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Innovation is necessary to ensure consistent economic growth and to meet the global competition. In view of this, the purpose of this study to check the effect of governance quality as a tool for fostering innovation performance.

The empirical analysis was arranged on 20 year’s (2000–2019) data from South Asian economies. Subject to the existence of cointegration, the authors use the fully modified ordinary least square model for regression analysis and check the robustness through robust least square model.

The empirical findings infer that all dimensions of governance have a positive significant impact on both research and development expenditures and trademark applications jointly known as innovation performance. In addition, the empirical analysis discloses the positive effect of all control variables, including FDI inflow, banking sector development, economic growth, ease of doing business index and government subsidies on innovation activities. The analysis confirms the “grease the wheel” role of governance in fostering innovation.

It is apparently recommended to strengthen the exercise of better governance to harvest better innovation scores.

This study advocates the positive role of individual dimensions of governance recommended by existing literature and complements the literature by jointly exploring the impact of all governance dimensions on overall innovation performance.

In the current competitive era of industrialization, a significant level of innovation is necessary to meet the growing competition. There are many economic forces that determine the pace of innovation within a country. Among others, this study aims to focus on exploring the relevant role of corruption control (CC) in determining the innovation level.

For empirical analysis, the authors sample the 24 years of data (1996–2019) of Asian economies and use the fully modified ordinary least square (OLS) and dynamic OLS models to check the regression among variables. The selection of both techniques is based upon the empirical suggestions offered by unit root testing and the Johansen cointegration test.

The empirical findings infer the positive and statistically significant role of CC in boosting innovation. Strengthening the corruption-free environment encourages innovation activities within the country. In addition, foreign direct investment has a negative relationship with CC while financial development, economic growth, export volume and government subsidies positively determine the innovation level.

Based on empirical analysis, it is suggested that the policy officials should do more focus on CC to enhance the competitiveness of the country through more innovation.

The empirical analysis robust the findings of existing literature in an alternative data set and offers innovative views regarding the role of other factors in boosting the innovation level.

The purpose of this paper is to discuss how enterprises can effectively perceive and use the digital opportunities brought about by digital technologies and dynamic environments and how they can enhance their capabilities to realize digital transformation and adapt to the development of the digital economy era.

Based on the windows of opportunity theory and strategic cognition theory, this paper conducts an empirical analysis of the questionnaire data of 268 enterprises and discusses the influence of external windows of opportunity and internal windows of opportunity on the digital transformation of enterprises, as well as the action mechanism of strategic cognition and entrepreneurship.

The results show that both the external windows of opportunity and the internal windows of opportunity have significant positive effects on the digital transformation of enterprises. Strategic cognition plays a partial mediating role in the external windows of opportunity and the internal windows of opportunity influencing the enterprise digital transformation process. Entrepreneurship plays a positive regulatory role in the process of external windows of opportunity and internal windows of opportunity influencing strategic cognition.

This paper deepens the relationship between internal and external windows of opportunity and enterprise digital transformation and contributes a new theoretical cognition. This paper integrates the strategic cognition theory to clarify the complex process mechanism of digital transformation using external situational opportunities and internal capabilities. This paper introduces entrepreneurship into the path mechanism of digital transformation and expands the characteristics of the study of digital transformation antecedents to the individual level within the enterprise.

This research explores how individual factors drive early-stage strategic entrepreneurship (SE) in Vietnam and Taiwan. The authors extend SE and integrate knowledge spillover theory to gain insights into the relationship between individual factors and SE. The research highlights the importance of a dual process, which involves advantage-creating by innovation, as value creation and capture, and advantage-leveraging by growth and international expansion, as value retention and capture.

Innovation-oriented SE (ISE), growth-oriented SE (GSE) and internationalization-oriented SE (ITSE) are identified as new measures of SE. There are six hypotheses containing the effect of six personal characteristics have on SE. The authors employed logit regression to estimate the effect of independent variables on SE based on a pooled cross-sectional dataset drawn from Global Entrepreneurship Monitoring (GEM) in Vietnam and Taiwan during 2013–2018.

Opportunity sensing, education, self-funding ability, startup knowledge and skills and startup experience are crucial to the engagement of at least one type of SE in Vietnam. In contrast, education, self-funding ability and start-up knowledge and skills are key factors in Taiwan.

