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The purpose of this paper is to examine the impacts of companies’ voluntary adoption of the General Data Protection Regulation (GDPR) as well as the readability of privacy statements on US customers’ intention to disclose information and their trust in a company.

Building on the construal level theory and psychological distance, the authors conduct a 2 × 2 + 2 between-participants experiment with 255 participants.

The findings show that a company’s voluntary adoption of the GDPR has positive effects on customers’ intention to disclose information to and their trust in that company. In addition, the effects of GDPR adoption are stronger when the adopting company’s privacy statements possess a higher level of readability.

The authors believe this study poses policy implications for the outcomes of GDPR adoption and the recent debate on both a stricter data breach and privacy regulation.

With the advancement of social economy, science and technology, nowadays, people face increasingly complex decision-making problems and ever-growing decision-associated information contents. Owing to the unique advantages of the evidence theory, evidence decision flow fits in well with the cognitive process of human beings, which provides us with an effective decision method. However, traditional evidence theories are built upon the one-dimensional evidence recognition framework, which merely reflects the reliability of information determined by evidence source and as such may fail to present the characteristic information of evidence source itself or the information required for the process of determining reliability. This greatly influences processes in decision-making, such as evidence processing and combination. This essay aims to propose a two-dimensional evidence-reasoning theory to address specific decision problems.

This article uses a two-dimensional evidence framework to reflect the process and characteristic information of evidence source based on the traditional evidence framework. As a result, evidence is processed and combined by two-dimensional modified information.

This research is of theoretical and practical significance by extending theoretical connotation, fully utilizing precise evidence information and therefore meeting the requirements of efficient and accurate decision-making performances.

The theory adds a two-dimension to modify and capture evidence on the basis of the traditional evidence framework. This proposal has significant theoretical and practical value to expand evidence theory and provides more accurate use of evidence information, higher efficiency and quality requirements and more precise decision-making.

This study aims to ensure the operation safety of high speed trains, it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time, yet the traditional tests of mechanical property can hardly meet this requirement.

In this study the acoustic emission (AE) technology is applied in the tensile tests of the gearbox housing material of an high-speed rail (HSR) train, during which the acoustic signatures are acquired for parameter analysis. Afterward, the support vector machine (SVM) classifier is introduced to identify and classify the characteristic parameters extracted, on which basis the SVM is improved and the weighted support vector machine (WSVM) method is applied to effectively reduce the misidentification of the SVM classifier. Through the study of the law of relations between the characteristic values and the tensile life, a degradation model of the gearbox housing material amid tensile is built.

The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process, and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%. The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.

The results of this study provide new concepts for the life prediction of tensile samples, and more further tests should be conducted to verify the conclusion of this research.

The purpose of this paper is to construct a multi-relational network for an online sharing platform in the age of the sharing economy, to identify the factors impacting users’ product adoption behavior and to predict consumers’ purchases of user-generated products on the platform.

The study conducted multi-relational network analyses of five different sub-networks in identifying influential factors for e-book adoption. Meanwhile, the study adopted machine learning methods with different classification algorithms and feature sets to predict users’ purchasing behaviors.

The authors found that an individual’s adoption of a product was correlated with his or her purchasing habits and collaboration with others on the online sharing platform. Through the inclusion of network features, the authors were able to build a predictive model that forecasted consumers’ purchases of user-generated e-books with reasonable accuracy.

The interdisciplinary approach used in the study can serve as a good reference for identifying factors impacting the product adoption behavior of users in the online sharing platform, through employing different sociological and computational methods.

The outcome of the study has provided important managerial implications, especially for the design of social commerce platform in the age of the sharing economy.

The authors verified the social influence impacting consumers’ product adoption behavior and shed light on the value of collaboration in the age of the sharing economy.

The study was the first to identify user-generated e-book adoption on an online sharing platform from a multi-relational network perspective. The idea and the approach supplied a new method of behavioral analysis in the context of a sharing economy.

