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Consumer adoption of shared products is a prerequisite for successful commercialization. The purpose of this paper is to explore what innovative characteristics of entity shared products can accommodate consumers' concerns and are likely to motivate adoption of consumers.

This paper used a conceptual model that combined the innovation diffusion theory and technology acceptance model to explore shared products adoption. It identified the direct and indirect effects of perceived app ease of use/online, perceived convenience of access/offline, perceived utility advantages and personal innovativeness on shared products adoption intention. Structural equation modeling was used for analyzing the questionnaire data from a sample of 479 users who used entity shared products such as shared cars, shared bicycles and shared power banks for mobile phones.

The empirical tests indicate that perceived utility advantages based on market innovation, perceived accessibility of usage rights based on technology innovation (including perceived app ease of use/online and perceived convenience of access/offline) and consumer personal innovativeness are the key factors affecting consumer adoption.

This paper constructs an innovation-adoption coupling model of entity shared products to understand shared products usage. The findings provide useful practical guidance for the design and development of shared products and “usage rights economy” business applications.

The purpose of this paper is to explore the effect of consumers' perceived product promotion and atmosphere promotion strategies on their participation intention, and the possible interaction between product promotion and atmosphere promotion strategies on their participation intention in online shopping festivals.

This paper conceptualized consumer perception of product promotion strategies of online shopping festivals as Perceived Temptation of Price Promotion, Perceived Categories Richness of Promotion and Perceived Fun of Promotion Activities and atmosphere promotion strategies as Perceived Contagiousness of Mass Participation. Based on the Stimulus-Response Theory, this study constructed an influencing model of promotion strategies on consumer participation intention in online shopping festivals. Structural equation modeling with partial least squares was used for analyzing the data from a sample of 495 consumers to test the proposed hypotheses.

The results showed that Perceived Temptation of Price Promotion, Perceived Categories Richness of Promotion, Perceived Fun of Promotion Activities and Perceived Contagiousness of Mass Participation significantly and positively affect consumer Participation Intention; Perceived Contagiousness of Mass Participation plays a moderating role in the effect of Perceived Temptation of Price Promotion on Participation Intention.

This study is the first empirical attempt to examine the moderating role of atmosphere promotion between product promotion and consumer participation intention in online shopping festivals. The findings provide theoretical basis and practical guidance for e-commerce platforms and merchants for improving their online shopping festival promotion strategies.

Singles’ Day Online Shopping Festival was originated in China and is characterized by gathering promotions to create consumer shopping atmosphere. Its rapid rise has affected Asia and the world, becoming the world’s largest shopping festival beyond Black Friday. The success of Singles’ Day Online Shopping Festival demonstrates Chinese experience of online shopping festive atmosphere marketing. The purpose of this paper is to explore the influence of Singles’ Day Online Shopping Festival atmosphere and Chinese cultural background, especially Confucian values, on Chinese consumers’ purchase intention in Singles’ Day Online Shopping Festival.

This paper conceptualized consumers’ most perceptive atmosphere characteristics as the three dimensions of perceived economic temptation, perceived festival entertainment and perceived mass participation. Taking Confucian values as moderators, based on the stimulus-response theory, this study constructed an influencing factor model of consumer purchase intention in online shopping festival, collected data of 398 Chinese consumers by questionnaire, and used structural equation modeling for hypotheses testing.

The results showed that online shopping festival atmosphere and Confucian values affect purchase intention; the two factors of “keeping face” and “listening to others” of Confucian values play moderating roles in the effect of online shopping festival atmosphere on purchase intention.

The sample of this study was biased toward the young and well-educated consumers; besides, this study focused on young consumers’ purchase intention of online shopping festival, rather than their actual consumption behaviors.

Confucian values have deeply influenced China and other Asian countries, especially East and Southeast Asian countries. Meanwhile they are the fastest growing regions of e-commerce in the world, the paper provides theoretical basis and reference for the e-commerce enterprises in the Confucian cultural societies to improve the atmosphere marketing of online shopping festivals, and attracts consumers to shop online, having particular significance in shedding light on the Asian “e-commerce Miracle.”

This study found that Singles’ Day purchase intention is dependent on online shopping festival atmosphere stimuli, Confucian values and their interaction. Marketing researchers should consider both online shopping festival atmosphere as a marketing tool and the influence of consumer cultural values, so as to help e-commerce platforms and e-commerce merchants establish shopping festival marketing strategies that suit consumers’ cultural values.

This paper addressed an interesting practical issue related to the effects of online shopping festival atmosphere stimuli and cultural values on consumer online purchase intention.

The purpose of this paper is to establish the grey relational estimating model of soil pH value based on hyper-spectral data.

As to the uncertainty of the factors affecting the soil pH value estimation based on hyper-spectral, the grey weighted relation estimation model was set up according to the grey system theory. Then the linear regression correction model is established according to the difference and grey relation degree information between the estimated samples and their corresponding pattern. At the same time, the model was applied to Hengshan county of Shanxi province.

The results are convincing: not only that the linear regression correction model of grey relation estimating pattern of soil pH value based on hyper-spectral data is valid, but also the model’s estimating accuracy is higher, which the corrected average relative error is 0.2578 per cent, and the decision coefficient R²=0.9876.

The method proposed in the paper can be used at soil pH value hyper-spectral inversion and even for other similar forecast problem.

The paper succeeds in realising both the soil pH value hyper-spectral grey relation estimating pattern based on the grey relational theory and the correction model of the estimating pattern by using the linear regression.

In order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees.

Based on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R²) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective.

The model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.