This study contributes to the extension of SE at the individual level in the early phase of new venturing and the integration of knowledge spillover theory. In order to drive early-stage SE further, the authors recommend to prioritize learning from spillovers within and among organizations, industries and communities, as well as through quality institutions, in addition to the individual drivers.

This study aims to explore the associations between leadership styles (i.e. transformational and ethical) and innovative work behavior within higher education institutions (HEIs), additionally investigating the mediating role of work engagement and the moderating role of psychological empowerment.

The basis of this study rested upon adopting a cross-sectional research framework. The data were acquired from a sample comprising 825 academic staff and 275 leaders across 226 HEIs in China. Employing a quantitative methodology, the researchers used AMOS version 26.0 and SPSS version 22.0 for statistical analysis.

The results indicated that leadership styles (i.e. transformational and ethical) positively affected innovative work behavior, both directly and indirectly (via work engagement). Also, psychological empowerment moderated the linkage between leadership styles and innovative work behavior but not the association between work engagement and innovative work behavior.

Despite some existing literature having discussed the correlation between leadership styles and innovative work behavior, there appears to be a conspicuous dearth of research endeavoring to explore the mediator (i.e. work engagement) and the moderator (i.e. psychological empowerment) within the above nexus, especially in the context of HEIs. Thus, this study can be considered original, introducing novel perspectives and substantial contributions to the management literature on HEIs. In addition, it offers insights for organizational managers.

The focus is on determining the long-term relationship in explaining how technological capabilities interact with trade and global value chain (GVC) participation to aid in the upgradation process using a panel auto-regressive distributed lag (ARDL) model. The results suggest that export of both low-skill and medium-skill technology-intensive manufactures and patents by residents positively and significantly impact GVC participation.

This paper examines the dynamic linkages between GVC participation and technological capability of major Asian countries in a comparative (1995–2018) perspective.

This implies that certain sectors enable greater integration into GVCs in the long-run, supported by critical learning variables. Further, with the help of the panel causality test, a bi-directional flow between GVC participation and export of high-technology manufactures and import of labour-intensive technology manufactures is witnessed. Even a one-way flow from research and development (R&D) intensity to GVC participation is seen.

The technological capabilities are found to be characterising the initial structure of local enterprises in trade and GVCs, as well as the extent to which emerging-market firms may harness knowledge flows and migrate into high-tech industries.

This study aims to test the effects of Technology-related Divestitures (TRDs) on firms' exploratory innovation. The moderating effects of firms' internal and external resource coordination activities, resource buffering and bridging, are also explored.

A set of data including 1,372 Chinese listed firms from 2009 to 2018 is adopted. Based on propensity score matching sample, random-effect Tobit models were employed to test the hypotheses.

The empirical results indicate that TRDs inhibit firms' exploratory innovation, while both resource buffering and bridging can mitigate this negative effect. This implies that to promote exploratory innovation, resource coordination activities are the essential.

The research findings can contribute to both the exploratory innovation and technological divestiture literature. The test on the moderating roles of resource buffering and bridging can also extend our understanding of the effect of TRDs on firms' exploratory innovation. Accordingly, several practical implications can be provided. This is especially important for strategic decisions of firms from emerging and developing countries, which often lack sufficient internal resources and strong technological capabilities to develop exploratory innovation.

The author investigates the effect of trade protection on domestic firm innovation in China and explores the channel through which trade protection affects corporate innovation.

Using a sample of Chinese A-share manufacturing companies from 2003 to 2019, the author starts with a univariate analysis by examining the innovation output after trade protection for all samples. The author uses the natural logarithm of one plus the number of trade protection cases received by the industry to which the firm belongs in a particular year to proxy for trade protection.

The author finds that trade protection significantly encourages firms’ patent application, particularly substantive patents, which is stronger in non-state-owned enterprises. Moreover, the mitigation of financial constraint is plausible channel that allows trade protection to promote innovation.

For practitioners, they should seize the dividends of national policies. In the process of implementing trade protection, they should concentrate on improving their innovation level and enhancing their core competitiveness. When they are not subject to trade protection, they can also make profits and develop in the long run.

For policy makers, in the early stage of industry development, trade protection can be used to ease the companies’ financing constraints and improve the companies’ profits, which will help them concentrate their efforts, promote innovation and further develop. However, in the mid-term development of the industry, policy makers should reduce trade protection. Through the entry of foreign capital, companies face increased competition, which can enhance the companies’ motivation for long-term development.