Either buying or making is predicted by the existing literature for firms to reduce dependence. However, firms in the rapid globalization are found to adopt a pattern of buying and making. Specially, they critically rely on foreign firms for needed materials and goods, and invest in innovation against the uncertainty of potential supply disruptions simultaneously. Therefore, this paper seeks to investigate how the depth and width of supplier globalization shape firm innovation together. Moreover, the moderating effects of institutional distance and market competition are also examined in the paper.

Grounded on the resource dependence theory, this paper develops a theoretical framework and tests the proposed hypotheses by Poisson model using secondary data from 502 Chinese listed firms with foreign suppliers.

The depth of supplier globalization has a positive impact on firm innovation, while the width of supplier globalization weakens firm innovation. The depth and width of supplier globalization further interact negatively to influence firm innovation. Moreover, this relationship is enhanced when firms establish relationships with foreign firms with greater institutional distance and is weakened when firms face fiercer product competition.

The authors contribute to the literature by evidencing that the existence of foreign suppliers results in firms' enhancement of innovation to secure their operations and showing that diversifying the country origins of foreign suppliers is an effective means to reduce firms' uncertainty about supply disruption. We also advance the understanding regarding the contextual factors in which firms are more likely or less likely to manage the uncertainty about supplier globalization.

In response to the intense competition in the platform economy, e-commerce platforms are actively introducing value-added services to maintain their competitiveness. However, how effective these value-added services are in fulfilling this purpose remains unclear. This paper explores how value-added services can enhance e-commerce platform competitiveness, measured by both user scale and reputation, considering the effect of network externalities.

A bilateral e-commerce platform with potential high-quality sellers and low-quality sellers on one side and potential buyers on the other side was chosen as research setting. Game theory models are constructed to simultaneously consider the behaviors of all actors (including sellers, buyers and the platform).

On the one hand, to increase the seller scale, basic services play a substituting role in determining the effect of value-added services. On the other hand, to increase the buyer scale and improve platform reputation, basic services play a fundamental role in determining the effect of value-added services. Furthermore, the higher the loss rate of the product value, the bigger the room for providing value-added services. With increasing loss rate of the product value, participating buyers who are attracted by value-added services are the fastest growing indicators; this indicates that the most significant effect of value-added services is its increase in the buyer scale.

Basic services determine the lower limit of platform competitiveness, while value-added services set the upper limit. The results of this paper can instruct different types of platforms to enhance their competitiveness in different ways.

(1) While previous studies on how to enhance platform competitiveness only considered scale or reputation separately, this paper applies a new perspective of platform competitiveness, namely the improvement of both the seller scale/buyer scale and platform reputation. (2) According to the characteristics of bilateral platforms, game theory models are constructed to explore how value-added services can enhance platform competitiveness considering both positive and negative network externalities. (3) The existing literature studies basic services and value-added services in a fragmented state; this paper contributes to research on value-added services by considering the mutual effect between basic and value-added services.

The extant literature recognizes that trade credit is influenced by the power imbalance between buyers and suppliers but most studies focus on either buyer power or supplier power. The purpose of this study is to investigate how buyer power and supplier power interact and jointly influence trade credit. Moreover, this study examines the moderating effects of political ties in an emerging economy context.

A research framework was developed by combining resource dependence theory and institutional theory to investigate the interactive effects of market power (i.e. market share and supplier concentration) and non-market power (i.e. political ties) on trade credit. The proposed hypotheses were empirically tested by a fixed effects model using secondary data from 2,433 listed firms in China.

The results show that a buyer firm's market share promotes trade credit but this effect is weakened by supplier concentration. Moreover, the buyer's political ties enhance the impact of market share on trade credit and attenuate the negative moderating effect of supplier concentration.

This study contributes to the trade credit and supply chain power literature by identifying the interactive effects of market share, supplier concentration and political ties in trade credit. It advances our understanding of how trade credit is jointly determined by a variety of factors in emerging economies.

