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Eco-innovations (EIs) are intended to benefit not only the environment but society and firms, but how the relationship is reconciled is unclear, particularly in emerging economies. The advancement of EI has resulted in both positive and negative relationships with sustainability, indicating that the association is more complex than a simple linear one.

Thus, the authors hypothesize that EI has a curvilinear relationship with sustainable business performance (SPB) and that market turbulence (MT) exerts stimulus that reinforces EIs. Accordingly, using the Stata software, the authors apply a moderated regression to a sample size data of 511 manufacturing firms to test the hypothesized assumptions.

Although the results attest to a positive relationship between EI and SBP, the results are synonymous with an inverted “U” shape that renders EIs unprofitable beyond a certain threshold (rebound effect). Additionally, the authors observe that the moderation stimulus of technology turbulence flattens the inverted U-shaped curve.

Built on the foundations of natural-resource-based view (NRBV) and contingency theory, the authors identify the rebound effect point of EI and SBP and the reinforcing stimulus of MT.

This study builds on knowledge-based view (KBV) research and the natural resource-based view of the firm (NRBV) to examine the effect of knowledge absorptive capacity on innovative performance. It also investigates the mediating role of three dimensions of the cluster environment: degree of network, institutional environment and access to factors of production.

By means of partial least squares-based structural equation modelling method, the study validated and analysed the responses of 503 owners and managers of small and medium-sized enterprises (SMEs) in the wood industrial cluster of Cote d'Ivoire, a developing economy.

The analysis and results reveal that knowledge absorptive capacity strongly predicts cluster environment and innovation performance. Moreover, the cluster environment emerged as a powerful determinant of innovation performance and a mediator of the effect of knowledge absorptive capacity on innovation performance. Unequivocally, institutional support has no significant impact on knowledge absorptive capacity (KAC) and innovative performance mediation relationship and is not a key determinant of innovative performance.

This study offers a key departure from past studies by linking knowledge absorptive capacity to innovative performance and the cluster environment in SMEs. It also extends knowledge concerning the role of KBV, NRBV and relational theory in innovation performance.

The purpose of this paper is to solve the problem of information privacy and security of social users. Mobile internet and social network are more and more deeply integrated into people’s daily life, especially under the interaction of the fierce development momentum of the Internet of Things and diversified personalized services, more and more private information of social users is exposed to the network environment actively or unintentionally. In addition, a large amount of social network data not only brings more benefits to network application providers, but also provides motivation for malicious attackers. Therefore, under the social network environment, the research on the privacy protection of user information has great theoretical and practical significance.

In this study, based on the social network analysis, combined with the attribute reduction idea of rough set theory, the generalized reduction concept based on multi-level rough set from the perspectives of positive region, information entropy and knowledge granularity of rough set theory were proposed. Furthermore, it was traversed on the basis of the hierarchical compatible granularity space of the original information system and the corresponding attribute values are coarsened. The selected test data sets were tested, and the experimental results were analyzed.

The results showed that the algorithm can guarantee the anonymity requirement of data publishing and improve the effect of classification modeling on anonymous data in social network environment.

In the test and verification of privacy protection algorithm and privacy protection scheme, the efficiency of algorithm and scheme needs to be tested on a larger data scale. However, the data in this study are not enough. In the following research, more data will be used for testing and verification.

In the context of social network, the hierarchical structure of data is introduced into rough set theory as domain knowledge by referring to human granulation cognitive mechanism, and rough set modeling for complex hierarchical data is studied for hierarchical data of decision table. The theoretical research results are applied to hierarchical decision rule mining and k-anonymous privacy protection data mining research, which enriches the connotation of rough set theory and has important theoretical and practical significance for further promoting the application of this theory. In addition, combined the theory of secure multi-party computing and the theory of attribute reduction in rough set, a privacy protection feature selection algorithm for multi-source decision table is proposed, which solves the privacy protection problem of feature selection in distributed environment. It provides a set of effective rough set feature selection method for privacy protection classification mining in distributed environment, which has practical application value for promoting the development of privacy protection data mining.

In this study, the proposed algorithm and scheme can effectively protect the privacy of social network data, ensure the availability of social network graph structure and realize the need of both protection and sharing of user attributes and relational data.

In developing countries, construction organizations are seeking to effectively implement green innovation strategies. Thus, this study aims to assess the importance of green innovation practices and develop a measurement model for quantifying the green innovation degrees of construction firms.

A mixed-methods research approach is adopted. First, an extensive literature review is performed to identify potential green innovation items, which are then used to design a preliminary questionnaire. Next, expert interviews are conducted to pilot-test this questionnaire. Subsequently, by using a convenience non-probability sampling method, 88 valid responses are collected from construction firms in Vietnam. Then, one-sample and independent-samples t tests are employed to assess the importance of green innovation practices. Fuzzy synthetic evaluation (FSE) is also applied to quantitatively compare such practices. Finally, green innovation level (GIL) is proposed to measure the green innovation indexes and validated by a case study of seven construction firms.

