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Small and medium-sized enterprises (SMEs) are currently showing an increasingly open innovation (OI) approach. Public policies supporting the adoption of OI by SMEs are becoming a priority for policymakers. Therefore, the aim of this article is to contribute to the literature by mapping scholars' policy recommendations for implementing OI among SMEs.

The authors conducted a systematic review of the literature (SRL) on the topic to achieve this purpose. A total of 99 academic articles were selected from the Web of Science and Scopus databases to suggest the main scholars' policy recommendations to implement OI among SMEs.

Results indicated that scholars' policy recommendations for OI adoption in SMEs can be organized into: research and development (R&D), networking, collaboration, knowledge and intellectual property rights (IPR), ecosystem, managerial capabilities, funding and incentives and sustainability policies.

Only relevant articles about this topic have been included due to the reliance on the interpretations of the authors. The analysis of the literature revealed that the authors did not always distinguish policies dedicated to SMEs and those dedicated to large companies. Moreover, policies are not matched according to each OI dimensions (e.g. inbound, outbound and coupled OI).

The article uses a systematic literature review method that combines qualitative and quantitative analyses. This method contributes to theoretical development of OI policies dedicated, in particular to SMEs. This paper also provides policymakers and researchers with insights on the scope of OI policies that could support economic growth.

The purpose of this paper is to draw on recent developments in the open innovation literature to explore whether the openness of SMEs to the four categories of external sources of information (ESI) is complementary, substitute or independent, while assessing the determinants of SMEs’ openness to these ESI.

This research is based on data from a survey of 451 manufacturing SMEs in the province of Québec, Canada. Data have been elaborated through a multivariate probit model to empirically show that SMEs are considered to be simultaneously open to different ESI. The results of this study show significant heterogeneity in the determinants of SMEs’ openness to these ESI.

The study found that the SMEs’ openness to different ESI seems to be complementary rather than substitute; and not all variables included in the model explain the SMEs’ openness to the different ESI.

The paper provides practical implications for managers and policy makers including the SMEs’ managers’ role to recognize the consolidation of different ESI jointly instead of separately. Furthermore, managers and policy makers should attempt to provide a fair context to SMEs to manage their openness ecosystem.

This study is virtually the first to investigate both the complementarity and the determinants of SMEs’ openness to different ESI using a sophisticated econometric model.

This study aims to examine the present condition of blockchain technology (BT) applications in auditing by analyzing journal publications on the topic to acquire a better understanding of the field.

This study makes use of the Bibliometric Analysis method and gathered 725 papers from the Web of Science and Scopus databases in the management and accounting, business, financial, economic and social science, as well as decision sciences fields from 2017 to 2021 using the R-Package Bibliometrix Analysis “biblioshiny”.

The findings revealed that blockchain research in terms of auditing has already increased and started to spark a quick rise in popularity, but is still in its initial phases with important quality though less in quantity. Moreover, the Journal of Emerging Technologies in Accounting is the most prolific journal with 2019 as the highest publication year, with the United States and China as the most cited countries in this field. Furthermore, in this field, there are much research topics involving blockchain, audit and smart contracts; and there is less involving data analytics, governance, hyperledger, distributed ledger and financial reporting. Additionally, Sheldon (2019) and Smith and Castonguay (2020) are the most productive authors in the field in terms of the H-index.

This study has certain limitations such as the fact that it only looked at 105 papers in the domains of finance, business, economics, accounting, management as well as multidisciplinary science. Moreover, the research’s data and dates have an impact on the results dependability. As this is an original topic, fresh studies are anticipated to remain to shine a spotlight on and suggest answers to blockchain’s implications on auditing. Additionally, the period of time was limited to only the last five years, from 2017 to 2021. As a result, extensive study into the topic is required since there is currently a research deficit in the blockchain field in the setting of auditing. So, new research is required to offer new frameworks and understandings for describing the blockchain function in auditing, including processes, techniques, security, as well as timeliness. Investigations in unique circumstances and research employing innovative research methodologies for discovering the new issue would be valuable in acquiring a higher grasp of the complexities faced.

