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This study aims to investigate the multiple influence paths or underlying mechanisms of entrepreneurial leadership (EL) on adaptive innovation from the perspectives of organizational learning and resource management, drawing on complex adaptive system theory.

With a questionnaire survey of 317 senior and middle managers from different firms in China, structural equation modeling was used to test the hypothesized conceptual model, and bootstrapping method was employed to examine the multiple mediating effects.

Results indicate that EL has a significant and positive effect on adaptive innovation. This relationship is partially mediated through exploitative learning, exploratory learning, resource bricolage and boundary-spanning integration, respectively. The impact of EL on adaptive innovation is also sequentially transmitted through exploitative learning and resource bricolage or exploratory learning and boundary-spanning integration.

Adaptive innovation has become a firm competition strategy to cope with dynamic changes in current uncertain environment where EL can play its effectiveness to engage firms in such innovation activities. However, the question of why and how EL drives adaptive innovation has yet to be discussed. This study highlights the innovation effectiveness of EL and the triggering process of adaptive innovation, and contributes to several countermeasures for firms to implement leadership and innovation practices responding to uncertain environment.

This paper aims to reveal how different types of events and top management teams' (TMTs’) cognitive frames affect the generation of breakthrough innovations.

Drawing on the event system theory and upper echelon theory, this study chose a Chinese manufacturing enterprise as the case firm and conducted an exploratory single-case study to unpack how breakthrough innovation generates over time.

By conducting the in-depth case analysis, the study revealed that firms do not produce breakthrough innovation in the catch-up stage and parallel-running stage but achieve it in the leading stage. It also indicated that when facing proactive events in the catch-up stage, TMTs often adopt a contracted lens, being manifested as consistency orientation, less elastic organizational identity and narrower competitive boundaries. In addition, they tend to adopt a contracted lens when facing reactive and proactive events in the parallel-running stage. In the face of reactive and proactive events in the leading stage, they are more inclined to adopt an expanded lens, being manifested as a coexistence orientation, more elastic organizational identity and wider competitive boundaries.

First, by untangling how TMT's cognitive frame functions in breakthrough innovations, this paper provides a micro-foundation for producing breakthrough innovations and deepens the understanding of upper echelon theory by considering the cognitive dimension of TMTs. Second, by teasing out several typical events experienced by the firm, this paper is the first attempt to reveal how events affect the generation of breakthrough innovation. Third, the work extends the application of the event system theory in technological innovation. It also provides insightful implications for promoting breakthrough innovations by considering the role of proactive and reactive events a firm experiences and TMT's perceptions.

Environmental degradation resulting from human activities may adversely affect human health in multiple ways. Until now, policies aimed at mitigating environmental problems such as climate change, environmental pollution and damage to biodiversity have failed to clearly identify and drive the potential benefits of these policies on health. The conducted study assesses and demonstrates how specific environmental policies and instruments influence perceived human health in order to ensure input for a data-driven decision process.

The study was conducted for the 2004–2020 period in European Union (EU) countries with the use of dynamic panel data modeling. Verification of specific policies' impact on dependent variables allows to indicate this their effectiveness and importance. As a result of the computed dynamic panel data models, it has been confirmed that a number of significant and meaningful relationships between the self-perceived health index and environmental variables can be identified.

There is a strong positive impact of environmental taxation on the health index, and the strength of this relationship causes effects to be observed in the very short term, even the following year. In addition, the development of renewable energy sources (RES) and the elimination of fossil fuels from the energy mix exert positive, although milder, effects on health. The reduction of ammonia emissions from agriculture and reducing noise pollution are other health-supporting factors that have been shown to be statistically valid. Results allow to identify the most efficient policies in the analyzed area in order to introduce those with the best results or a mix of such measures.

The results of the authors' research clearly indicate the health benefits of measures primarily aimed at improving environmental factors, such as environmental taxes in general. The authors have also discovered an unexpected negative impact of an increase in the share of energy taxes in total taxes on the health index. The presented study opens several possibilities for further investigation, especially in the context of the rapidly changing geopolitical environment and global efforts to respond to environmental and health challenges. The authors believe that the outcome of the authors' study may provide new arguments to policymakers pursuing solutions that are not always easily acceptable by the public.

Purchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially for e-retailers. To date, little is known about how e-retailers can scientifically detect users' intents within a purchase conversion funnel during their ongoing sessions and strategically optimize real-time marketing tactics corresponding to dynamic intent states. This study mainly aims to detect a real-time state of the conversion funnel based on graph theory, which refers to a five-class classification problem in the overt real-time choice decisions (RTCDs)—click, tag-to-wishlist, add-to-cart, remove-from-cart and purchase—during an ongoing session.

