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Original equipment manufacturers (OEMs) are increasingly discoursing well-known brands to support their own brands. This study explores how original equipment manufacturing (OEM) brand disclosure affects willingness to buy (WTB) by examining the mediation effect of perceived brand competence (PBC) and perceived brand warmth (PBW), as well as the moderating effects of product type and consumer self-esteem (CSE).

This study builds on signal theory and the stereotype content model to theorize the mediating role of PBC and PBW between OEM brand disclosure and WTB. A 2×2 between-subjects experiment with 442 participants was conducted, employing ANOVA, seemingly unrelated regression and moderated mediation tests to examine the hypotheses.

OEM brand disclosure is positively related to WTB through PBC and PBW. Specifically, PBC’s mediation effect on OEM brand disclosure is stronger than that of PBW. Additionally, the mediation effect of OEM brand disclosure on WTB via PBC is moderated by product type and CSE.

This study contributes to the existing brand self-disclosure and brand spillover literature by opening the black box of how OEM brand disclosure affects WTB and reveals the underlying mechanisms of PBC and PBW. It offers valuable insights for OEMs to leverage previous OEM brands to support their own brands by improving PBC and PBW and is more beneficial for consumers with high self-esteem and experience products.

Despite the popularity of leveraging cause-related marketing (CRM) to make societal contributions and bolster business profits, sellers face a profound dilemma when conducting CRM due to consumers’ ambivalent understanding of sellers’ motivation for the initiative. Therefore, it is imperative to unravel consumers’ ambivalent understanding of CRM and determine how sellers can effectively employ CRM to elicit positive evaluations from consumers.

This study gathered survey data from 217 participants and applied a polynomial regression model and response surface analysis for disentangling ambivalent perception of CRM by investigating the influence of (in)congruence between perceived egoistic and altruistic motivation.

The incongruence between perceived egoistic and altruistic motivation can positively influence consumers’ evaluations of sellers. Moreover, when perceived egoistic and altruistic motivations are congruent, increasing their absolute level also enhances consumers’ evaluation of sellers. Moreover, sellers’ platform function usage behavior can amplify the positive effect of incongruence but has no salient moderating role on the congruence effect.

Differing from prior literature that predominantly focused on either the positive or negative interpretation of CRM, this study reveals the coexistence of both positive and negative viewpoints and disentangles the congruence and incongruence effect between the two motivational understandings.

This study aims to better understand the morphological characteristics of single particle and the health risk characteristics of heavy metals in PM_2.5 in different functional areas of Handan City.

High resolution transmission electron microscopy was used to observe the aerosol samples collected from different functional areas of Handan City. The morphology and size distribution of the particles collected on hazy and clear days were compared. The health risk evaluation model was applied to evaluate the hazardous effects of particles on human health in different functional areas on hazy days.

The results show that the particulate matter in different functional areas is dominated by spherical particles in different weather conditions. In particular, the proportion of spherical particles exceeds 70% on the haze day, and the percentage of soot aggregates increases significantly on the clear day. The percentage of each type of particle in the teaching and living areas varied less under different weather conditions. Except for the industrial area, the size distribution of each type of particle in haze samples is larger than that on the clear day. Spherical particles contribute more to the small particle size segment. Soot aggregate and other shaped particles contribute more to the large size segment. The mass concentrations of hazardous elements (HEs) in PM_2.5 in different functional areas on consecutive haze pollution days were illustrated as industrial area > traffic area > living area > teaching area. Compared with the other functional areas, the teaching area had the lowest noncarcinogenic risk of HEs. The lifetime carcinogenic risk values of Cr and As elements in each functional area have exceeded residents’ threshold levels and are at high risk of carcinogenicity. Among the four functional areas, the industrial area has the highest carcinogenic and noncarcinogenic risks. But the effects of HEs on human health in the other functional areas should also be taken seriously and continuously controlled.

The significance of the study is to further understand the morphological characteristics of single particles and the health risks of heavy metals in different functional areas of Handan City. the authors hope to provide a reference for other coal-burning industrial cities to develop plans to improve air quality and human respiratory health.

To support digital humanities research more effectively and efficiently, this study develops a novel Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) for the Digital Humanities Research Platform for Biographies of Chinese Malaysian Personalities (DHRP-BCMP) based on artificial intelligence (AI) technology that would not only allow humanities scholars to look at the relationships between people but also has the potential for aiding digital humanities research by identifying latent relationships between people via relationships between people and organizations.

To verify the effectiveness of KGAT-PO, a counterbalanced design was applied to compare research participants in two groups using DHRP-BCMP with and without KGAT-PO, respectively, to perform people relationship inquiry and to see if there were significant differences in the effectiveness and efficiency of exploring relationships between people, and the use of technology acceptance between the two groups. Interviews and Lag Sequential Analysis were also used to observe research participants’ perceptions and behaviors.

The results show that the DHRP-BCMP with KGAT-PO could help research participants improve the effectiveness of exploring relationships between people, and the research participants showed high technology acceptance towards using DHRP-BCMP with KGAT-PO. Moreover, the research participants who used DHRP-BCMP with KGAT-PO could identify helpful textual patterns to explore people’s relationships more quickly than DHRP-BCMP without KGAT-PO. The interviews revealed that most research participants agreed that the KGAT-PO is a good starting point for exploring relationships between people and improves the effectiveness and efficiency of exploring people’s relationship networks.

The research’s limitations encompass challenges related to data quality, complex people relationships, and privacy and ethics concerns. Currently, the KGAT-PO is limited to recognizing eight types of person-to-person relationships, including couple, sibling, parent-child, friend, teacher-student, relative, work, and others. These factors should be carefully considered to ensure the tool’s accuracy, usability, and ethical application in enhancing digital humanities research.

The study’s practical implications encompass enhanced research efficiency, aiding humanities scholars in uncovering latent interpersonal relationships within historical texts with high technology acceptance. Additionally, the tool’s applications can extend to social sciences, business and marketing, educational settings, and innovative research directions, ultimately contributing to data-driven insights in the field of digital humanities.

The research’s originality lies in creating a Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) using AI, bridging the gap between digital humanities research and AI technology. Its value is evident in its potential to efficiently uncover hidden people relationships, aiding digital humanities scholars in gaining new insights and perspectives, ultimately enhancing the depth and effectiveness of their research.