This research contributed to the field by assessing the present state of the art of research on the usage and use of BT in finding research gaps, the audit profession and, most importantly, recommending a future direction for researchers in the subject.

This paper aims to design, build and evaluate a blockchain platform in the accounting domain, taking an ecosystem perspective. To achieve this aim, the research provides evidence for developing a decentralised architecture rooted on blockchain technology, designing a proof of concept and modelling an accounting blockchain-based system.

Moving from the analysis of previous literature and leveraging on the design science approach, this paper provides a framework grounded on the main pillars of blockchain and accounting functions, identifying technical and non-technical issues that must be addressed embrace blockchain technology's full potential.

We propose and discuss a conceptual framework for a blockchain-based accounting context, moving from the identification of a typical accounting scenario. The framework is organised around three scalable levels: the first level is a technological infrastructure based on a distributed database with peer-to-peer storage; second, in the intermediate level, increasing control levels are assured through permissions and validation and third, in the higher level, the system provides the integration of business and security applications. The deployment of this system relies on a private network of nodes that validates transactions.

The proposed conceptual framework about blockchain development in accounting allows closing the knowledge gap between blockchain developers and accounting experts by suggesting technological and strategic issues for practitioners.

We provide practical guidelines to design and adopt blockchain in the accounting domain.

Energy conservation and environmental protection industry (ECEPI) is a strategic choice to promote energy conservation and emission reduction, develop green economy and circular economy. However, China’s ECEPI is still in the stage of rapid development and the overall scale is relatively small, what development periods have the ECEPI experienced? This study aims to contribute to a better understanding of collaborative innovation evolution based on social network analysis from the perspective of multi-dimensional proximity.

Methodologically, this study uses social network analysis method to explore the co-evolution of multidimensional collaboration networks. It divides China’s ECEPI into four periods based on national policies from 2001 to 2020. This contribution constructs collaborative innovation networks from geographical, technological and organizational proximity.

The results show that the collaborative innovation network was initially formed in the central region of China, gradually expanded to neighboring cities and the core positions of Beijing, Jiangsu and Guangdong have been continuously consolidated. C02F has been the core of the collaboration networks, and the research focus has gradually shifted from the treatment of wastewater, sewage or sludge to the separation field. Enterprises always occupy a dominant position in the collaboration networks.

This research investigates the dynamic evolution process of collaborative innovation network in China’s ECEPI from the perspective of multidimensional proximity, explores the community structure, important nodes and multidimensional proximity features in the network, expands the research perspective on evolution characteristics of innovative network and the research field of social network analysis. Theoretically, this study enriches collaborative innovation theory, social network theory and multi-dimensional proximity theory.

Know your customer (KYC), accounting standards, issuance, clearing, and trade settlement became the major barrier to implement accounting, accountability and assurance process in supply chain finance (SCF). Blockchain technology features have the potential to solve accounting problems. This research focuses on exploring how blockchain technology provides solutions to overcome the barriers of accounting process in SCF. The benefits, opportunities, costs and risks related to blockchain adoption are also explored.

Multi-case study and qualitative methods are used with a framework based on blockchain role to overcome the accounting process barriers. Ten blockchain projects in SCF and 29 interviews of participants as a unit of analysis are considered.

The findings indicate that blockchain technology offers solutions to solve accounting, accountability and assurance problems in SCF. Validity, verification, smart contracts, automation and enduring data on trade transactions potentially solve those barriers. However, it is also necessary to consider costs such as implementation, technology, education and integration costs. Then there are possible risks such as regulatory compliance, operational, code development and scalability risk. This finding reflects the current status of blockchain technology roles in SCF.

This study unveils blockchain's SCF accounting potential, emphasizing multi-case method limitations and future research prospects. Diverse contexts challenge findings' applicability, warranting cross-industry studies for deeper insights. Addressing selection bias and integrating quantitative measures can enhance understanding of blockchain's accounting impact.

Accounting professionals can get an idea of the future direction and impact of blockchain technology on accounting, accountability and assurance processes.

