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The purpose of this study is to explore the economic impact of financial restatements by major customers on the audit opinion of their suppliers, showing that non-financial information disclosure potentially helps auditors make better assessments.

Using a sample of China’s listed firms from 2007 to 2021, the authors aim to find the relationship between customers’ financial restatements and their suppliers’ audit opinions. Heckman selection model, placebo tests and other robustness checks are used as well.

The findings reveal that customers’ financial restatements have a significant effect on the likelihood of suppliers receiving modified audit opinions. This relationship is pronounced when suppliers face a higher level of financial constraints, exhibit poorer accounting conservatism or receive more negative media coverage. Additionally, this effect occurs through increased business risk and information risk, which heightens auditors’ perceived audit risk. Moreover, the study highlights the influence of switching costs, auditor expertise and restatement severity on this relationship.

Risks originating from customers can spread along the supply chain, emphasizing the necessity for auditors to give heightened attention to both the audited firms and their customer information. Moreover, regulators should carefully consider the important impact of customer information disclosures to maximize the protection of the interests of external information users.

This study not only confirms the crucial role of customer information disclosures in annual reports for stakeholders and auditors but also contributes to the existing literature on customer–supplier relationships.

The aim of this study was to develop a smart wearable mask designed for the prevention of respiratory infectious diseases by understanding consumer's preferences in designs and functions of the smart wearable masks.

To develop a smart mask design, a survey was conducted on Chinese consumers in their 20–40s and analyzed their mask wearing behaviors, preferences and caring aspects of masks. The collected data were analyzed to identify the demographic characteristics of the subjects surveyed by using the SPSS program, and technical statistical analysis was conducted. To identify differences in demographic characteristics, an independent samples t-test, one-way analysis of variance and Scheffe's ad hoc test were conducted.

Based on the research results, design guidelines for wearable masks were defined, and four wearable mask designs were developed and presented in 2D and 3D images based on the design guidelines. There were significant differences among people with different backgrounds.

It is significant that this research presents smart wearable mask design guidelines and designs through supplementation and improvement of existing mask. It is expected that this research provides basic empirical data for mask designs through the planning of smart wearable mask designs and surveys assessing consumer perceptions, attitudes and satisfaction.

Organizations are communicating with the public about their thoughts and behaviors relevant to the environment via social networking sites. The purpose of this paper is to explore for-profit and nonprofit organizations' Twitter messages to understand their environment-related messages and their influences on the publics' responses.

This study conducted a content analysis adopting four message classification systems: environmental message orientation, message specificity, message framing, and environmental issue. Guided by attribution theory, this study also explored how the organization's environmental messages influence social media (Twitter) user responses, likes, retweets, and replies.

The analysis showed that for-profits' messages tend to discuss their green products and manufacturing processes with specific numeric evidence, while nonprofits are disposed to describe a severely degraded environment. In addition, the study revealed that tweets yield a high number of likes and replies when the organizations are for-profits and the messages emphasize green products.

The findings of this study showed that the green message categorization systems are applicable to the social media context. But, this study focused on Twitter only. Future studies need to examine various social media platforms.

The study findings recommend communication practitioners use substantive green messages highlighting actual pro-environmental performances. Also, practitioners might need to make a linkage between the discussed environmental issue and the organization (e.g. a water issue by a wildlife-related nonprofit, an energy issue by a home appliance manufacturer, an air pollution issue by a bicycle company). In addition, regarding the message specificity, infographics can be present specific information that audiences can readily understand because it is described visually.

Scholars investigated environmental messages in advertising and cautioned that environmental messages that are not substantive or specific can cause audiences to perceive the messages as greenwashing. However, these previous studies focused on conventional media, and they have not been replicated in the age of social media. Thus, it is important to explore the current status of organizational environmental messages on social media.

The purpose of this research was to develop clothing-typed soft wearable robot embedded with textile-based actuators on ankles for elderly adults needing gait assistance.

Design guidelines were developed and they included function (type, targeting area, routing line and anchor points), design (size/fit, fabric/material, fastener, detail, color) and actuator (shape memory alloy type, size, deformation type, integration material, integration technique and evaluation method). Fabric-based actuator, integration methods to fabrics, routing lines and anchoring points were developed based on the guidelines and evaluated. Then, three long socks types and a pants type were designed and prototyped. Routing line position displacement measurement test was conducted with the prototypes. A survey was conducted to investigate satisfaction, likeness and use intention on the design/prototype to modify the designs.

Important design factors were identified, and design guidelines for clothing-typed soft wearable robots (SWRs) were developed. People satisfied the developed SWR designs and prototypes with mean scores over 4.60.

The results are expected to be helpful for designers and developers of SWRs in the development process, and they will ultimately be beneficial to members of the elderly population who have gait difficulties.

