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This study analyses the link between product/service innovation, partnerships and Managerial Control System (MCS). Particularly, it aims to analyse empirically the role of MCS in supporting the innovation partnership successful functioning and management.

The sample of this study consists of 106 Italian manufacturing firms belonging to the sectors of the Italian economy with the largest number of registered patents according to the European trend chart on innovation.

The results show that MCS may play a key role in reducing risks and lowering the likelihood of failure of innovation partnerships. Particularly, the authors found a positive correlation between the use of informal control mechanisms and a partnership’s successful performance. Moreover, among informal control, the findings show that trust is the only true informal mechanism that can guarantee a successful collaboration. The results of this study may offer relevant implications for practitioners. With regard to the control of the partnership’s activities, the initiatives and creativity of those who are actively involved in the innovation process should not be inhibited; therefore, stifling them with strict rules and procedures would be ineffective but if a firm is not willing to give up formal control mechanisms altogether because it does not believe that a trust-based coordination is sufficiently reassuring, it should opt for “weak”, albeit formal, control mechanisms based on a shared production and management of plans and reports, thus ensuring a perfect information symmetry among different partners.

Notwithstanding the different opportunities provided by partnerships and strategic alliances to support there is a growing body of evidence of a high failure rate in such organisational forms. One of the causes cited in the literature is the high level of risk associated with alliances as compared to internal development of innovation. The risks mainly arise from the difficulties to obtain cooperation with partners that might have different objectives, and from the potential opportunistic behaviour of some of the partners. This is particularly true in innovation networks where the uncertainty of producing an interesting result is very high and the investments that the partners make are considerable. In this context, MCS could play a relevant role in reducing the risks and decreasing the likelihood of failure.

In recent years, environmental issues and resource depletion have posed significant challenges to firms and society. To address these environmental challenges, firms seek to build strategic alliances of green supply chain management (GSCM) with their supply chain partner. As the largest developing country in the Asia–Pacific region, China needs to take more responsibility for environmental protection, which requires more Chinese firms to participate in GSCM. Therefore, focusing on the issue of GSCM and innovation persistence in the context of an increasingly harsh ecological environment is essential.

To test the hypothesis, the authors perform an empirical analysis on a sample of 124 listed firms in China from 2014 to 2019. The results are robust to a battery of robustness analyses the authors performed to take care of endogeneity.

Empirical results indicate that GSCM can promote innovation persistence and both market environment turbulence and technology environment turbulence have a positive moderating effect on the relationship between the two. Mechanism tests show that GSCM can improve innovation efficiency, ensure innovation quality and alleviate financing constraints, thus promoting the innovation persistence of firms.

This study can provide a theoretical basis for the country to promote GSCM orientation, raise firms' awareness of the value of GSCM, convey the significance of GSCM to investors, influence firms' investment decisions and give experience to other developing countries.

The main purpose of this paper is to explore whether the nature of innovation is accumulative or radical and to what extent past year accumulation of technology stock can predict future innovation. More importantly, the authors are concerned with whether a change of policy regime or a variance in the quality of technology will moderate the nature of innovation.

The authors examined a dataset of 3.6 million Chinese patents during 1985–2015 and constructed more than 5 million citation pairs across 8 sections and 128 classes to track knowledge spillover across technology fields. The authors used this citation dataset to calculate the technology innovation network. The authors constructed a measure of upstream invention, interacting the pre-existing technology innovation network with historical patent growth in each technology field, and estimated measure's impact on future innovation since 2005. The authors also constructed three sets of metrics – technology dependence, centrality and scientific value – to identify innovation quality and a policy dummy to consider the impact of policy on innovation.

Innovation growth is built upon past year accumulation and technology spillover. Innovation grows faster for technologies that are more central and grows more slowly for more valuable technologies. A pro-innovation and pro-intellectual property right (IPR) policy plays a positive and significant role in driving technical progress. The authors also found that for technologies that have faster access to new information or larger power to control knowledge flow, the upstream and downstream innovation linkage is stronger. However, this linkage is weaker for technologies that are more novel or general. On most occasions, the nature of innovation was less responsive to policy shock.

