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Lightweight apps such as WeChat mini programs (WMPs) are an emerging mobile channel (m-channel) touchpoint that have gained remarkable popularity among consumers. Despite the focus of migration research on traditional m-channel touchpoints (e.g. native apps and mobile websites), but few researchers have examined why consumers switch from native to lightweight apps. Drawing on the push-pull-mooring framework, this study aims to identify the key factors influencing consumers' switching related to lightweight apps.

The data were collected using a questionnaire survey of 416 WMP consumers and the proposed model was analyzed through structural equation modeling.

The results show that the push effect, specifically, high privacy concern, nudges consumers away from native apps, whereas the pull effects, including relative ease of use, convenience of access and exit and socially-oriented interaction, entice consumers to lightweight apps. Further, consumer switching intention is influenced by habit and perceived technology control, both of which reflect the mooring effects. Switching intention also stands as an important precedent of actual behavior.

This study is among the first theoretical explorations of consumer switching across m-channel touchpoints in the context of mobile commerce. For information system practice, these findings provide new insights for both incumbent providers and newcomers on how to retain existing shoppers as well as attract potential shoppers effectively.

The purpose of this paper is to propose the self-presentation styles of advertising influence consumer self-image, which in turn influence purchase intention.

Using virtual brands as stimuli in a series of experiments, this study collects data on consumer self-image and purchase intention in the conditions of different advertising styles.

While consumer self-image mediates the relationship between advertising self-presentation style and purchase intention, the consumption situation (public vs private) moderates the relationship between self-presentation style, consumer self-image and purchase intention. That is, self-enhancing advertising promotes customers’ ideal self, which in turn increases their purchase intention for publicly consumed products, whereas self-deprecating advertising solicits customers’ real self, which in turn increases their purchase intention for privately consumed products.

This study informs product/brand managers and marketers of the importance of aligning the self-presentation style of advertising with the consumption situation of the product being advertised.

Based on self-consistency theory, this study not only finds a relationship between the self-presentation style of advertising and purchase intention, but also uncovers the mediating role of self-image in this relationship. Furthermore, the relationship chain of “self-presentation style of advertising – self-image – purchase intention” is moderated by the consumption situation of the product. This is one of the first studies to explore the intricacies of these relationships.

The current study examines the impact of big data analytics capabilities (BDAC) on supply chain performances of the manufacturing firms. Based on the underpinning of resource-based view (RBV) theory, the current study will highlight the significance of BDAC on green dynamic capabilities (GDC), supply chain agility (SCA) and green competitive advantage (GCA). Furthermore, the study examines the moderating effect of supply chain innovativeness (SCI) on the relationship between GCA and firm performance (FP).

Online survey method was employed for the data collection from the 331 managers employed in Pakistan Stock Exchange (PSX)-listed manufacturing firms. The hypothesized model was tested using partial least squares structural equation modeling (PLS-SEM) technique.

The study results indicate that BDAC has a positive influence on both GDC and SCA, leading to enhanced GCA. Furthermore, the results demonstrate that GCA significantly and positively impacts FP, and the relationship between them is positively moderated by SCI.

This study developed a novel theoretical perspective based on RBV theory and provided empirical evidence that manufacturing firms' performances are significantly influenced by BDAC, GDC and SCA. The study results provide valuable practical implications top management regarding the effectiveness of BDAC and SCA in the supply chain. The findings further highlight the significance of SCI strengthening relationship between GCA and FP.

This paper aims to examine the internal logical relationship between two intergenerational inheritance ways of passing property rights and residual control rights (RCR) and to construct a conceptual model comprising transfer elements, paths and timing of succession in this process.

Driven by the cases of Haixin, Tianyijiao and Changhe Group, this paper applies research methods of copying and expanding analysis logic, progressive deduction, content analysis and comparative research based on the perspective of HeXie theory to explore the deep interrelation of transfer elements, paths and timing during family business succession.

The findings present that the content of intergenerational inheritance of a family firm is the inheritance of property rights and RCR. First, the inheritance of property rights is a static inheritance of time-point delivery, whereas the inheritance of RCR is a dynamic inheritance process for a period of time. Second, the inheritance of property rights and RCR are not independent; only a “HeXie” succession of both rights can realize a successful inheritance of family firms.

This paper constructs the paths and timing model of intergenerational inheritance of property rights and RCR in family firms. This paper integrates the current literature studies on the family inheritance of property rights and RCR and explains their internal mechanisms. This paper also provides a theoretical foundation and empirical evidence for family business transitions in the business world.

Drawing on attribution theory, the current paper aims to examine the effects of review content structures on online review helpfulness, focusing on three pertinent variables: review sidedness, information factuality, and emotional intensity at the beginning of a review. Moreover, the moderating roles of reviewer reputation and review sentiment are investigated.

