Search results

Research on decision rights partitioning and its impact on platform performance has predominantly focused on single rights, leading to inconclusive results. This study is driven by a more nuanced objective of exploring diverse governance models that can enhance the performance of sharing platforms across different contexts. Rather than delegating single decision right to users, this approach partitions several essential decision rights concurrently throughout the transaction process. By examining the complex relationships between multiple decision rights partitioning and platform performance, this study identifies and explains suitable governance models that are tailored to specific contextual factors for improving the performance of sharing platforms.

Collecting data from 60 sharing platforms in China, this study employs a combination of cluster and configuration analyses to address research questions.

The study explores three strategic decision rights partitioning modes widely adopted by sharing platforms. It further identifies four governance models for sharing platforms, which are termed as conservative seller model, conservative buyer model, aggressive seller model and aggressive buyer model, related to certain contextual factors.

In addressing platform governance as key to sharing platform success, the study contributes to the literature by investigating how multiple-rights partitioning portfolios and strategic differentiation in decision rights partitioning can enhance platform performance.

As China’s industrial structure continues to upgrade and optimize, the consumption capacity of rural residents gradually improves and the role of consumption in economic growth is increasingly prominent. Against the background of weak external demand, the untapped potential of rural consumption has become a key force in expanding domestic demand. As one of the important means that the government has long relied on, fiscal support for agriculture has played a crucial role in activating the rural consumer market. This manuscript aims to explore the impact of local fiscal support for agricultural expenditure (FSAE) on rural consumption in China, as well as to examine the mediating role of the level of rural financial development.

In this manuscript, the authors use the provincial panel data of 31 provinces in China from 2000 to 2020. The data of all variables mainly come from China Statistical Yearbook and China Rural Statistical Yearbook. According to the variable selection above, 651 sample data of 31 provinces and cities across China from 2000 to 2020 are organized. In terms of methodology, multiple fixed-effects panel model is applied to regression.

Firstly, FSAE varies significantly, while rural consumption slowly but steadily rises, with a relatively stable consumption structure. Secondly, FSAE has a significant positive effect on rural consumption. Thirdly, mediation testing indicates that mechanisms such as income, uncertainty and financial development have significant positive mediating effects on rural consumption. Thirdly, there is evident regional heterogeneity in FSAE’s impact on rural consumption. The Western regions, under government fiscal support, show a more significant effect on the elevation of rural consumption levels, while the role of FSAE in optimizing the consumption structure of rural residents in eastern and central regions is more pronounced.

Firstly, a systematic examination of local FSAE and rural consumption has been conducted, enriching relevant theories. Secondly, utilizing econometric empirical methods to research the relationship between local FSAE and rural consumption provides an exploratory extension to empirical studies on rural consumption in China. This offers empirical evidence for local fiscal support in agricultural development and the promotion of rural consumption.

Data transformation has prompted enterprises to rethink their strategic development. Scholars have frequently acknowledged the vast potential value of supply chain data and realised that simply owning data resources cannot guarantee excellent innovation performance (IP). Therefore, this study focussed on the mediating and moderating issues between data-driven supply chain orientation (DDSCO) and IP. More specifically, the purpose was to explore (1) whether DDSCO promotes enterprise innovation through dynamic and improvisational capabilities and (2) how information complexity (INC) plays a moderating role between capabilities and performance.

An empirical study was performed using the results of a questionnaire survey, and a literature review was used to build the premises of this study. A sample was conducted on 296 Chinese enterprises, and the data collected were used to test the hypothesis by successive regression.

This research has implications for the theoretical development of DDSCO, as well as the dynamic capabilities (DC) and improvisation capabilities (IC) in innovation strategic literature. The empirical results show that DDSCO has a direct, positive impact on both DC and IC, which thus positively impact IP. Meanwhile, IC has a negative moderating effect on the path joining DC and IP. Conversely, IC has a positive moderating effect on the path joining IC and IP.

Although this study has limitations, it also creates opportunities for future research. The survey comes from different industries, so the possibility of unique influences within industries cannot be ruled out. Second, the authors' survey is based on cross-sectional data, which allow for more comprehensive data verification in the future. Third, this study also provides opportunities for future research, because it proves that DC and IC, as partial mediators of DDSCO and IP, can mine other paths of the data-driven supply chain in IP. For example, the perspective of the relationship between supply chain members, knowledge perspective, etc.

The research findings offer a novel perspective for enterprise managers. First, enterprises can leverage supply chain data to gain competitive advantages in innovation. Second, it is imperative for enterprises to acknowledge the significance of developing dynamic and IC. This also requires enterprises to acknowledge innovations in DDSCO necessitate a focus on dynamic and IC. Third, it is recommended that managers take into account both sides of IC and encourage enterprises to prioritise the utilisation of IC.

Empirical research results revealed how DDSCO improves IP and is an extension of digital transformation in the supply chain field, providing new opportunities and challenges for enterprise innovation. It can also expand the enterprise's understanding of DDSCO. Second, based on resource-based theory, it is possible to develop and test theoretical arguments regarding the importance of dynamic and IC as intermediaries in the DDSCO-IP. Third, the authors conducted simulations of highly dynamic data environments to develop and test theoretical arguments about the importance of IC as a moderator of capabilities-performance relationships.