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This paper investigates the semantic association mechanisms of weak demand signals that facilitate innovative product development in terms of conceptual and temporal precedence, despite their inherent ambiguity and uncertainty.

To address this challenge, a domain ontology approach is proposed to construct a customer demand scenario-based framework that eliminates the blind spots in weak demand signal identification. The framework provides a basis for identifying such signals and introduces evaluation indices, such as depth, novelty and association, which are integrated to propose a three-dimensional weak signal recognition model based on domain ontology that outperforms existing research.

Empirical analysis is carried out based on customer comments of new energy vehicles on car platform such as “Auto Home” and “Bitauto”. Results demonstrate that in terms of recognition quantity, the three-dimensional weak demand signal recognition model, based on domain ontology, can accurately identify six demand weak signals. Conversely, the keyword analysis method exhibits a recognition quantity of four weak signals; in terms of recognition quality, the three-dimensional weak demand signal recognition model based on domain ontology can exclude non-demand signals such as “charging technology”, while keyword analysis methods cannot. Overall, the model proposed in this paper has higher sensitivity.

This paper proposes a novel method for identifying weak demand signals that considers the frequency of the signal's novelty, depth and relevance to the target demand. To verify its effectiveness, customer review data for new energy vehicles is used. The results provide a theoretical reference for formulating government policies and identifying weak demand signals for businesses.

This paper aims to systematically investigate the direct and indirect effects of four types of support – peer instrumental support, peer emotional support, platform business support and platform communication support – on seller trade volume in social commerce. It also aims to uncover the path of support-to-sales of the seller from a platform perspective and provides a more complete picture of the social commerce phenomenon.

This paper uses multi-source data including primary survey data and secondary data on trade volume to test the hypotheses. PROCESS mediation model is used to analyze the multi-source data set.

This study finds that the positive effects of peer instrumental support, platform business support and platform communication support on seller trade volume are fully mediated by seller collaborative information exchange. Also, peer emotional support has a significant negative effect on seller trade volume and collaborative information exchange can serve as a buffer to mitigate the negative effect.

The authors provide new insights into what types of support are or are not conducive to improving transaction volume of individual sellers and highlight the mediating role of seller information exchange in this value generation process in social commerce. These findings advance current knowledge of how seller interactions increase value in social commerce. The chosen research setting may limit the generalizability of the findings of this study.

This paper offers valuable implications for social commerce platforms on how to better serve their sellers to achieve high growth. Specifically, the findings suggest that platforms should encourage instrumental support and information exchange among peer sellers. In addition, platforms should expand seller support from a single-focus on sellers’ business to a dual-focus on both sellers’ business and socialization in social commerce.

This paper fulfills an identified need to study how sellers can better derive value from the social interactions and how social commerce platforms can effectively influence transactions, support sales and serve as a selling platform.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

Under-race lubrication can increase the amount of lubricating oil entering a bearing and greatly improve lubrication and cooling effects. The oil-air two-phase flow and heat transfer characteristics inside a ball bearing with under-race lubrication play a key role in lubrication and cooling performance. The purpose of this paper is to study these two characteristics, and then provide guidance for lubrication and heat dissipation of bearing with under-race lubrication.

In this paper, a simplified three-dimension heat transfer model of ball bearing with under-race lubrication is established; the coupled level set volume of fluid method is used to track the oil-air two-phase flow, and the Palmgren method is used to calculate the heat generation. The influence of rotation speed and inlet velocity on oil volume fraction, temperature and convection heat transfer is investigated. A temperature test for under-race lubrication is carried out.

Because of the centrifugal force, lubricating oil is located more on the outer ring raceway. As the rotation speed decreases and the inlet velocity increases, the oil volume fraction increases and the temperature decreases. Furthermore, the area with high oil volume fraction has a large convection heat transfer coefficient and low temperature. The error between the simulation temperature and the test temperature is within 10%.