This study identifies 13 green innovation variables, of which several key practices are highlighted for small/medium and large construction firms. The results of FSE analysis indicate that green process innovation is the most vital green category in construction firms, followed by green product and management innovations, respectively. As a quantitative measure, GIL could allow construction firms to frequently evaluate their green innovation indexes, thereby promoting green innovation practices comprehensively. Hence, construction firms would significantly enhance green competitive advantages and increasingly contribute to green and sustainable construction developments.

This research is one of the first attempts to integrate various green innovation practices into a comprehensive formulation. The established indexes offer detailed green innovation evaluations, which could be considered as valuable references for construction practitioners. Furthermore, a reliable and practical tool (i.e. GIL) is proposed to measure the GILs of construction firms in developing countries.

The purpose of this study is to address a significant gap in the understanding of entrepreneurship at the microfoundation level. It focuses on how individual entrepreneurs, specifically Hongbang entrepreneurs in China from 1896 to 1949, shape and transform their contexts. The aim is to provide a deeper understanding of the mechanisms that facilitate entrepreneurial success.

The study adopts a microhistorical approach, investigating the case of Hongbang entrepreneurs in China during 1896-1949. It involves an in-depth examination of historical records to explore the strategic interactions between these entrepreneurs and core stakeholders such as consumers, financial intermediaries, government regulators, and human resources. The research methodology emphasizes a process-oriented view, examining the evolution of personalized networks into extensive connections.

The research reveals that Hongbang entrepreneurs successfully reshaped their unfavorable embedded contexts by strategically collaborating with key stakeholders. They influenced consumer tastes, allied with financial intermediaries, negotiated with governments on regulation policies, and developed human resource stocks. The transformation was facilitated by the evolution of their networks from personalized to extensive connections. These findings highlight the localized strategies such as cronyism in resource acquisition within China’s private property development industry.

This study contributes to the field by offering insights into entrepreneurial contextualization and networking. It sheds light on the complex interplay between entrepreneurs and their contexts, providing a nuanced understanding of localized strategies in the Chinese context. The findings add value to the discourse on entrepreneurship by elucidating the strategic and processual acts through which entrepreneurs engage with stakeholders and reshape their environments.

While implementing green innovation-driven strategies when facing growing grim environmental problems and the realistic demands of achieving high-quality development is increasingly urgent, changing abruptly is inevitably detrimental to the smooth functioning of social and economic development. Restrained by resources, innovation-driven strategy is a huge strategy for an organization to shift from traditional technological innovation to green innovation. Supports and implementation in green technology investment would necessarily crowd out other business investment and lead to reduction of innovation outputs and mount of financial uncertainty. Under the guidance of harmonious balance, the equilibrium allocation between green research and non-green counterpart is badly needed to be addressed for decision-makers inside and outside the organizations. The differentiated inputs of them would lead to different effects on organizational performance in practice.

The authors first conducted a Hausman test on green research intensity (GRI) and innovation performance, economic performance, social performance, and environmental performance, respectively. Adopting the fixed effects model for estimation seems accurate, if there is no significant heteroscedasticity shown in the BP test. The authors then adopted the least square dummy variable method to handle individual heterogeneity (Xia et al., 2020). After controlling the industry effect and time effect simultaneously, the results were consistent with that of fixed effects model, thereby eliminating the impact of heteroscedasticity.

The authors construct a multi-dimensional performance system—innovation performance, economic performance, social performance, and environmental performance—to probe into the influence of GRI from the resource-based view and allocation theory. Different performance does not benefit equally from increasing the intensity of green research. Performance increase may squeeze out the quantity of total innovation but can compensate quality for knowledge spillovers of green technology. The organization's growth and long-term value may be beneficial from the increase, but not the short-term financial performance. While the relationship between GRI and social performance has the characteristic of reverse U-curve, there has to be some scale of green research to gain considerable and nonlinear environmental performance. Low level of green research may increase pollution until green research has cross over the inflection point. These relationships are intensely moderated by the environmental regulation.

Because of the focus of this study is on the organizational performance of green research, the analysis comes with some limitations that should be addressed in future research. Data were inter-professional, with large enterprises and small businesses innovating green technology at the same time. Though the hypotheses presented here were grounded in existing theoretical rationale, the generality of this study cannot be assumed. Multi-performance of green activities in small- and medium-sized businesses should be further explored. Additionally, concrete index of the corresponding evaluation system constructed here contribute more to practical activities of green innovation. Refinement of synergy performance index is the task for future work. Further, grounded in Chinese context, the authors' results could be compared with other scenario with institutional heterogeneity to provide detailed evidences for institutional theory. Future studies could also move forward to longitudinal case study to delicately investigate the performance differentiation of green research when in different development stage.

First, what and how the authors do is novel as the authors use listed Chinese manufacturing companies to probe into the complex relationship between GRI and multiple performance rather than discussing the performance of green innovation input from a single perspective merely. Second, the authors systematically define the performance as economic performance, environmental performance, social performance and innovation performance in depth, which consider adequately the tangible and intangible value as well as internal and external benefits of green research. And finally, in the context of environmental regulation, the study discusses the differentiation of the increase of green research intensity from the perspective of resource constraints, providing reference for optimizing the resource allocation in green and non-green research and solving the decoupling between earnest social appeal and sluggish or reluctant green behaviors.