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This study aims to investigate the causal relationships within an experimental culture of improvisation capability and firm performance, following the logic of “culture-capability-performance” and building on informal institution theory and dynamic capability theory.

Data was mainly collected via on-site questionnaires from firm managers, and 196 valid questionnaires were analyzed using structural equation modeling to test the relationship among experimental culture, improvisation capability and firms’ performance.

Trust and support had a positive impact on firm spontaneity, while the effect of action promotion and error tolerance was not significant. Action promotion, trust and support demonstrate substantial positive effects on the creativity of a firm. Both dimensions of improvisation capability positively and significantly influence a firm’s performance.

The research respondents were firm managers. Cross-sectional data were used to analyze the model, which may cause common method variance. The research context was limited to China, and the generalizability of the results needs to be considered.

Firms can cultivate a culture of trust and support to enhance their spontaneity and improvisation capability. They can encourage cross-departmental communication, empower employees with autonomy in decision-making, provide appropriate resource support for employees’ decisions and use praise and reward incentives to spur further innovation achievements.

This study addresses the gaps in a firm’s improvisation capability within a Chinese market context by theoretically and empirically examining the role of experimental culture and assessing the relationship among each of the dimensions of improvisation capability in relation to firm performance identified in this study.

This paper aims to focus on the characteristics of a two-tier network featuring internal subject cooperation and external embedded cooperation in the context of regional innovation systems (RISs) and explore the influence of network characteristics on knowledge emergence.

Using social network analysis, a two-tier internal and external cooperation network of a RIS is constructed. A negative binomial regression method is used to explore the effects of the characteristics of these two-tier internal and external networks on knowledge emergence, the moderating effect of the cooperation knowledge base in this context is investigated and grouping and quantile regressions are used to conduct heterogeneity analysis.

The scale of the internal cooperation network has a positive effect on knowledge emergence, and the betweenness centralization of the internal cooperation network has an inverted U-shaped effect on knowledge emergence. The scale and structural holes of the external embedded network have an inverted U-shaped effect on knowledge emergence. Furthermore, the internal cooperation knowledge base weakens the influence of the external embedded network on knowledge emergence.

This research may enlighten policymakers with respect to improving the scale and structure of the RIS cooperation network and matching the embedded network based on the internal cooperation knowledge base to promote knowledge emergence.

This research contributes to the study of knowledge emergence by exploring the influence of a two-tier network structure and scale characteristics on knowledge emergence in RISs. This paper also extends the framework of relevant research by integrating the internal cooperation knowledge base into the analysis of externally embedded cooperation and knowledge emergence.

This study aims to examine the relationship between blockchain technology (BCT) adoption and firm performance (FIP) mediated by cyber-security risk management (CSRM) in the context of Vietnam, a developing country. Besides, the mediating effect of risk-taking tendency (RTT) has been considered in the BCT–CSRM nexus.

Data is collected using a survey questionnaire of Vietnamese financial firms through strict screening steps to ensure the representativeness of the population. The ending pattern of 449 responses has been used for analysis.

The findings of partial least squares structural equation modeling demonstrated that CSRM has a positive effect on FIP and acts as a mediator in the BCT–FIP nexus. Furthermore, RTT moderates the relationship between BCT and CSRM significantly.

This study introduces the attractive attributes of applying BCT to CSRM. Accordingly, managers should rely on BCT and take advantage of it to improve investment resources, business activities and functional areas to enhance their firm's CSRM. Especially, managers should pay attention to enhancing their RTT, which improves FIP.

This study supplements the previous literature in the context of CSRM by indicating favorable effects of BCT and RTT. Additionally, this study identifies the effectiveness of RTT as well as its moderating role. Ultimately, this paper has been managed as a pioneering empirical study that integrates BCT, RTT and CSRM in the same model in a developing country, specifically Vietnam.

This paper aims to identify, prioritise and explore the relationships between the various barriers that are hindering the machine learning (ML) adaptation for analysing accident data information in the Indian petroleum industry.

The preferred reporting items for systematic reviews and meta-analysis (PRISMA) is initially used to identify key barriers as reported in extant literature. The decision-making trial and evaluation laboratory (DEMATEL) technique is then used to discover the interrelationships between the barriers, which are then prioritised, based on three criteria (time, cost and relative importance) using complex proportional assessment (COPRAS) and multi-objective optimisation method by ratio analysis (MOORA). The Delphi method is used to obtain and analyse data from 10 petroleum experts who work at various petroleum facilities in India.

The findings provide practical insights for management and accident data analysts to use ML techniques when analysing large amounts of data. The analysis of barriers will help organisations focus resources on the most significant obstacles to overcome barriers to adopt ML as the primary tool for accident data analysis, which can save time, money and enable the exploration of valuable insights from the data.

This is the first study to use a hybrid three-phase methodology and consult with domain experts in the petroleum industry to rank and analyse the relationship between these barriers.