The research on the temperature field and convection heat transfer characteristics of under-race lubrication ball bearings at different rotation speeds and inlet velocities is rarely involved.

Exploring the determinants of a winery brand reputation (BR) and how those determinants interact is vital for the sustainable development of wineries as well as the growth of the wine industry as a whole. This study aims to test an integrated model to better understand the observed measurement constructs of winery brand reputation, including collective reputation (CR), wine label (WL), expert opinion (EO), social media advertising (SMA) and consumer wine knowledge (CWK).

In-depth interviews, an expert panel review and a pilot study were conducted to examine and improve the observed variables. A questionnaire survey was conducted as the main data source for the study. A total of 616 valid questionnaire responses were collected from 102 cities in mainland China and Hong Kong, Macao and Taiwan from December 2021 to April 2022. Structural equation modeling was conducted for the data analysis.

This study supported 9 of the 18 proposed theoretical hypotheses. WL, EO and SMA had positive effects on BR. CWK was found to have a moderating effect on the relationship between expert opinions/social media advertising and brand reputation.

The results of this study can guide wine practitioners, researchers and administrators in brand development, label regulation and consumer education.

To the best of the authors’ knowledge, this is the first attempt to examine the determinants of winery brand reputation among Chinese wine consumers. This study explains the mechanism of winery brand reputation, demonstrating the dynamics and effects of the observed measurement constructs on brand reputation.

This study aims to research the time-varying mesh stiffness (TVMS) model for orthogonal face gear drives considering elastohydrodynamic lubrication (EHL) and provide a theoretical basis for understanding the dynamic characteristics of face gear drives.

Considering EHL, a novel model is proposed to calculate the TVMS of orthogonal face gears using the deformation compatibility condition. First, the tooth surface equations of orthogonal face gears are derived according to the tooth surface generation principle. Then, the oil film thickness on the tooth surface of face gears is obtained by solving the governing equations of EHL. Furthermore, the proposed model is used to calculate the TVMS of face gears along the mesh cycle and is verified. Finally, the effects of module, tooth number of shaper cutter and pressure angle on mesh stiffness are analyzed.

The results indicate that when the contact ratio is greater than 1 and less than or equal to 2, the TVMS of face gears exhibits a phenomenon of double-single tooth alternating meshing where sudden changes occur. As the module increases, the overall mesh stiffness of face gears increases, and the magnitude of the sudden change at the moment of single-double tooth alternating meshing gradually increases. As the tooth number of shaper cutter and pressure angle increase, so does the TVMS of face gears. When the effect of oil film is considered, the calculated TVMS of face gears slightly increases overall and the increase in average oil film thickness leads to a rise in the TVMS. This study provides a theoretical basis for understanding the dynamic characteristics of face gear drives.

This study’s originality and value lie in its comprehensive approach, which includes conducting analysis based on loaded tooth contact, considering the influence of elastohydrodynamic lubrication, proposing a novel analytical–finite–element model, calculating TVMS of face gears, verifying the proposed model and analyzing the effects of typical structural parameters and oil film thickness.

This paper aims to propose a numerical method to calculate the convective heat transfer coefficient of spiral bevel gears under the condition of splash lubrication and to reveal the lubrication and temperature characteristics between the gears and the oil-air two-phase flow.

Based on computational fluid dynamics, the multiple reference frames (MRF) method was used to simulate the rotational characteristics of gears and the motions of their surrounding fluid. The lubrication and temperature characteristics of gears were studied by combining the MRF method with the volume of the fluid multiphase flow model.

The convective heat transfer coefficient can be improved by increasing the rotational speed and the oil immersion depth. Moreover, the temperature of the tooth surface having a large convective heat transfer coefficient is also found to be low. A large convection heat transfer coefficient could lead to a good cooling effect.

This method can be used to obtain the convective heat transfer coefficient values at different meshing positions, different radii and different tooth surface positions. It also can provide research methods for improving the cooling effect of gears under the condition of splash lubrication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2020-0233/