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The purpose is to investigate how to create a new premium new energy vehicle brand.

This study employed a two-stage design. Firstly, a single-case study of NIO was undertaken to execute a thematic analysis, from which propositions were proposed and a theoretical model was constructed. Subsequently, quantitative data were collected through the questionnaire method to empirically test the model developed in the first stage.

NIO creates great user experience through four aspects: product, service, digital touchpoints and lifestyle. Functional experience is shaped by product and digital touchpoints, while emotional experience is affected by service and lifestyle. NIO wins extremely high user satisfaction through great user experience. User satisfaction is affected by both functional and emotional experience. Taking extremely satisfied users as the core, NIO leverages word-of-mouth recommendations to increase brand awareness and build premium brand image, so as to achieve high performance in the long term.

This study contributes to the literature by proposing and testing a theoretical model of creating a new premium new energy vehicle brand. It highlights the significance of emotional factors in the process of creating a new premium brand. It proposes employing the “ripple model” to translate user satisfaction into financial performance. It provides a three-step guide to creating a new premium brand for managers.

The literature on job crafting has paid scant attention to the role of skill variety, one dimension of job characteristics, as a predictor of employee job crafting. By integrating regulatory focus and social exchange perspectives with job crafting literature, the authors investigate how skill variety promotes employee job crafting and the moderating roles of employee's promotion focus and procedural justice climate.

The authors conducted two questionnaire surveys, one with a sample of 205 employees from a variety of organizations in China, and the other one with a sample of 265 employees within 44 work groups at a state-owned enterprise in China, to examine the hypotheses.

Results suggest that a high level of skill variety within a job promotes employee job crafting, that such an effect is stronger when the employee's promotion focus is high rather than low, and that procedural justice climate mitigates the negative influence of a low level of promotion focus.

The authors' findings suggest that both self-regulatory and social exchange mechanisms play a critical role in promoting employee job crafting when individuals are engaged in jobs that entail a high level of skill variety.

This study investigates the effect of ownership structure – ownership concentration and firm ownership – on outward foreign direct investment (OFDI) by emerging market multinational enterprises (EMNEs), and further explores the moderating effects of international experience and migrant networks on this relationship.

Data of Chinese MNEs listed on Shenzhen and Shanghai stock exchanges between 2005 and 2016 are used. The empirical analysis is based on the negative binomial regression model.

The empirical results reveal a significant inverted-U relationship between ownership concentration and OFDI by EMNEs. State ownership is found to have a positive effect on OFDI by EMNEs. Both international experience and migrant networks strengthen the inverted-U relationship between ownership concentration and OFDI as well as the positive effect of state ownership on OFDI by EMNEs.

EMNEs need to maintain a moderate ownership concentration when conducting OFDI, and they are supposed to make full use of their own international experience and focus on migrant networks of the host country. Policy-makers in emerging economies need to better create a fair business environment for enterprises.

Combining agency theory and the resource-based view, this study integrates ownership structure, firm-level heterogeneous resources – international experience and country-level heterogeneous resources – migration networks into a framework to study OFDI by EMNEs, which expands the scope of research in international business.

The purpose of this paper is to explore to which extent the quality of social media short text without extensions can be investigated and what are the predictors, if any, of such short text that lead to trust its content.

The paper applies a trust model to classify data collections based on metadata into four classes: Very Trusted, Trusted, Untrusted and Very Untrusted. These data are collected from the online communities, Genius and Stack Overflow. In order to evaluate short texts in terms of its trust levels, the authors have conducted two investigations: (1) A natural language processing (NLP) approach to extract relevant features (i.e. Part-of-Speech and various readability indexes). The authors report relatively good performance of the NLP study. (2) A machine learning technique in more precise, a random forest (RF) classifierusing bag-of-words model (BoW).

The investigation of the RF classifier using BoW shows promising intermediate results (on average 62% accuracy of both online communities) in short-text quality identification that leads to trust.

As social media becomes an increasingly new and attractive source of information, which is mostly provided in the form of short texts, businesses (e.g. in search engines for smart data) can filter content without having to apply complex approaches and continue to deal with information that is considered more trustworthy.

Short-text classifications with regard to a criterion (e.g. quality, readability) are usually extended by an external source or its metadata. This enhancement either changes the original text if it is an additional text from an external source, or it requires text metadata that is not always available. To this end, the originality of this study faces the challenge of investigating the quality of short text (i.e. social media text) without having to extend or modify it using external sources. This modification alters the text and distorts the results of the investigation.

The purpose of this paper is to explore the relationship between top management team (TMT) internal social capital and strategic decision-making speed, and further explore role of TMT behavioral integration in their relationship. It reveals how TMT internal social capital impacts strategic decision-making speed.

On the basis of the social capital theory and upper echelons theory, at first, a model about TMT internal social capital and strategic decision-making speed is proposed by exploratory case study. Then, the data obtained via questionnaire from 67 TMTs by software SPSS 19.0 and AMOS 17.0 are analyzed, and the theoretical hypotheses as mentioned above are verified.

The empirical study found that different dimensions of TMT internal social capital have significant positive impact on TMT behavioral integrity; TMT behavioral integrity has significant positive impact on strategic decision-making speed; and TMT behavioral integrity as an intermediary variable played a brokering role in the relationship between TMT internal social capital and strategic decision-making speed.

The study enriches the empirical test on the relationship between TMT internal social capital and decision speed, thereby helping the authors further understand how to improve the speed of strategic decision making in TMT.

The aggregate index and per capita index have different meanings for some countries or regions. CO₂ emissions per capita matters for China because of its huge population. Therefore, this study aims to deepen the understanding of Kuznets curve from the perspective of CO₂ emissions per capita. In this study, mathematical formulas will be derived and verified.

First, this study verified the existing problems with the environmental Kuznets curve (EKC) through multiple regression. Second, this study developed a theoretical derivation with the Solow model and balanced growth and explained the underlying principles of the EKC’s shape. Finally, this study quantitatively analyzed the influencing factors.

The CO₂ emission per capita is related to the per capita GDP, nonfossil energy and total factor productivity (TFP). Empirical results support the EKC hypothesis. When the proportion of nonfossil and TFP increase by 1%, the per capita CO₂ decrease by 0.041 t and 1.79 t, respectively. The growth rate of CO₂ emissions per capita is determined by the difference between the growth rate of output per capita and the sum of efficiency and structural growth rates. To achieve the CO₂ emission intensity target and economic growth target, the growth rate of per capita CO₂ emissions must fall within the range of [−0.92%, 6.1%].

Inspired by the EKC and balanced growth, this study investigated the relationships between China’s environmental variables (empirical analysis) and developed a theoretical background (macro-theoretical derivation) through formula-based derivation, the results of which are universally valuable and provide policymakers with a newly integrated view of emission reduction and balanced development to address the challenges associated with climate change caused by energy.