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The purpose of this paper is to examine the effectiveness of an improved dummy variables control grey model (DVCGM) considering the hysteresis effect of government policies in China's energy intensity (EI) forecasting.

Energy consumption is considered as an important driver of economic development. China has introduced policies those aim at the optimization of energy structure and EI. In this study, EI is forecasted by an improved DVCGM, considering the hysteresis effect of energy-saving policies of the government. A nonlinear optimization method based on particle swarm optimization (PSO) algorithm is constructed to calculate the hysteresis parameter. A one-step rolling mechanism is applied to provide input data of the prediction model. Grey model (GM) (1, N), DVCGM (1, N) and ARIMA model are applied to test the accuracy of the improved DVCGM (1, N) model prediction.

The results show that the improved DVCGM provides reliable results and works well in simulation and predictions using multivariable data in small sample size and time-lag virtual variable. Accordingly, the improved DVCGM notes the hysteresis effect of government policies and significantly improves the prediction accuracy of China's EI than the other three models.

This study estimates the EI considering the hysteresis effect of energy-saving policies in China by using an improved DVCGM. The main contribution of this paper is to propose a model to estimate EI, considering the hysteresis effect of energy-saving policies and improve forecasting accuracy.

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.

Since 2000, China, along with the USA, UK, France, Japan and many other developed countries have drawn up new blueprints for the development of a marine economy. At present, international marine economics research has entered into a new period of development, and the research methods of ocean econometrics are becoming more complex and mature. The purpose of this paper is to review the progress of international marine econometrics research and gives the development direction of marine econometrics.

The Web of Science core collection database was utilized, harvesting data from 1996 to May 2018, measuring the marine economy research from 1,489 articles as its sample, using CiteSpace visualization analysis tools.

Mapping the knowledge map from annual international marine economic metrology, literature identification, keywords, involving disciplines and related journals, countries (regions) and research and analyzing the research status of reveals the research frontiers of international marine economy measurement (learning) by using CiteSpace.

The conceptions and characteristics of marine econometrics are defined and analyzed, and the theoretical method of marine econometrics is sorted out. Mapping the knowledge diagram of marine econometrics and discussing the research status of international marine economics, and clarifying the existing problems, future opportunities and challenges of international marine econometrics research.

The purpose of this paper is to explore a descriptive framework for a more structured and objective evaluation of the risk situation of textile and apparel, also to find the best set of methods or optimal scientific grounds for the safety evaluation of textile and apparel.

Risk analysis theory is used to analyze potential hazard of textile and apparel, weight is given to risk indicators using subjective and objective weighting method, respectively, grading standards of safe risk of textile and apparel is made. Finally a safety risk assessment model of textile and apparel based on support vector machine (SVM) is built, and empirical analysis is also made.

Quantitative and highly reliable evaluation of textile and apparel risks, relatively easy grading classification and simplicity in operating the evaluation process are the advantages that promote the application of risk assessment model based on SVM for textile and apparel, and empirical analysis showed considerably good applicability.

The research is useful to ensure safety textile and apparel in market, also contributing to the sustainable development of textile industries in future.

SVM as a risk assessment method provided safety evaluation to toxic and harmful substance and small parts in textile and apparel, which can be an effective tool to monitor textile and apparel safety.

In practical engineering, oil filters often work under asymmetric cyclic loading. In order to improve the prediction accuracy of fatigue life of the oil filters under asymmetric cyclic loading, the effect of strain ratio and low cycle fatigue plastic deformation on fatigue life need to be considered. This paper aims to discuss the aforementioned objective.

First, strain-controlled fatigue tests with strain ratios of 0, 0.5 and −1 were carried out on the oil filter material 2A70-T6 aluminum alloy, and the test data were used to obtain strain fatigue life curves at three strain ratios. Then, based on the idea of the constant life curve method, the average value of the ratio of the strain amplitude corresponding to different strain ratios under the same partial life was defined as the strain ratio factor. Finally, the elastic-plastic factor was modified by the strain ratio factor, and a new fatigue life prediction model considering the effect of strain ratio was proposed.

