors utilize a new triple-layer core-periphery framework to analyze Insigma Group’s multi-platform collaboration innovation ecosystem, in order to explore the architecture and heterogeneous functions inside an innovation ecosystem.

The authors illustrate the components and working mechanisms of the four platforms, which function as ideation, entrepreneurship, financing and investment, and innovation, inside Insigma’s innovation ecosystem in detail, and explain how they interact and collaborate toward a shared aim of the whole innovation ecosystem.

The innovation ecosystem is an emerging concept. In this study, the authors combined two existing analytical frameworks of innovation ecosystem, and proposed a triple-layer core-periphery framework, which enable us to analyze the heterogeneity inside an innovation ecosystem.

The authors discussed the role of government and its policies in shaping the innovation ecosystem at the enterprise level.

The authors believe that this paper provides a holistic study of Insigma’s innovation ecosystem. The triple-layer core-periphery framework can be used to study other enterprise innovation ecosystem in the future.

Boundary-spanning exploration through establishing alliances is an effective strategy to explore technologies beyond local search in innovating firms. The purpose of this paper is to argue that it is useful to make a distinction in boundary-spanning exploration between what a firm learns from its alliance partners (explorative learning from partners (ELP)) and what it learns from other organisations (explorative learning from non-partners (ELN)).

The authors contend that alliances play a role in both types of exploration. More specifically, the authors discern three types of alliances (inside ties, clique-spanning ties and outside ties) based on their role vis-à-vis existing alliance cliques. Clique members are highly embedded, and breaking out of the cliques through clique-spanning and outside alliances is crucial to improving explorative learning. Thereafter, the authors claim that clique-spanning ties and outside ties have a different effect on ELN and ELP.

The empirical analysis of the “application specific integrated circuits” industry indicates that inside ties have negligible effects on both types of explorative learning. Clique-spanning ties have a positive effect on ELP, but not on ELN. The reverse is true for outside ties. The results show that research on explorative learning should devote greater attention to the various roles alliance partners and types of alliances play in advancing technological exploration.

The literature only emphasises the learning from partners, focussing mainly on accessing their technology. In sum, alliance partners play different roles in exploration, and their network position influences the role they are able to play.

The purpose of this research is to survey whether consumer ethnocentrism and animosity will affect consumers' perceived betrayal and cause negative word-of-mouth (NWOM).

This study conducted a 2 (consumer ethnocentrism) × 3 (consumer animosity) between-subject experiment design to test the hypotheses. Comprised of 380 respondents, this study used ANOVA to examine the data.

The results showed that if a brand violates the perception of fairness, ethnocentrism and animosity will have a positive effect on perceived betrayal. In addition, low consumer animosity revealed a significant consumer ethnocentrism effect and low ethnocentrism revealed a significant animosity effect, while the relationship between perceived betrayal and word of mouth is negative.

The current research adds to the understanding about how the reaction to a domestic brand's marketing strategies that are viewed as unfair and hurt the domestic consumers' expectations.

This study aims to investigate whether intellectual capital (IC) and its subcomponents enhance value and improve the profitability of real estate (RE) and infrastructure (INF) firms in India. In this study, IC is measured through the value-added intellectual coefficient (VAIC) model. The study further extends the VAIC model by incorporating an additional component of social welfare efficiency (SWE).

The study uses the panel data investigation based on the data of 63 firms (22 RE and 41 INF firms), for a period of 10 years (2008–2017). The dependent variables in the study are return on assets (ROA) and market price to book value ratio (PB), whereas the independent variables are VAIC and its components. The panel is tested for stationarity, heteroscedasticity and multicollinearity problems. Finally, to account for heteroscedasticity and endogeneity, Arellano and Bond's (1991) panel regression estimator with robust estimates are used.

The findings of the study suggest that IC has a significant influence on the profitability and value of infra firms, whereas capital-employed efficiency (CEE) positively affects the profitability of both RE and INF firms.

The study is an attempt to find the effect of IC and its components on profitability and value of RE and INF firms in India. The author has also extended the VAIC model, which was introduced by Pulic (2000), by adding an additional IC component, i.e. SWE. The study uses Arellano and Bond's (1991) panel regression estimator with robust estimates, which helps produce robust results.