This paper contributes to the debate on the nature of innovation by determining whether upstream innovation has strong predictive power on future innovation. The authors develop the assumption used in the technology spillover literature by considering a time-variant, directional and asymmetric matrix to model technology diffusion. For the first time, the authors answer how the nature of innovation will vary depending on the technology network configurations and policy environment. In addition to contributing to the academic debate, the authors' study has important implications for economic growth and industrial or innovation management policies.

With the evolution of the turbulent environment constantly triggering the emergence of a trust crisis between organizations, how can university–industry (U–I) alliances respond to the trust crisis when conducting green technology innovation (GTI) activities? This paper aims to address this issue.

The authors examined the process of trust crisis damage, including trust first suffering instantaneous impair as well as subsequently indirectly affecting GTI level, and ultimately hurting the profitability of green innovations. In this paper, a piecewise deterministic dynamic model is deployed to portray the trust and the GTI levels in GTI activities of U–I alliances.

The authors analyze the equilibrium results under decentralized and centralized decision-making modes to obtain the following conclusions: Trust levels are affected by a combination of hazard and damage (short and long term) rates, shifting from steady growth to decline in the presence of low hazard and damage rates. However, the GTI level has been growing steadily. It is essential to consider factors such as the hazard rate, the damage rate in the short and long terms, and the change in marginal profit in determining whether to pursue an efficiency- or recovery-friendly strategy in the face of a trust crisis. The authors found that two approaches can mitigate trust crisis losses: implementing a centralized decision-making mode (i.e. shared governance) and reducing pre-crisis trust-building investments. This study offers several insights for businesses and academics to respond to a trust crisis.

The present research can be extended in several directions. Instead of distinguishing attribution of trust crisis, the authors use hazard rate, short- and long-term damage rates and change in marginal profitability to distinguish the scale of trust crises. Future scholars can further add an attribution approach to enrich the classification of trust crises. Moreover, the authors only consider trust crises because of unexpected events in a turbulent environment; in fact, a trust crisis may also be a plateauing process, yet the authors do not study this situation.

First, the authors explore what factors affect the level of trust and the level of GTI when a trust crisis occurs. Second, the authors provide guidelines on how businesses and academics can coordinate their trust-building and GTI efforts when faced with a trust crisis in a turbulent environment.

First, the interaction between psychology and innovation management is explored in this paper. Although empirical studies have shown that trust in U–I alliances is related to innovation performance, and scholars have developed differential game models to portray the GTI process, building a differential game model to explore such an interaction is still scarce. Second, the authors incorporate inter-organizational trust level into the GTI level in university–industry collaboration, applying differential equations to portray the trust building and GTI processes, respectively, to reveal the importance of trust in CTI activities. Third, the authors establish a piecewise deterministic dynamic game model wherein the impact of crisis shocks is not equal to zero, which is inconsistent with most previous studies of Brownian motion.

Interorganizational power dependence has become an increasingly important factor for small and medium-sized enterprises (SMEs) to improve product innovation. This paper examines the role of power dependence in SMEs' product innovation trade-offs between exploration and exploitation. The article further studies the mediating effect of supply chain adaptability and the moderating effect of knowledge acquisition on the relationship between power dependence and product innovation.

The study proposes a model to verify the impact of power dependence on SMEs' product innovation trade-offs based on social network theory. Two conceptually independent constructs, “availability of alternatives (ALTRN)” and “restraint in the use of power (RSPTW),” are used to evaluate the power dependence. The model also analyzed how these effects are mediated by supply chain adaptability and moderated by knowledge acquisition. The authors test these relationships using data collected from 224 SMEs in China.