The authors propose a novel graph-theoretic framework to detect different states of the conversion funnel by identifying a user's unobserved mindset revealed from their navigation process graph, namely clickstream graph. First, the raw clickstream data are identified into individual sessions based on a 30-min time-out heuristic approach. Then, the authors convert each session into a sequence of temporal item-level clickstream graphs and conduct a temporal graph feature engineering according to the basic, single-, dyadic- and triadic-node and global characteristics. Furthermore, the synthetic minority oversampling technique is adopted to address with the problem of classifying imbalanced data. Finally, the authors train and test the proposed approach with several popular artificial intelligence algorithms.

The graph-theoretic approach validates that users' latent intent states within the conversion funnel can be interpreted as time-varying natures of their online graph footprints. In particular, the experimental results indicate that the graph-theoretic feature-oriented models achieve a substantial improvement of over 27% in line with the macro-average and micro-average area under the precision-recall curve, as compared to the conventional ones. In addition, the top five informative graph features for RTCDs are found to be Transitivity, Edge, Node, Degree and Reciprocity. In view of interpretability, the basic, single-, dyadic- and triadic-node and global characteristics of clickstream graphs have their specific advantages.

The findings suggest that the temporal graph-theoretic approach can form an efficient and powerful AI-based real-time intent detecting decision-support system. Different levels of graph features have their specific interpretability on RTCDs from the perspectives of consumer behavior and psychology, which provides a theoretical basis for the design of computer information systems and the optimization of the ongoing session intervention or recommendation in e-commerce.

To the best of the authors' knowledge, this is the first study to apply clickstream graphs and real-time decision choices in conversion prediction and detection. Most studies have only meditated on a binary classification problem, while this study applies a graph-theoretic approach in a five-class classification problem. In addition, this study constructs temporal item-level graphs to represent the original structure of clickstream session data based on graph theory. The time-varying characteristics of the proposed approach enhance the performance of purchase conversion detection during an ongoing session.

Manufacturing firms must embrace smart technologies and develop complex leadership approaches to achieve sustainability. Using the dynamic capability theory, this paper aims to examine the influence of the adoption of industry 4.0 technologies (AT) and paradoxical leadership (PL) on corporate sustainable performance (CSP) of manufacturing small-medium enterprises (SMEs) in Malaysia. Moreover, organisational ambidexterity (OA) is a mediator and strategic flexibility (SF) is a moderator in the study.

The study is a cross-sectional, quantitative study design that collected 395 usable responses through a simple random sampling technique and a close-ended structured questionnaire. Structural equation modelling (SEM) procedures were followed to analyse the data.

The statistical outcome implies that the AT significantly influence CSP and OA and mediate with CSP in the presence of OA. Moreover, PL shows a significant impact on OA, is insignificant on CSP and mediates with OA and CSP. The authors found a significant association between OA and CSP; however, SF did not provide evidence of a moderate effect.

The findings of this study clarify the role that organisational capabilities (OA, AT, PL and SF) play in fostering sustainability. The authors suggest incorporating SMEs from different geographies in other sectors by applying diverse methodologies and relevant constructs.

The result injects new perspectives into policy, managerial and individual levels. Installing OA, AT, PL and SF makes SMEs sustainable.

The empirical validation of the influence of OA and AT on CSP and the interaction of PL and SF enriches the organisational and entrepreneurial literature.

Despite the innovation and performance of venture capital (VC)-backed firms receiving extensive attention, how and under what conditions VC influences innovation and performance remains unclear. The present paper draws on organizational learning (OL) theory to examine the moderating effect of syndication on backed firms.

Drawing on a literature review that connects OL and innovation to the performance of VC-backed firms, this study examines the effects of OL on innovation and firm performance among these firms by questioning the moderating effect of VC syndication. A sample of 78 VC-backed firms was used to test the robustness of the proposed model and causal relationships through the use of partial least squares structural equation modeling (PLS-SEM).

The empirical evidence demonstrates that the intervention of venture capitalists can not only stimulate innovation, but also have a significantly positive effect on firm performance. Furthermore, the evidence reveals that syndication of VC investment supports backed firms in improving the firms' performance and generating innovation from acquired knowledge.

To the best of the authors' knowledge, this study is the first in North Africa that focuses on the moderating effects of venture capital syndication on the relationships between OL, innovation and firm performance.

Aiming to address the problem in which practitioners mindlessly prioritise the reputation of social media influencers (SMIs) over ensuring proper congruence between the influencers themselves and content marketing strategies, this study develops a conceptual framework that adopts an elaboration likelihood model and attachment theory to ascertain the effects of SMIs, live content marketing and their congruency on impulsive buying (IB).

Through collecting cross-sectional data from 608 valid replies, this study adopts an integrated method that combines partial least squares structural equation modeling (PLS-SEM), artificial neural network (ANN) and fuzzy-set qualitative comparative analysis (fsQCA) to analyse.