Overall, this paper sheds light on the real effects of trade protection and the determinants of innovation. First, the paper sheds light on the impact of international trade on firms’ innovation. Second, this study also contributes to the emerging literature on the effect of trade policy uncertainty on financial constraint. Third, the paper adds to the stream of literature on the drivers of innovation.

In recent years patents have become a very popular data source for forecasting technological changes. However, since a vast amount of patents are “worthless” (Moore, 2005), there is a need to identify the promising ones. For this purpose, previous approaches have mainly used bibliographic data, thus neglecting the benefits of textual data, such as instant accessibility at patent disclosure. To leverage these benefits, this study aims to develop an approach that uses textual patent data for predicting promising patents.

For the identification of promising patents, the authors propose a novel approach which combines link prediction with textual patent data. Thereby the authors are able to predict the emergence of hitherto unmentioned bigrams. By mapping these future bigrams to recent patents, the authors are able to distinguish between promising and nonpromising patents. To validate this approach, the authors apply the methodology to the case example of camera technology.

The authors identify stochastic gradient descent as a suitable algorithm with both a receiver operating characteristic area under curve score and a positive predictive value of 78%, which outperforms chance by a factor of two. In addition, the authors present promising camera patents for diverse application fields, such as cameras for surgical systems, cameras for rearview vision systems in vehicles or light amplification by stimulated emission of radiation detection and ranging cameras for three-dimensional imaging.

This study contributes in at least three directions to scholarship. First, the authors introduce a novel approach by combining link prediction with textual patent analysis and, in this way, leverage the benefits of both worlds. Second, the authors add to all theories that regard novel technologies as a recombination of existing technologies in presenting word combinations from textual data as a suitable instrument for revealing recombination in patents. And third, the approach can be used by scholars as a complementary or even integrative tool with conventional forecasting methods like the Delphi technique or Scenario planning.

At least three practical implications arise from the study. First, incumbent firms of a technology branch can use this approach as an early-warning system to identify technological change and to identify opportunities related to their company’s technological competence and provide inspiration for new ideas. Second, companies seeking to tap into new markets may also be interested in the approach as managers could anticipate whether their company’s technological competences are in line with upcoming trends. Third, the approach may be used as a supportive tool for various purposes, such as investment decisions or technology life cycle analysis.

The approach introduces textual patent data as suitable means for forecasting activities. As the statistical validation reveals, the promising patents identified by the approach are cited significantly more often than patents with less promising prospects.

Consequence prediction is an emerging topic in safety management concerning the severity outcome of accidents. In practical applications, it is usually implemented through supervised learning methods; however, the evaluation of classification results remains a challenge. The previous studies mostly adopted simplex evaluation based on empirical and quantitative assessment strategies. This paper aims to shed new light on the comprehensive evaluation and comparison of diverse classification methods through visualization, clustering and ranking techniques.

An empirical study is conducted using 9 state-of-the-art classification methods on a real-world data set of 653 construction accidents in China for predicting the consequence with respect to 39 carefully featured factors and accident type. The proposed comprehensive evaluation enriches the interpretation of classification results from different perspectives. Furthermore, the critical factors leading to severe construction accidents are identified by analyzing the coefficients of a logistic regression model.

This paper identifies the critical factors that significantly influence the consequence of construction accidents, which include accident type (particularly collapse), improper accident reporting and handling (E21), inadequate supervision engineers (O41), no special safety department (O11), delayed or low-quality drawings (T11), unqualified contractor (C21), schedule pressure (C11), multi-level subcontracting (C22), lacking safety examination (S22), improper operation of mechanical equipment (R11) and improper construction procedure arrangement (T21). The prediction models and findings of critical factors help make safety intervention measures in a targeted way and enhance the experience of safety professionals in the construction industry.

The empirical study using some well-known classification methods for forecasting the consequences of construction accidents provides some evidence for the comprehensive evaluation of multiple classifiers. These techniques can be used jointly with other evaluation approaches for a comprehensive understanding of the classification algorithms. Despite the limitation of specific methods used in the study, the presented methodology can be configured with other classification methods and performance metrics and even applied to other decision-making problems such as clustering.

This study sheds new light on the comprehensive comparison and evaluation of classification results through visualization, clustering and ranking techniques using an empirical study of consequence prediction of construction accidents. The relevance of construction accident type is discussed with the severity of accidents. The critical factors influencing the accident consequence are identified for the sake of taking prevention measures for risk reduction. The proposed method can be applied to other decision-making tasks where the evaluation is involved as an important component.