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Oppositional loyalty toward rival brands is prevalent. Although its antecedents have increasingly received scholarly attention, the literature is rather disparate. Based on identity theory, this study aims to propose that oppositional loyalty is a brand identity-driven outcome and provides a unified framework for understanding the formation and activation of brand identity in influencing oppositional loyalty.

Structural equation modeling was used to test the theoretical framework based on an online survey of 329 brand community members. Multigroup analysis was used to test the moderating effect of inter-consumer brand rivalry and brand community engagement.

The results show that self-brand similarity, brand prestige and brand uniqueness lead to consumers’ brand identity (i.e. consumer-brand identification), which, in turn, facilitates oppositional loyalty. Furthermore, the results indicate that inter-consumer brand rivalry and brand community engagement are identity-salient situations that strengthen the relationship between consumer-brand identification and oppositional loyalty.

Identity has great power in shaping consumer behaviors. Fostering consumer-brand identification is critical for firms to prevent consumers from switching to competing brands. Inter-consumer brand rivalry and brand community engagement can help firms consolidate their customer base by evoking consumers’ brand identity.

This investigation makes theoretical contributions by providing a unified theoretical framework to model the development of oppositional loyalty based on identity theory.

This paper aims to focus on the high-speed rotary lip seal in aircraft engines, combining its service parameters, its own structure and application conditions, to study the influence of different eccentric forms, eccentricity, rotational speed and other factors on the performance of the rotary lip seal.

A numerical simulation model for high-speed eccentric rotary lip seals has been developed based on the theory of elastic hydrodynamic lubrication. This model comprehensively considers the coupling of multiple physical fields, including interface hydrodynamics, macroscopic solid mechanics and surface microscopic contact mechanics, under the operating conditions of rotary lip seals. The model takes into account eccentricity and uses the hazardous cross-sectional method to quantitatively predict sealing performance parameters, such as leakage rate and friction force.

Eccentricity has a large impact on lip seal performance; lips are more susceptible to wear failure under static eccentricity and to leakage failure under dynamic eccentricity.

This study provides a new idea for the design of rotary lip seal considering eccentricity, which is of guiding significance for the engineering application of rotary lip seal.

This study examines the relationship between chief financial officer (CFO) general ability and corporate debt financing, using all Chinese listed companies as the sample. It has significant implications for corporate shareholders and investors. Companies aiming to increase the proportion of external capital should prioritise the CFO's general ability. Similarly, investors can consider this a valuable sign before making investment decisions.

This study follows Custódio et al.'s (2013) approach to measuring the CFO general ability index (GAI) in five dimensions, while the D/E ratio is treated as the primary proxy of corporate debt financing. This study investigates the linear relationship between CFO general ability and corporate debt financing by using the ordinary least squares (OLS) regression with controlling year and industry fixed effect simultaneously.

Based on a sample dataset of all listed firms in China from 2008 to 2023, this paper identifies a significant positive correlation between CFOs' general management skills and corporate debt financing. This finding underscores that generalist CFOs prefer debt financing over equity financing. The paper also suggests that corporate innovation could be a potential mechanism through which CFOs' comprehensive management skills influence debt financing.

This study expands upon prior research by establishing a positive correlation between generalist CFOs and debt financing. Previous studies investigating the influence of CFO demographic characteristics have predominantly concentrated on singular dimensions, such as educational background, varied professional experiences and career trajectories, often overlooking the significance of past work experience. Secondly, this paper enriches the financial literature by introducing a novel determinant that substantially impacts debt financing.

This study aims to test the link between enterprise digital transformation and technological innovation and the mechanisms and channels behind this link.

This study systematically examines whether and how enterprise digital transformation affects technological innovation in China.

Enterprise digital transformation effectively improves technological innovation. This result remains stable in robustness and endogeneity checks. The channel mechanisms of this promoting effect are internal (improvement of internal control quality and alleviation of agency costs) and external (increased attention of analysts and reduction of customer concentration). Moreover, this promoting effect is more significant for state-owned enterprises, small and medium-sized enterprises, enterprises in areas with low marketization and enterprises that do not enjoy digital subsidies from the government.

Enterprises need to attend to the mechanisms behind the link between digital transformation and technological innovation and to the unique effects of different enterprise attributes and capital markets, such as size, the ownership nature, the degree of regional marketization and government subsidies. Doing so will effectively promote digital transformation and technological innovation and strengthen core competitiveness.