The empirical analysis shows that ALTRN has a more substantial effect on exploration for product innovation, while RSTPW has a more significant impact on exploitation for product innovation. Moreover, empirical data indicate a partial mediating effect by supply chain adaptability between power dependence and product innovation of SMEs. The results also show that knowledge acquisition positively moderates the relationship between ALTRN/RSTPW, supply chain adaptability and product innovation.

Overall, the findings of the study advance the understanding of the roles of power dependence in product innovation for SMEs. In addition, the research also uncovers the impact mechanisms of existing theoretical frameworks and extends the boundaries of the theory.

The COVID-19 pandemic has significantly affected service small- and medium-sized enterprises (SMEs), increasing the importance of understanding how these businesses can become more resilient and how service innovation can be an effective strategy to increase their adaptive capacity and survival. This study aims to examine the role of dynamic capabilities in service innovation as a factor explaining the resilience of SMEs in Puerto Rico and the Dominican Republic during the COVID-19 crisis and its impact on service innovation. Additionally, the authors assess whether service innovation has a significant impact on value cocreation in these businesses.

This study used a quantitative method by surveying 118 SME owners in Puerto Rico and the Dominican Republic. The data were analyzed using partial least-squares structural equation modeling.

The results reflect important theoretical contributions by analyzing resilience from an innovation perspective instead of a retrospective approach, which is an area that has not been analyzed in the literature. Additionally, theoretical contributions to marketing services in SMEs are discussed, which is an underresearched topic. The results advance by discussing the role of service innovation through the reconfiguration of resources and how this can be an effective strategy to increase value cocreation with customers during crises.

This study is original in that it analyzes resilience from the perspective of innovation, and not from a retrospective approach. It offers a vision in response to the need for studies that provide a clearer conceptualization of resilience in small businesses. This highlights the importance of considering regional differences and service innovation as effective strategies to enhance resilience and value cocreation with customers.

This research studies the development of the evolving dynamic system model and explores the important elements or factors and what detailed attributes are the main influences model in achieving the success of a business, industry and management. It also identifies the real and major differences between static and dynamic business management models and the detailed factors that influence them. Later, this research investigates the benefits/advantages and limitations/disadvantages of some research studies. The studies conducted in this research put more emphasis on the capabilities of system dynamics (SD) in modeling and the ability to measure, analyse and capture problems in business, industry, manufacturing etc.

The research presented in this work is a qualitative research based on a literature review. Publicly available research publications and reports have been used to create a research foundation, identify the research gaps and develop new analyses from the comparative studies. As the literature review progressed, the scope of the literature search was further narrowed down to the development of SD models. Often, references to certain selected literature have been examined to find other relevant literature. To do so, a supporting tool (that connects related articles) provided by Google Scholar, Scopus, and particular journals has been used.

The dynamic business and management model is very different from the static business model in complexity, formality, flexibility, capturing, relationships, advantages, innovation model, new goals, updated information, perspective and problem-solving abilities. The initial approach of a static system was applied in the canvas business model, but further developments can be continued with a dynamic system approach.

Based on this study, which shows that businesses are developing more towards digitalisation, wanting the ability to keep up with the era that is moving so fast and the desire to increase profits, an instrument is needed that can help describe the difficulties of the needs and developments of the future world. This instrument, or tool of SD, is also expected to assist in drawing future models and in building a business with complex variables that can be predicted from the beginning.

This study will contribute to the SD study for many business incubator research studies. Many practical in business incubator management to have a benefit how to achieve the business performance management (BPM) in SD review.

The significant differences between static and dynamics to be used for business research and strategic performance management. This comparative study analyses some SD models from many authors worldwide. Their goals behind their strategic business models and encounter for their respective progress.

The purpose of this study is to investigate whether and how inter-firm cooperation and firm age moderate the relationship between family ownership and productivity.

We first estimate the total factor productivity (TFP) of a large sample of Italian firms observed over the period 2010–2018 and then apply a Poisson random effects model.