The results reveal that the credibility and relevance of live content, the empathy and professionalism of SMIs, and their congruence are critical antecedents of consumers' attachments to live content and SMIs, thus triggering their attachment to endorsement and IB.

In knowledge, this study benefits the scope of the elaboration likelihood model and attachment theory and enriches live streaming commerce literature. In method, an integrated analytical method is used to cope with complex structural relationships in non-linear patterns and predictions, as well as examine the complex causal configurations. In practice, this study facilitates practitioners to further ascertain what attributes in live content and SMIs can foster customer's emotional attachments in choosing endorsers and developing endorsement strategies, thus driving their IB.

As a kind of knowledge-intensive team coordinated across physical distance, it is necessary to construct a meta-knowledge driven transactive memory system (TMS) for the knowledge management of distributed agile team (DAT). This study aims to explore the comprehensive antecedents of TMS establishment in DATs and considers how TMS establishment is affected by herding behavior under the artificial intelligence (AI)-related knowledge work environment that emerges with technology penetration.

The data derived from 177 students of 52 DATs in a well-known Chinese business school, which were divided into 26 traditional knowledge work groups and 26 AI-related task groups to conduct a random comparative experiment. The ordinary least squares method was used to analyze the conceptual model and ANOVA was used to examine the differences in herding behavior between the control groups (traditional knowledge work DATs) and treatment groups (DATs engaged in AI-related knowledge work).

The results showed that knowledge diversity, professional knowledge, self-efficacy and social system use had significantly positive effects on the establishment of TMS. Interestingly, the authors also find that herding behavior may promote the process of establishing TMS of the new team, and this effect will be more significant when AI tasks are involved in team knowledge work.

By exploring the comprehensive antecedents of the establishment of TMS, this study provided a theoretical basis for knowledge management of DATs, especially in AI knowledge work teams. From a practical perspective, when the DAT is involved in AI-related knowledge works, managers should appropriately guide the convergence of employees’ behaviors and use the herding effects to accelerate the establishment of TMS, which will improve team knowledge sharing and innovation.

The purpose of the study was to analyse the impact of innovation ambidexterity represented by explorative and exploitative innovation capabilities and their combined effects on product innovation performance and to prove the mediating effect of decentralization.

The study uses partial least squares for structural equation models and SmartPLS version 3.3.1 on a sample of 174 Romanian medium- and large-sized firms from the IT industry to test six research hypotheses. To measure innovation ambidexterity, the orthogonal approach was used, conceptualizing innovation ambidexterity as a multidimensional, second-order construct composed of explorative and exploitative innovation capabilities. Innovation ambidexterity was conceptualized as a multiplicative term of both explorative and exploitative innovation capabilities.

The empirical results prove that innovation ambidexterity is positively correlated with product innovation performance, while decentralization is mediating the impact of innovation ambidexterity on product innovation performance in the IT industry.

The data was based on self-reported assessments of senior executives. While innovation ambidexterity may influence product innovation performance in the long term, such long-term effects are not assessed. Other studies found a moderating effect between centralization or decentralization and ambidexterity, while we found that it has a mediating effect.

In the context of innovation capability, the combination of explorative or exploitative capabilities may lead to a better synergy. Innovation ambidexterity influences product innovation performance through a synergistic effect, making the simultaneous combination of capabilities useful for firms willing to make efficient use of existing resources and make their capabilities mutually supportive. Moreover, for senior executives, the effects of decentralization as a mediator provide further incentive to include it in their development of firms' innovation capabilities.

This study extends findings of other studies by contributing to a deeper examination of the effects of decentralization, on innovation outcomes by focusing on a specific type of innovation, product innovation. Moreover, since innovation capability is often studied in small firms or in the manufacturing industry, this study contributes to the research on innovation capability and the consequences on innovation capability in the services sector and medium- and large-sized companies. By proving that decentralization mediates the effects of innovation ambidexterity on product innovation performance, it enables reconsideration of the organizational structure role in fostering innovation.

This study analyzes strategic flexibility with a two-dimensional approach (structural and decisional flexibility). It also investigates the relationships among competitive strategy, structural flexibility, decisional flexibility, and financial and nonfinancial performance.

The authors collected data from members of 16 chambers of commerce in the United States and used PLS-SEM (partial least squares structural equation modeling) to test the hypotheses.

The findings suggest that strategic flexibility impacts financial and nonfinancial performance in different ways. While financial performance is influenced by both the structural and decisional dimensions of strategic flexibility, nonfinancial performance is impacted only by structural flexibility. In addition, the research indicates a negative relationship between cost leadership and structural flexibility and positions structural flexibility as a mediator between cost-leadership and nonfinancial performance.

The authors contribute to strategic flexibility research in the following ways: (1) analyzed the impact on nonfinancial performance; (2) examined structural and decisional elements and (3) identified cost leadership as a potential barrier.