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Given that women around the world face more exclusion in terms of limited access to economic and innovation activities than men do, it is understandable that some female-led start-ups in weak institutional environments engage in bribery to help reduce the difficulties they encounter in the face of intense business competition. However, the link between bribery and product innovation performance is unclear. The purpose of this study is to investigate the relationship between bribery and product innovation performance for female-led start-ups, as well as the roles of institutional support and self-control in this link.

This empirical study evaluates survey data from female-led manufacturing start-ups in China's Yangtze River Delta region to investigate the relationship between bribery and product innovation performance.

This research shows that bribery has an inverted U-shaped impact on product innovation performance in female-led manufacturing start-ups, meaning that the product innovation performance of these firms initially increases but then decreases as the bribery intensity (i.e. the frequency and amount of bribes) increases. The authors also focus on the roles of institutional support and self-control in this link, where the authors find that this relationship is steeper for firms with strong institutional support, as well as for individual female entrepreneurs who have high levels of self-control.

The findings of this study indicate that policymakers should undertake efforts to improve institutional quality (e.g. increasing clarity around decisions, providing more institutional support, etc.) and to guide female entrepreneurs to cultivate higher levels of self-control, as such efforts would reduce the appeal of, and the opportunity for, bribery.

To date, very few studies focus specifically on female-led enterprises in the field of bribery research. The research findings presented here on the effect of bribery in female-led start-ups on firm product innovation performance are useful to researchers, policymakers and businesspeople, as they provide a better understanding of bribery in female-led start-ups in China, which can also be extrapolated to encompass other transition economy contexts.

This study aims to construct a theory of multi-dimensional organizational innovation cultures and innovation performance in transitional economies and explore the moderating effect of team cohesion on this theoretical relationship.

Using data collected from 175 manufacturing firms in transitional economies, this study constructs a new theory framework of multi-dimensional organizational innovation cultures (knowledge sharing, organizational innovation atmosphere, team decision-making and organizational change) and firms’ innovation performance and also explores the moderating effect of team cohesion on this theoretical relationship.

The findings show that there are positive relationships between knowledge sharing, organizational innovation atmosphere, team decision-making, organizational change and innovation performance of firms. Furthermore, team cohesion plays a positive moderating role in this relationship.

It extends the general understanding of multi-dimensional organizational cultures management in the context of transition economies by exploring the differences between the Chinese and Vietnamese firms in terms of the impact of organizational innovation culture on innovation performance.

This study constructs a new theory framework of multi-dimensional organizational innovation cultures along the four dimensions of knowledge sharing, organizational innovation atmosphere, team decision-making and organizational change. These factors together have rarely been examined before. Hence, the findings extend existing research on organizational cultures management. Moreover, a new idea for this study is that the authors consider team cohesion as a moderating variable between organizational innovation culture and innovation performance of firms, hence providing both theoretical discussion and empirical validation of the impact of team cohesion on this relationship. It thus extends existing research on the team theory.

The purpose of this paper is to achieve intelligent superstore site selection. Yonghui Superstores partnered with Cardinal Operations to incorporate a tremendous amount of site-related information (e.g. points of interest, population density and features, distribution of competitors, transportation, commercial ecosystem, existing own-store network) into its store site optimization.

This paper showcases the integration of regression, optimization and machine learning approaches in site selection, which has proven practical and effective.

The result was the development of the “Yonghui Intelligent Site Selection System” that includes three modules: business district scoring, intelligent site engine and precision sales forecasting. The application of this system helps to significantly reduce the labor force required to visit and investigate all potential sites, circumvent the pitfalls associated with possibly biased experience or intuition-based decision making and achieve the same population coverage as competitors while needing only half the number of stores as its competitors.

To our knowledge, this project is among the first to integrate regression, optimization and machine learning approaches in site selection. There is innovation in optimization techniques.

Carbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.

This paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO₂ and PM_2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.

Results suggest that the carbon trading mechanism induces 2.7% of CO₂ emission reduction and 6.7% of PM_2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.

This paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.

As of December 2021, WeChat had more than 1.2 billion active users worldwide, making it the most active online social media in mainland China. The term social commerce is used to describe new online sales through a mix of social networks and/or peer-to-peer communication or marketing strategies in terms of allowing consumers to satisfy their shopping behaviour through online social media. Thus, given the numerous active users, the development of online social media and social commerce on WeChat is a critical issue of internet research.

This empirical study takes WeChat as the online social media research object. Questionnaires for WeChat users in China were designed and distributed. All items are designed as nominal and ordinal scales (not Likert scale). The obtained data was put into a relational database (N = 2,342), and different meaningful patterns and rules were examined through data mining analytics, including clustering analysis and association rules, to explore the role of WeChat in the development of online social media and social commerce.

Practical implications are presented according to the research findings of meaningful patterns and rules. In addition, alternatives to WeChat in terms of further development are also proposed according to the investigation findings of WeChat users’ behaviour and preferences in China.

This study concludes that online social media, such as WeChat, will be able to transcend the current development pattern of most online social media and make good use of investigating users’ behaviour and preferences, not only to stimulate the interaction of users in the social network, but also to create social commerce value in social sciences.

Based on the perspective of knowledge management capability, this paper aims to reveal the internal mechanism of the digital empowerment of mobile social platforms to improve the operational performance of Chinese traditional retail enterprises. Such improvements have crucial theoretical value and practical implications for Chinese traditional retail enterprises to achieve transformation and sustainable development.

This study applied the typical analysis method, selected China’s leading mobile social platform, WeChat, as a typical case, and observed and analyzed the public data of the traditional retail industry and social platforms and interviews with relevant enterprises. On this basis, this study used the inductive and deductive methods of qualitative research to conduct an in-depth analysis of the mechanism by which WeChat’s digital empowerment improves the operational performance of Chinese traditional retail enterprises. It also discussed the critical role and path knowledge management capabilities play in this mechanism.

This research demonstrated that mobile social platforms empower Chinese traditional retail enterprises to build diversified digital channels, enhance the knowledge acquisition capability of enterprises and thus improve their performance; empower Chinese traditional retail enterprises to build digital community networks, enhance the knowledge diffusion capability of enterprises and thus improve their performance; and empower Chinese traditional retail enterprises to integrate online and offline businesses, enhance the knowledge integration capability of enterprises and thus improve their performance.

This study clarifies the internal mechanism of how the digital empowerment of mobile social platforms can improve the performance of Chinese traditional retail enterprises. This mechanism implies that knowledge management capabilities (knowledge acquisition, diffusion and integration capability) are the underlying logic for Chinese traditional retail enterprises to achieve higher performance levels. This has important practical implications for managers of Chinese traditional retail enterprises to leverage the digital infrastructure of mobile social platforms to achieve the sustainable development of enterprises.

This study provides an in-depth analysis of how the traditional retail industry uses digital social platforms to improve operational performance from the perspective of knowledge management capabilities, which can further promote the theoretical research and practical development of digitalization and knowledge management. At the same time, this study explored the research on the operational performance of Chinese traditional retail enterprises from the perspective of knowledge management capabilities and expanded the research on knowledge management in related fields. The authors have initially sorted out the impact of knowledge management capabilities on the operational performance of Chinese traditional retail enterprises in the digital era. This will help better understand the role and function of knowledge management in strategic transformation and expand the application of knowledge management theory.

In this paper, we study a partially linear dynamic panel data model with fixed effects, where either exogenous or endogenous variables or both enter the linear part, and the lagged-dependent variable together with some other exogenous variables enter the nonparametric part. Two types of estimation methods are proposed for the first-differenced model. One is composed of a semiparametric GMM estimator for the finite-dimensional parameter θ and a local polynomial estimator for the infinite-dimensional parameter m based on the empirical solutions to Fredholm integral equations of the second kind, and the other is a sieve IV estimate of the parametric and nonparametric components jointly. We study the asymptotic properties for these two types of estimates when the number of individuals N tends to ∞ and the time period T is fixed. We also propose a specification test for the linearity of the nonparametric component based on a weighted square distance between the parametric estimate under the linear restriction and the semiparametric estimate under the alternative. Monte Carlo simulations suggest that the proposed estimators and tests perform well in finite samples. We apply the model to study the relationship between intellectual property right (IPR) protection and economic growth, and find that IPR has a non-linear positive effect on the economic growth rate.