The development of China's solar photovoltaic (PV) industry is in a transition period from pursuing scale and speed to focusing on efficiency and quality. “Smart PV projects” (SPVPs) were proposed by the ministries of the Chinese government in 2018 to encourage intelligent upgrading and to fill the gaps in traditional PV projects. However, only a small number of PV enterprises are in progress, and only a few SPVPs have been built. The intelligence level of China's PV projects needs to be improved. The purpose of this study is to analyze the willingness of the main participants to be involved in the intelligent upgrading of PV projects by establishing an evolutionary game model that includes three parties.

A tripartite evolutionary game model is constructed that considers PV enterprises, project owners and the government. The evolutionary stability strategies of each party and the corresponding stable conditions are obtained. The parameters that affect the decision behaviors are also analyzed.

The four stages of the intelligent upgrade of PV projects and the effects of the government subsidy strategies are examined. At different stages, adopting different measures to promote cooperation among the three parties involved is necessary. Government subsidies should be provided to PV enterprises during the initial stage and should be biased toward project owners during the intermediate stage. During the peak stage, PV enterprises constantly need to decrease project costs and improve quality and service, thus helping project owners reduce their initial investments and obtain additional gains. The government's reputation drives it to continually adopt incentive strategies.

This research focuses on the interactions among the three parties. Based on evolutionary game analysis, several conditions that facilitate the intelligent upgrading of PV projects are illustrated. Implications for different developing stages are proposed from the perspectives of each party for the decision-makers of SPVPs.

Sharing resources with stakeholders is the key for keystones to govern business ecosystems successfully. However, existing research has not paid further attention to how keystones share resources under the condition of resource sufficiency and how keystones balance resource sharing with complementors when they lack resources. Therefore, this paper aims to explore how keystones govern their business ecosystems under the conditions of resource sufficiency and resource insufficiency.

This paper adopts the single case study method. First, by adopting Gioia coding to analyze the relevant data of the case sample, this paper obtains the key concepts of the business ecosystem governance process. Then, it establishes the relationship between the concepts by analyzing the governance process of the case sample.

Under the condition of resource sufficiency, keystones under the condition of resource sufficiency, should make full use of resources to incubate more complementors, and further integrate the resources of the business ecosystem, to create more value for their business ecosystems. Under the condition of resource insufficiency, keystones should break the boundaries of business ecosystems and acquire external resources, to meet the resource needs of complementors. Subsequently, keystones should redeploy idle resources according to the actual needs of complementors, to meet the changing resource needs of complementors.

This study subdivides business ecosystem governance conditions and further constructs the business ecosystem governance process model, which provides a theoretical and practical reference for business ecosystem governance.

This study aims at developing a better understanding of the relationship between network embeddedness and incremental innovation capability and further examines the moderating effect of open innovation.

This paper adopts hierarchical regressions to validate the theoretical model and collect the patent data of the top 54 firm patentees in the smartphone industry as empirical sample. Using patent citation network data, this paper estimates the relationship between open innovation, network embeddedness and incremental innovation capability.

This paper empirically shows that structural embeddedness exerts a negative effect on incremental innovation capability, while relational embeddedness is positively related to incremental innovation capability. And open innovation strengthens the relationship between network embeddedness and incremental innovation capability.

This paper shifts the focus of the determinants of incremental innovation capability from internal factors to the external network features by exploring the linkage between network embeddedness and incremental innovation capability. A counterintuitive conclusion is that structural embeddedness shows a negative effect on firm's incremental innovation capability. Furthermore, in contrast to most previous studies, which only focus on the direct effect of open innovation on the firm's incremental innovation capability, our study examines the moderating effect of open innovation on the relationships between network embeddedness and incremental innovation capability. At last, the results provide practical guidance for firms to occupy the beneficial network positions and adopt appropriate open innovation strategies to improve their incremental innovation capability.