This study provides initial findings on the potential, costs and risks of blockchain that is beneficial for parties involved in SCF, especially for banks and insurance underwriters. In addition, the findings also provide direction for the contribution of blockchain technology to accounting theory in the future.

The unstable dynamic propagation of multistage hydrofracturing fractures leads to uneven development of the fracture network and research on the mechanism controlling this phenomenon indicates that the stress shadow effects around the fractures are the main mechanism causing this behaviour. Further studies and simulations of the stress shadow effects are necessary to understand the controlling mechanism and evaluate the fracturing effect.

In the process of stress-dependent unstable dynamic propagation of fractures, there are both continuous stress fields and discontinuous fractures; therefore, in order to study the stress-dependent unstable dynamic propagation of multistage fracture networks, a series of continuum-discontinuum numerical methods and models are reviewed, including the well-developed extended finite element method, displacement discontinuity method, boundary element method and finite element-discrete element method.

The superposition of the surrounding stress field during fracture propagation causes different degrees of stress shadow effects between fractures and the main controlling factors of stress shadow effects are fracture initiation sequence, perforation cluster spacing and well spacing. The perforation cluster spacing varies with the initiation sequence, resulting in different stress shadow effects between fractures; for example, the smaller the perforation cluster spacing and well spacing are, the stronger the stress shadow effects are and the more seriously the fracture propagation inhibition arises. Moreover, as the spacing of perforation clusters and well spacing increases, the stress shadow effects decrease and the fracture propagation follows an almost straight pattern. In addition, the computed results of the dynamic distribution of stress-dependent unstable dynamic propagation of fractures under different stress fields are summarised.

A state-of-art review of stress shadow effects and continuum-discontinuum methods for stress-dependent unstable dynamic propagation of multiple hydraulic fractures are well summarized and analysed. This paper can provide a reference for those engaged in the research of unstable dynamic propagation of multiple hydraulic structures and have a comprehensive grasp of the research in this field.

To address the problem of how to guide and promote health knowledge adoption, based on online diabetes communities, this study explores the impact mechanism of social support on users' individual health knowledge adoption to provide insights for online diabetes community management and personal health management.

Integrating the theories of cognitive-affective personality system (CAPS) and social support, this study constructs a theoretical model, collects data through a questionnaire and uses a structural equation model to analyse 356 data.

The results show that: (1) Considering the online diabetes communities, it is reasonable to divide social support into emotional, information, network and respect support. (2) Social support affects individual health knowledge adoption through the intermediaries of knowledge argument quality, knowledge source credibility and positive emotions. (3) The order of the mediating effect of cognitive and emotional factors between social support and health knowledge adoption is knowledge argument quality > knowledge source credibility > positive emotions and rationality > sensibility. (4) Users pay more attention to the source credibility of professional health knowledge than that of experiential health knowledge.

This research expands the application scope of CAPS and opens the “black box” of the impact of social support on individual health knowledge adoption behaviour. Simultaneously, the dimensions of social support and the mediating effect between social support and the two types of health knowledge are discussed.

The traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds of “omics” data brings opportunities for developing new computational drug repositioning methods based on big data. The paper aims to discuss this issue.

Here, a new computational strategy is proposed for inferring drug–disease associations from rich biomedical resources toward drug repositioning. First, the network embedding (NE) algorithm is adopted to learn the latent feature representation of drugs from multiple biomedical resources. Furthermore, on the basis of the latent vectors of drugs from the NE module, a binary support vector machine classifier is trained to divide unknown drug–disease pairs into positive and negative instances. Finally, this model is validated on a well-established drug–disease association data set with tenfold cross-validation.

This model obtains the performance of an area under the receiver operating characteristic curve of 90.3 percent, which is comparable to those of similar systems. The authors also analyze the performance of the model and validate its effect on predicting the new indications of old drugs.

This study shows that the authors’ method is predictive, identifying novel drug–disease interactions for drug discovery. The new feature learning methods also positively contribute to the heterogeneous data integration.