The purpose of this study is to examine factors affecting mobile phone users' attitude toward watching TV content on a mobile device. Under the uses and gratifications theory, the current study examined attitude toward watching TV content on a mobile phone, with antecedents of affinities for both watching TV content and a mobile phone use.

The current study adopted a construct of relative advantage as a mediator between affinities and attitude, and four moderators that affect the relationship between relative advantage and attitude. The study then analyzed 430 survey responses from public mobile phone users with a generalized linear model.

Research findings reveal that both affinities are significantly associated with the relative advantage of watching TV content on a mobile phone. Relative advantage plays a salient role in explaining attitudes toward watching TV content on a mobile phone. The relationship between attitude and relative advantage was significantly affected by relaxation, fashion status and accessibility.

This study contributes to media literature, especially where new, applied technology is considered. Particularly, the current research theoretically explains rationale behind a mobile phone user's positive attitude toward watching TV content on a mobile phone, and potential implication of the current and increasing trend of broadcasting individual content through social media outlets.

The television (TV) content ecosystem has shifted from traditional broadcasting systems to dedicated content producers and over-the-top (OTT) services. However, less empirical effort has been paid to the actual behaviors of the mobile users who watch TV content when explaining the impact of OTT service and mobile network profiles in watching TV content. This study aims to investigate the impact of gratifications and attitude formed by mobile TV users on actual mobile TV watching behaviors, as well as the moderating impacts of paid OTT service subscriptions and mobile network profiles, based on gratification theory, cognition–affect–behavioral (CAB) framework, sunk cost effect and walled-garden effect.

This study employs the generalized linear model (GLM) with generalized estimating equations (GEE) to test hypothesized relationships. A total of 338 mobile phone users who have been watching TV content using a mobile phone participated in the survey. The moderating variables, 4 types of paid streaming platform subscriptions, were classified based on the walled gardens formed by mobile telecom services.

The study’s results revealed that obtained gratifications and opportunity constructs substantially influenced a mobile phone user’s attitude and behaviors. Additionally, mobile network profiles and the degree of access to paid platform services played significant moderating roles in the relationship between users’ attitudes and behavior.

This research enriches the existing OTT service literature and is one of the pioneering studies investigating the walled-garden effect’s role in mobile phone users’ actual watching behaviors, offering valuable practical implications for the OTT platform providers.

To enhance the fit, comfort and overall satisfaction of lower body attire for online shoppers, this study introduces a reclassification method of the lower body profiles of young females in complex environments, which is used in the framework of remote clothing mass customization.

Frontal and lateral photographs were collected from 170 females prior, marked as size M. Employing a salient object detection algorithm suitable for complex backgrounds, precise segmentation of body profiles was achieved while refining the performance through transfer learning techniques. Subsequently, a skeletal detection algorithm was employed to delineate distinct human regions, from which 21 pivotal dimensional metrics were derived. These metrics underwent clustering procedures, thus establishing a systematic framework for categorizing the lower body shapes of young females. Building upon this foundation, a methodology for the body type combination across different body parts was proposed. This approach incorporated a frequency-based filtering mechanism to regulate the enumeration of body type combinations. The automated identification of body types was executed through a support vector machine (SVM) model, achieving an average accuracy exceeding 95% for each defined type.

Young females prior to being marked as the same lower garment size can be further subdivided based on their lower body types. Participants' torso types were classified into barrel-shaped, hip-convex and fat-accumulation types. Leg profile shapes were categorized into slender-elongated and short-stocky types. The frontal straightness of participants’ legs was classified as X-shaped, I-shaped and O-shaped types, while the leg side straightness was categorized based on the knee hyperextended degree. The number of combinations can be controlled based on the frequency of occurrence of combinations of different body types.

This methodological advancement serves as a robust cornerstone for optimizing clothing sizing and enabling remote clothing mass customization in E-commerce, providing assistance for body type database and clothing size database management as well as strategies for establishing a comprehensive remote customization supply chain and on-demand production model.

This research was to understand people's perceptions and trends in wearable robots and the research questions were as follows: (1) investigating key terms related to wearable robots that were frequently used by and exposed to people and (2) analyzing relationships among those key terms.

Textom, a big data collection and analysis software system, was used to collect data using the keyword – wearable robot.

The frequency-inverse document frequency, term frequency and central analyses were investigated, and the major key terms related to wearable robots and their connectivity were identified. After performing network analysis and convergence of iterated correlations analyses using UCINET and NetDraw programs, the major key term categories were identified.

It is important to understand how people think and perceive about wearable robots before developing wearable robots. The results of the research are expected to be helpful to better understand how people perceive and what key terms are mainly discussed by people in both countries and ultimately help when developing wearable robots with better market targeting approach methods.