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Drawing on the resource-based view (RBV) and structure–conduct–performance (SCP) paradigm perspectives, this study aims to investigate the influence of the innovation and technical capabilities of exporting organisations on their export performance moderate by competitive intensity.

Primary data were collected from 162 Chinese manufacturer–exporter companies operating across China. The conceptual framework of this study, which incorporated the impact of RBV and SCP paradigm determinants on export performance through the interaction effect of competitive intensity, was tested using structural equation modelling (Smart-PLS).

Results show that the technical and innovation capabilities can increase its export success in international markets. Furthermore, this research finds that competitive intensity moderates the positive relationship between technical capability and export performance but not the relationship between innovation capability and export performance.

This study presents a holistic assessment of the export performance of manufacturer–exporter enterprises by accounting for the overlooked effect of organisational capabilities through the moderating function of competitive intensity. This study has far-reaching consequences for export academics and practitioners, including the fundamental concept of an internationalizing small- and medium-sized enterprises, especially the manufacturers.

Green technology innovation is the organic combination of green development and innovation driven. It is also a powerful guarantee for shaping sustainable competitive advantages of manufacturing enterprises. To explore what kind of executive incentive contracts can truly stimulate green technology innovation, this study aims to distinguish the equity incentive and reputation incentive, upon their contractual elements characteristics and green governance effects, and then put forward suggestions for green technology innovation accordingly.

This study establishes an evaluation model and uses empirical methods to test. Concretely, using data from A-share listed manufacturing companies for the period from 2007 to 2020, this study compares and analyzes the impact of equity and reputation incentive on green technology innovation and explores the relationship between internal green business behavior and external green in depth.

This study finds that reputation incentives focus on long-term and non-utilitarian orientation, which can promote green technology innovation in enterprises. While equity incentives, linked to performance indicators, have a inhibitory effect on green technology innovation. Internal and external institutional factors such as energy conservation measures, the “three wastes” management system, and environmental recognition play the regulatory role in the relationship between incentive contracts and green technology innovation.

Those findings validate and expand the efficient contracting hypothesis and the rent extraction hypothesis from the perspective of green technology innovation and provide useful implications for the design of green governance systems in manufacturing enterprises.

This paper aims to study the startup performance of thrust bearing. The effects of acceleration scenarios, roughness, the area ratio of texture and texture depth on the transient startup performance of the thrust bearing were analyzed.

The lubrication model is solved by the Reynolds equation with the mass-conservation boundary condition. The Greenwood and Tripp contact model is used to predict asperity contact load. The finite volume method is used to discretize the governing equations.

By studying the bearing performance with different acceleration functions, it was found that the higher the acceleration at the beginning of the startup, the faster the thrust bearing operates under the hydrodynamic lubrication regime in the start stage. It appears that the friction and contact time of asperity increase with the increasing roughness. The optimal area ratio of texture is within 30%–50%. The depth of texture ranging from 1 to 2 is the best.

This paper proposes a transient mixed lubrication analysis model of the thrust bearing. This model can be used to analyze the variations of tribological performance and lubrication regime of the thrust bearing under different acceleration scenarios.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2022-0268/

This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.

This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks.

The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments.

The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available.

The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.

Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.

Since the start of the current century, the world at large has experienced uncertainties as a result of climate change, terrorism threats and increasing economic upheaval. These uncertainties create non-classical risks for global seaborne container trade and liner shipping networks (LSNs). The purpose of this paper is to establish a novel risk-based resilience framework to measure the effectiveness of different recovery strategies for the disruptions in LSNs in a quantitative manner.

Based on a resilience loss triangle model, an indicator of resilience–cost ratio is designed to measure the performance of LSNs during recovery. Four recovery strategies are proposed to test the rationality and feasibility of the developed indicator in aiding decision-making of LSNs from a resilience perspective.

The analysis results reveal that the superiorities of different recovery strategies vary depending on both the structures of LSNs and the specific requirements during recovery. Moreover, optimizing the sequence of ports being recovered will improve the overall recovery efficiency of the investigated LSN.

As an exploratory research trying to enrich the risk-based resilience evaluation of LSNs from a complex network perspective, only two attributes (e.g. port scare and economy) are considered at the current stage when estimating the time needed to fully recover the whole LSN. In future research, more attributes from the industry may be identified and incorporated into the proposed model to further extend its ability and application scopes.

