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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.

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/

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.

The purpose of this paper is to build a novel data envelopment analysis (DEA) model to evaluate the efficiencies of decision making units (DMUs).

Using the Choquet integrals as aggregating tool, the authors give a novel DEA model to evaluate the efficiencies of DMUs.

It extends DEA model to evaluate the DMU with interactive variables (inputs or outputs), the classical DEA model is a special form. At last, the authors use the numerical examples to illustrate the performance of the proposed model.

The proposed DEA model can be used to evaluate the efficiency of the DMUs with multiple interactive inputs and outputs.

This paper introduce a new DEA model to evaluate the DMU with interactive variables (inputs or outputs), the classical DEA model is a special form.

The purpose of this paper is to analyze the characteristics of spatio-temporal dynamics and the evolution of land use change is essential for understanding and assessing the status and transition of ecosystems. Such analysis, when applied to Horqin sandy land, can also provide basic information for appropriate decision-making.

By integrating long time series Landsat imageries and geographic information system (GIS) technology, this paper explored the spatio-temporal dynamics and evolution-induced land use change of the largest sandy land in China from 1983 to 2016. Accurate and consistent land use information and land use change information was first extracted by using the maximum likelihood classifier and the post-classification change detection method, respectively. The spatio-temporal dynamics and evolution were then analyzed using three kinds of index models: the dynamic degree model to analyze the change of regional land resources, the dynamic change transfer matrix and flow direction rate to analyze the change direction, and the barycenter transfer model to analyze the spatial pattern of land use change.

The results indicated that land use in Horqin sandy land during the study period changed dramatically. Vegetation and sandy land showed fluctuating changes, cropland and construction land steadily increased, water body decreased continuously, and the spatial distribution patterns of land use were generally unbalanced. Vegetation, sandy land and cropland were transferred frequently. The amount of vegetation loss was the largest. Water body loss was 473.6 km², which accounted for 41.7 per cent of the total water body. The loss amount of construction land was only 1.0 km². Considerable differences were noted in the rate of gravity center migration among the land use types in different periods, and the overall rate of construction land migration was the smallest. Moreover, the gravity center migration rates of the water body and sandy land were relatively high and were related to the fragile ecological environment of Horqin sandy land.

The results not only confirmed the applicability and effectiveness of the combined method of remote sensing and GIS technology but also revealed notable spatio-temporal dynamics and evolution-induced land use change throughout the different time periods (1983-1990, 1990-2000, 2000-2010, 2010-2014, 2014-2016 and 1983-2016).