The review sentiment of 144,982 online hotel reviews was computed at the sentence level by considering the presence of adverbs and negative terms. Then, the authors quantified the impact of variables that were pertinent to review content structures on online review helpfulness in terms of review sidedness, information factuality and emotional intensity at the beginning of a review. Zero-inflated negative binomial regression was employed to test the model.

The results reveal that review sidedness negatively affects online review helpfulness, and reviewer reputation moderates this effect. Information factuality positively affects online review helpfulness, and positive sentiment moderates this impact. A review that begins with a highly emotional statement is more likely to be perceived as less helpful.

Using attribution theory as a theoretical lens, this study contributes to the online customer review literature by investigating the impact of review content structures on online review helpfulness and by demonstrating the important moderating effects of reviewer reputation and review sentiment. The findings can help practitioners develop effective review appraisal mechanisms and guide consumers in producing helpful reviews.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

The purpose of this paper is to investigate the effect of NaCl solution with different concentrations on impact-sliding fretting corrosion behavior of Inconel 690TT steam generator heat transfer tubes.

The optical 3D profiler was used to measure the wear profile and calculated the wear volume. Corrosion behavior was studied using open circuit potential monitoring and potentiodynamic polarization testing. The morphologies and elemental distributions of wear scars were analyzed using scanning electron microscopy and energy-dispersive spectroscopy. The synergism of wear and corrosion was analyzed according to the ASTM G119 standard.

The corrosion tendency reflected by OCP and the corrosion current calculated by Tafel both increased with the increase of NaCl concentration. The total volume loss of the material increased with concentration, and it was known from the synergism that the volume loss caused by corrosion-enhanced wear accounted for the largest proportion, while the wear-enhanced corrosion also made a greater contribution to volume loss than tangential fretting corrosion. Through the analysis of the material morphologies and synergism of wear and corrosion, the damage mechanism was elucidated.

The research findings can provide reference for impact-sliding fretting corrosion behavior of Inconel 690TT heat transfer tubes in NaCl solution with different concentrations.

This paper aims to predict the effect of the hub cavity leakage on the overall performance with numerical simulations in the fan/booster of a high bypass ratio turbofan engine.

Simulations are conducted for leakage at the fan, the outlet of guide vane and the three-stage booster, as well as hub leakage (contain cavities and sealing). The results obtained are compared to the corresponding simulations without hub leakage.

The rotor/stator interaction locations are evaluated to discover a better location. The results show that the seal tooth structure produces secondary flow and turbulence in the root of blade suction surface, which increases the aerodynamic loss. The sealing clearance should be controlled to shrink the turbulent region and decrease the leakage.

This work can provide a theoretical guidance and technical support for the compressor design, which avoid many repeated manufactures and reduce waste of resources.

This work improves the understanding of the impact mechanism of hub cavity leakage on the performance when the clearance size of seal is variable.

The purpose of this paper is to analyze the demands of the core stakeholders and how these stakeholders drive the information disclosure behaviors of the enterprise and local government.

Content analysis was conducted. The authors collected and analyzed information disclosure laws and regulations regarding environmental emergencies in China, as well as related media reports and official accident investigation report about the oil pipeline leakage and explosion accident in City Q. The authors divided the whole process of the accident into four stages, i.e., the prodromal stage, acute stage, chronic stage, and resolution stage, and then analyzed the different demands of stakeholders and the different information disclosure behaviors of the enterprise and local government during these four stages.

During the environmental emergency, the enterprise and local government exhibited information disclosure behaviors for their own benefits. There was severe information asymmetry between the enterprise and local government. Local government acted more positively in terms of information disclosure than the enterprise due to the demands of stakeholders. There were significant differences between the driving effects of different stakeholders. The effects of central government and local communities were the strongest, followed by news media and environmental organizations, whereas general public had the weakest impact. In addition, the effects of stakeholders on the information disclosure varied throughout different stages.

This paper considered a Chinese typical case study, thereby providing details of information disclosure behaviors of the enterprise and local government during an environmental emergency, and making comparative analysis on the driving effects on information disclosure by different stakeholders.

In social media, crisis information susceptible of generating different emotions could be spread at exponential pace via multilevel super-spreaders. This study aims to interpret the multi-level emotion propagation in natural disaster events by analyzing information diffusion capacity and emotional guiding ability of super-spreaders in different levels of hierarchy.

We collected 47,042 original microblogs and 120,697 forwarding data on Weibo about the “7.20 Henan Rainstorm” event for empirical analysis. Emotion analysis and emotion network analysis were used to screen emotional information and identify super-spreaders. The number of followers is considered as the basis for classifying super-spreaders into five levels.

Official media and ordinary users can become the super-spreaders with different advantages, creating a new emotion propagation environment. The number of followers becomes a valid basis for classifying the hierarchy levels of super-spreaders. The higher the level of users, the easier they are to become super-spreaders. And there is a strong correlation between the hierarchy level of super-spreaders and their role in emotion propagation.

This study has important significance for understanding the mode of social emotion propagation and making decisions in maintaining social harmony.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0192.