This study provides systemic evidence of the link between enterprise digital transformation and technological innovation. The findings enrich the research literature on enterprise digitization and the factors of influencing enterprises’ technological innovation and provide a reasonable explanation for how enterprise digital transformation affects technological innovation.

Purpose: Health insurance and big data analytics have become increasingly intertwined in recent years, offering both opportunities and challenges for the industry. Thus, the primary aim is to utilize bibliometric analysis for comprehensive literature reviews in health insurance and big data analytics.

Design/methodology/approach: Scopus, chosen for its broad coverage, is utilized to extract 493 manuscripts meeting the inclusion criteria set (year and language) for a 25-year period. The tools employed in the study include VOSViewer and Biblioshiny package (R-programming).

Findings: An emerging trend has been observed in the field of health insurance and big data analytics for 25 years. The US has been observed as the topmost leading country to contribute to the subject under study. The Ministry of Science and Technology of Taiwan is at the top first rank of top leading institutions contributing 20 documents to the field of health insurance and big data analytics. Moreover, thematic mapping and word cloud is done to find the most relevant keywords in the study. Furthermore, co-occurrence analysis revealed the relationship of keywords for health insurance and big data mining.

Implications: The implications of the research extend beyond academic insights and have practical implications for stakeholders involved in healthcare policy, practice, and research.

Originality/Value/Implications: The novelty in the manuscript has been brought in by focusing on one of the many types of insurance, i.e., health. Moreover, big data analytics in relation to health insurance for such a range of time period serves as the original presentation of the work with regards to the matter under study.

The paper aims to clarify the operation rationality of high speed trains (HSTs) under tunnel condition with the speed of 400 km/h through representative aerodynamic factors including running drag, eardrum comfort, carriages noise, aerodynamic loads on tunnel ancillary facilities and HST, micro-pressure waves, and then put forward engineering suggestions for higher speed tunnel operation based on the analysis.

Based on the field measurement data of CR400AF-C and CR400BF-J tunnel operation, correlations between each aerodynamic indicators with HST speed were established. By analyzing the safety reserve of aerodynamic indicators at 350 km/h and the sensitivity of each indicator to HST speed increasing and the indicators’ formation mechanism, the coupling relationship between various indicators was obtained.

The sensitivity of different aerodynamic indicators to speed variation differed. The aerodynamic indicators representing flow field around HST showed a linear relationship with HST speed including noise, eardrum comfort, aerodynamic load on HST body. The positive aerodynamic load on tunnel auxiliary facilities and the micro-pressure wave at the entrance of the tunnel have the same sensitivity to the 3th-power relation of HST speed. The over-limit proportion of micro-pressure wave was the highest among the indicators, and aerodynamic buffering measures were recommended for optimization. The open tunnel pressure relief structure is recommended, while allowing trains to pass through the tunnel at an unconditional speed of 380 km/h.

Comprehensive evaluation of multiple aerodynamic indicators for HST tunnel operation with higher speeds was realized. The main engineering requirements to release aerodynamic effect were identified and the optimization scheme is proposed.

In this digital age and risk society, this study aims to explore innovative strategies for E-retailers during supply chain disruptions to construct a more resilient supply chain system.

Various game theoretical models are constructed to analyze four supply chain scenarios. Meanwhile, sufficient numerical analysis was conducted to observe the impact of key parameters on supply chain strategies.

Multiple crucial factors exert a comprehensive influence on E-retailers’ decisions on sourcing and pricing, leading to the diversity and complexity of decision-making conditions. First, with the increased probability of disruption, the purchase quantities of the E-retailer from different suppliers are not in a linear changing pattern, and the total purchase quantity is allocated variably between different suppliers. Second, the variation in disruption severity (partial or complete) results in the shift of decisions between single-sourcing and dual-sourcing. Responsive pricing is conducive to increasing the purchase quantity and profits under partial disruption; its advantages are diminished when completely disrupted. Third, higher commission rates usually have a detrimental impact on profit, whereas responsive pricing may mitigate this impact.

Unlike the previous single perspective, this study innovatively explores strategies from the hybrid perspective of sourcing and pricing. By extracting two key factors (disruption probability and severity), it realizes the scientific characterization of supply chain disruptions. These achievements boost theoretical innovation. Concentrating on E-retailers, it avoids the generalization of conclusions and enhances the application value.