The findings will help to improve managerial understandings of recovery strategies to build more resilient LSNs. The proposed model has the capability to be tailored to tackle different types of risks in addition to the storm disaster condition.

The risk-based resilience framework and the resilience–cost ratio indicator are newly developed in this research. They can consider LSNs' structural resilience and the total costs that a recovery strategy needs to restore the whole system simultaneously.

This study aims to investigate the role of regional environmental transparency on corporate environmental disclosure.

This study uses the introduction of a nationwide automated air pollution monitoring network in China as a quasi-natural experiment and employs regression analysis. Robustness checks, including parallel trend test and placebo test, are performed to test the robustness of the results.

Sharing air pollution data with the public can improve corporate environmental disclosure. Firms with poorer environmental, social and governance (ESG) performance prefer to disclose less informative information after the automated network is implemented compared with firms with better ESG performance. The relationship between information sharing and corporate environmental transparency is more pronounced when local air pollution is severer, firms face stronger investor scrutiny and firms are from heavily polluting industries. The mechanism tests suggest the automated system can draw public environmental attention and improve governments’ aspiration for environmental governance. Finally, corporate environmental disclosure can reduce stock price crash risk and cost of equity.

Real-time pollution data reporting is an important solution to raising public environmental awareness and then enhancing the effectiveness of pollution control.

This study has implications for policy-making regarding environmental governance and environmental disclosure.

This study confirms that pollution information transparency can motivate firms to increase environmental disclosure.

We contribute to the growing literature examining how social enterprises might best accommodate their hybrid structure when pursuing dual goals of social improvement and economic sustainability. Drawing on extant literature, the case is made for why synergy between the social and commercial business models that hybrid social enterprises employ should positively impact effectiveness in delivering organization outcomes. We then develop a method for comparing the synergy between the social and commercial business models employed within and across organizations, and test the method using a sample of seven social enterprises operating in different social fields. Results demonstrate that our method can be applied consistently across a range of social enterprise types and that variation in degree of synergy is considerable with overlap rates ranging from 9% to 77%. Using learning from this exploratory study, we develop propositions describing how and why social entrepreneurs develop business model synergy, the relationship between business model synergy and organizational performance, and suggest future research to test these propositions. Implications for theory development and practice are discussed.

Memorable tourism experiences (MTEs) can reinforce a destination's competitiveness. The literature has called for further research on this topic. This study develops and empirically examines a conceptual model exploring the relationships between MTEs, satisfaction, destination image (DI) and loyalty.

Quantitative research was conducted with 729 respondents who had visited the Greek island of Santorini in the last three years, and structural equation modeling was used.

The findings confirm the strength of the proposed model, which explained 58% of the variance for MTEs and 82% of that for tourist loyalty. The results reveal that MTEs influenced destination loyalty directly and indirectly through satisfaction, while DI influenced loyalty directly and indirectly.

This study provides new insight into the importance of MTEs, satisfaction and DI in the formation of destination loyalty.

This study provides new insight into the importance of MTEs, satisfaction, and DI in the formation of destination loyalty.

The proposed model is the first to include these factors and the specific relationships between them.

Disruptive innovation theory has attracted the interest of researchers and practitioners across many areas, resulting in the development of new business models and strategies. Despite the increasing scholarly attention, its definition has not yet been understood, the understanding of the term “disruptive” and the complex nature of this innovation has provoked some misinterpretations, and the meaning remains ambiguous. To address this confusion, this article undertakes a critical review of disruptive innovation in an attempt at providing a solid theoretical grounding.

The review examines the key issues of published articles, identified after conducting a search in the Web of Science scholarly database. The analysis highlights the basic definitions of disruptive innovation, showing its evolution, types and its characteristics. This article also examines the behaviours adopted by the actors associated with disruptive innovation (i.e. incumbents, entrants and customers).

Overall, this article finds that disruptive innovation has its own elements to be identified, requiring an in-depth analysis to avoid confusing with other innovation approaches. The findings suggest that disruptive innovation affects businesses and sectors in varied and complex ways because customers from low-end market and mainstream market appreciate this innovation. Further, its impact on practice is huge and incites further efforts in establishing a stronger theoretical grounding.

Our research contributes on the evolution of this theory, helping to better understand the phenomenon of disruption and can be used for different types of research settings.

The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.

First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.

The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.

The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.