TFP is, on average, higher for non-family firms (non-FFs) than for FF. Furthermore, inter-organizational cooperation and firm age mitigate the negative effect of family ownership. In detail, it is found that belonging to a network acts as a moderator in different ways according to firm age. Indeed, young FFs underperform non-FF peers, although the TFP gap decreases with age. In contrast, the benefits of a formal network are high for older FFs, suggesting that an age-related learning process is at work.

The study provides evidence that FFs can outperform non-FFs when they move away from Socio-Emotional Wealth-centered reference points and exploit knowledge flows arising from high levels of social capital. In the case of mature FFs, networking is a driver of TFP, allowing them to acquire external resources. Since FFs often do not have sufficient in-house knowledge and resources, they must be aware of the value of business cooperation. While preserving the familiar identity of small companies, networks grant FFs the competitive and scale advantages of being large.

Despite the wide but ambiguous body of research on the performance gap between FFs and non-FFs, little is known about the role of FFs’ heterogeneity. This study has proven successful in detecting age as a factor in heterogeneity, specifically to explain the network effect on the link between ownership and TFP. Based on a representative sample, the study provides a solid framework for FFs, policymakers and academic research on family-owned companies.

Open innovation (OI), by now one of the major concepts for the analysis of innovation, is seen as a methodology for collaboratively designing and implementing solutions by engaging stakeholders in an iterative and inclusive service design process. This paper aims to empirically investigate OI capacities, defined as a cooperative, knowledge-sharing innovation ecosystem, and to explore how it can lead to improved performance of firms in Central and Eastern European (CEE) and Southeastern European (SEE) countries.

The study builds on the World Bank/European Bank for Reconstruction and Development (EBRD’s) Business Environment Enterprise Performance Survey (BEEPS) dataset for 2009, 2013 and 2019. Primarily, the research model was estimated using log-transformed ordinary least squares (OLS). Taking into consideration that this method might produce substantial bias, yielding misleading inferences, this study is fitting Poisson pseudo maximum likelihood estimators with robust standard errors and instrumental variable/generalized method of moments estimation (IV/GMM) approach for comparative results. Secondarily, the research model was tested using structural equation modelling (SEM) to investigate the relationship between five OI capacities and firm performance.

The findings indicate that there is a significant positive relationship between most OI capacities and firm performance, except for innovation, which did not show a statistically significant relationship with firm performance. Specifically, research and development (R&D), knowledge and coopetition are statistically significant and positively associated with firm performance, whereas transformation is statistically significant but negatively associated with firm performance. The IV/GMM estimations’ findings support the view that the firm performance is significantly affected by OI capacities, together with some control variables such as size, age, foreign ownership and year dummy to have a significant impact on firm performance.

This paper fills an identified gap in the literature by investigating the impact of OI on firm performance executed in the specific CEE and SEE country context.

The purpose of this study is to describe latent classes explaining the innovation logic in the Finnish construction companies. Innovativeness is a driver of competitive performance and vital to the long-term success of any organisation and company.

Using finite mixture structural equation modelling (FMSEM), the authors have classified innovation logic into latent classes. The method analyses and recognises classes for companies that have similar logic in innovation activities based on the collected data.

Through FMSEM analysis, the authors have identified three latent classes that explain the innovation logic in the Finnish construction companies – LC1: the internal innovators; LC2: the non-innovation-oriented introverts; and LC3: the innovation-oriented extroverts. These three latent classes clearly capture the perceptions within the industry as well as the different characteristics and variables.

The presented latent classes explain innovation logic but is limited to analysing Finnish companies. Also, the research is quantitative by nature and does not increase the understanding in the same manner as qualitative research might capture on more specific aspects.

This paper presents starting points for construction industry companies to intensify innovation activities. It may also indicate more fundamental changes for the structure of construction industry organisations, especially by enabling innovation friendly culture.

This study describes innovation logic in Finnish construction companies through three models (LC1–LC3) by using quantitative data analysed with the FMSEM method. The fundamental innovation challenges in the Finnish construction companies are clarified via the identified latent classes.