High-tech enterprises, as the national innovation powerhouses, have garnered considerable interest, particularly regarding their technological innovation capabilities. Nevertheless, prevalent research tends to spotlight the impact of individual factors on innovative behavior, with only a fraction adopting a comprehensive viewpoint, scrutinizing the causal amalgamations of precursor conditions influencing the overall innovation proficiency of high-tech enterprises.

This paper employs a hybrid approach integrating necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) to examine the combinatorial effects of antecedent factors on high-tech enterprises' innovation output. Our analysis draws upon data from 46 listed Chinese high-tech enterprises. To promote technological innovation within high-tech enterprises, we introduce a novel perspective that emphasizes technological innovation networks, grounded in a network agents-structure-environment framework. These antecedents are government subsidy, tax benefits, customer concentration, purchase concentration rate, market-oriented index and innovation environment.

The findings delineate four configurational pathways leading to high innovative output and three pathways resulting in low production.

This study thereby enriches the body of knowledge around technological innovation and provides actionable policy recommendations.

This paper aims to propose and demonstrate a novel surface plasmon resonance (SPR)-sensing approach by using the fundamental mode beam based on a graded index multimode fiber (GIF). The proposed SPR sensor has high sensitivity and controllable working dynamic range, which expects to solve the two bottlenecks of fiber SPR sensor, including low sensitivity and the difficulty in multichannel detection.

The low-order mode of the GIF to SPR sense, which keeps the sensitivity advantage of the single-mode fiber SPR sensor, is used. By using this new SPR sensor, the effect of light incident angle and gold film thickness on working dynamic range was studied. According to the study results, the smaller is the incident angle, the larger is the SPR working dynamic range and the longer is the resonance wavelength with a fixed gold film thickness; the larger is the gold film thickness, the longer is the resonance wavelength with a fixed grinding angle. After the parameter optimization, the sensitivity of these two parameter-adjusting methods reach 4,442 and 3031 nm/RIU.

When the grinding angle of the GIF increases, the dynamic range of the resonance wavelength increases and has a redshift, sensitivity increases, and the resonance valley becomes more unobvious with a fixed gold film thickness. Similarly, when gold film thickness increases, the dynamic range of the resonance wavelength increases and has a redshift, sensitivity increases, and the resonance valley becomes more unobvious with a fixed grinding angle. These adjusting performance aforementioned lay the foundation for solving of the fiber-based SPR multichannel detection and increasing of the fiber-based SPR sensor sensitivity, which has a good application value.

Following are the learning outcomes: to understand how the tools and frameworks of strategic analysis can be applied to understand the evolution of value creation and capture in the FMCG industry; to analyze the core competencies of a company and understand their relevance in this fast-changing industry; to understand how to evaluate the pros and cons of a certain strategy and business model; and to develop strategic recommendations.

The case series traces the developments in China’s FMCG industry from the early 2010s to 2017, in general, and the efforts of Beijing WinChannel Software Technology Co., Ltd. (WinChannel) and its affiliated company, Huixiadan, in their attempt to apply new digital technologies to transform the traditional trade channel, in particular. The decision point of Case A, in early 2015, is how WinChannel can help improve the reach and efficiency of the traditional trade channel and wonders if the emerging online/mobile B2B FMCG platforms are the right solution for the increasingly digitized FMCG retail industry in China. The decision point of Case B, at the end of 2017, is how could Huixiadan’s business model be sustainable and what it should do to withstand the competitive threats even as it tries to exploit opportunities in the traditional FMCG industry in China.

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CSS: 11: Strategy.