With the increasing complexity of public–private partnership (PPP) projects, the amount of data generated during the construction process is massive. This paper aims to develop a new information management method to cope with the risk problems involved in dealing with such data, based on domain ontologies of the construction industry, to help manage PPP risks, share and reuse risk knowledge.

Risk knowledge concepts are acquired and summarized through PPP failure cases and an extensive literature review to establish a domain framework for risk knowledge using ontology technology to help manage PPP risks.

The results indicate that the risk ontology is capable of capturing key concepts and relationships involved in managing PPP risks and can be used to facilitate knowledge reuse and storage beneficial to risk management.

The classes in the risk knowledge ontology model constructed in this research do not yet cover all the information in PPP project risks and need to be further extended. Moreover, only the framework and basic methods needed are developed, while the construction of a working ontology model and the relationship between implicit and explicit knowledge is a complicated process that requires repeated modifications and evaluations before it can be implemented.

The ontology provides a basis for turning PPP risk information into risk knowledge to allow the effective sharing and communication of project risks between different project stakeholders. It can also have the potential to help reduce the dependence on subjectivity by mining, using and storing tacit knowledge in the risk management process.

The apparent suitability of the nine classes of PPP risk knowledge (project model, risk type, risk occurrence stage, risk source, risk consequence, risk likelihood, risk carrier, risk management measures and risk case) is identified, and the proposed construction method and steps for a complete domain ontology for PPP risk management are unique. A combination of criteria- and task-based evaluations is also developed for assessing the PPP risk ontology for the first time.

In light of growing concerns related to the psychological vulnerability during the pandemic, this study aims to examine the impact of fear or trauma of COVID-19 on stress, anxiety and depression among management students. Additionally, the study also explores the possible strategies adopted by professional students to cope with the pandemic situation.

With an approach to establish a probable concrete relationship between fear with the level of stress, anxiety and depression, the data for the study was collected from 1,408 management students through a structured questionnaire designed in Google Form and administered through WhatsApp. The survey was carried out in the month of July and August 2020 during the lockdown period. Correlation and structural equation modeling have been used to examine the relationship among the test attributes.

The results from the study discovered that “fear of COVID-19” has a significant and considerable impact on the increased level of anxiety and stress among the professional students, but the observations did not demonstrate a significant influence of the “fear” on “depression.” The responses reveal that students have developed anxiety and felt stressed mostly due to uncertainty in the upcoming academic plans, disturbances in their regular academic routines and concerns about their future careers. Further, the findings have portrayed that students have adopted both protective and avoidance coping strategies to overcome the adverse consequences of the pandemic.

The study gives an insight on the psychological vulnerability of the management students and their capability to overcome such sudden disruptions due to pandemics. This research could thus, serve as a reference to the policymakers, universities and institutions while planning out programs and schemes, which would encourage the aspiring managers to overcome the crisis and prepare themselves to befit the vibrant corporate world.

Several studies exist on the impact of the pandemic on undergraduate students in different universities. However, there are a dearth of literature, which reflects the psychological vulnerability of professional graduates especially management students who are on the verge of starting their professional career.

Classifying the types of fabric defects in the textile industry requires a way to effectively detect. The traditional textile fabric defects detection method is human eyes, which performs very low efficiency and high cost. Therefore, how to improve the classification accuracy of textile fabric defects by using current artificial intelligence and to better meet the needs in the textile industry, the purpose of this article is to develop a method to improve the accuracy of textile fabric defects classification.

To improve the accuracy of textile fabric defects classification, an ensemble learning-based convolutional neural network (CNN) method in terms of textile fabric defects classification (short for ECTFDC) on an enhanced TILDA database is used. ECTFDC first adopts ensemble learning-based model to classify five types of fabric defects from TILDA. Subsequently, ECTFDC extracts features of fabric defects via an ensemble multiple convolutional neural network model and obtains parameters by using transfer learning method.

The authors applied ECTFDC on an enhanced TILDA database to improve the robustness and generalization ability of the proposed networks. Experimental results show that ECTFDC outperforms the other networks, the precision and recall rates are 97.8%, 97.68%, respectively.

The ensemble convolutional neural network textile fabric defect classification method in this paper can quickly and effectively classify textile fabric defect categories; it can reduce the production cost of textiles and it can alleviate the visual fatigue of inspectors working for a long time.