The proposed model was validated, respectively, by fatigue test data of 2A70-T6 aluminum alloy, 2124-T851 aluminum alloy and oil filter and the results of the proposed model were compared with the Coffin–Manson equation, Morrow model and Smith–Watson–Topper (SWT) model, showing that the proposed model had higher applicability and accuracy.

In this work, a strain ratio factor is established based on the idea of the constant life curve method, and the strain ratio factor is used to modify the introduced elastic-plastic factor, and then a new fatigue life prediction model considering the influence of strain ratio and low cycle fatigue plastic deformation on material fatigue damage accumulation is proposed. The results show that the prediction results of the proposed model are in good agreement with the experimental data, and the proposed model has good fatigue life prediction ability considering the influence of strain ratio and lays a foundation for the fatigue life prediction of the oil filter.

This paper aims to systematically examine the key notion of integration of non-market and market strategies in the increasingly popular study of corporate non-market strategies.

This paper is based on a brief literature review of the non-market strategy (NMS) research that shows the existing literature does not offer a clear and systematic account of the key notion of integration. It suggests any systematic account of integration should address at least three interrelated questions, i.e. why, what and how to integrate non-market and market strategies?

For the why question, the authors use a formal model to demonstrate that the essence of the most important type of integration synergy lies in the positive spillover or externality from non-market to market strategies. For the what question, the authors identify the contents of integration at three levels, i.e. the level of non-market environment analysis, the level of NMS choice, and the level of non-market dynamic interactions. For the how question, the authors argue that the combination of non-market and market strategies should be seamless in terms of horizontal, vertical and intentional coordination. Overall, the authors argue, only when the right contents are combined and seamlessly coordinated will there be high synergies from integration of non-market and market strategies.

Managers are advised to give non-market strategies full attention. Managers charged with non-market tasks should explore how to seamlessly coordinate non-market and market strategies in order to gain maximal synergies.

This paper is the first to examine the key notion of integration in a systematic manner. It is the first to propose a three-question solution to systematic understanding of the notion and the first to propose the seamless coordination concept and its associated three aspects of seamless coordination.

The purpose of this paper is to investigate the development of software pricing, following the advent of cloud-based business intelligence & analytics (BI & A) Software. A value-based conceptual software model is developed to ignite and structure further research.

A two-step research approach is applied. In step one, the available literature is screened and evaluated, and this is followed by ten semi-structured expert interviews. With that input, a conceptual software pricing model is designed. In step two, this model is validated and refined through discussions with representatives of the five leading business intelligence suites.

The paper sheds light on the value perception of customers and suggests a clear focus on the interaction between customers and vendors, and less on technical issues. The developed customer-centric, value-based pricing framework helps to improve pricing techniques and strategies.

The research is focused on the pricing strategy of software houses and excludes differentiations of technical specifications and functionalities.

The research can support practitioners in the field of BI & A in rethinking their pricing methods. Placing the customer at center stage can lead to lower customer churn rates, higher customer satisfaction and more pricing flexibility.

This empirical study reveals the importance of a customer-centric pricing approach in the specific case of BI & A. It can also be applied to other fast-developing sectors of the software industry.

Considering the small sample size and non-linear characteristics of historical energy consumption data from certain provinces in Southwest China, the authors propose a hybrid accumulation operator and a hybrid accumulation grey univariate model as a more accurate and reliable methodology for forecasting energy consumption. This method can provide valuable decision-making support for policy makers involved in energy management and planning.

The hybrid accumulation operator is proposed by linearly combining the fractional-order accumulation operator and the new information priority accumulation. The new operator is then used to build a new grey system model, named the hybrid accumulation grey model (HAGM). An optimization algorithm based on the JAYA optimizer is then designed to solve the non-linear parameters θ, r, and γ of the proposed model. Four different types of curves are used to verify the prediction performance of the model for data series with completely different trends. Finally, the prediction performance of the model is applied to forecast the total energy consumption of Southwest Provinces in China using the real world data sets from 2010 to 2020.

The proposed HAGM is a general formulation of existing grey system models, including the fractional-order accumulation and new information priority accumulation. Results from the validation cases and real-world cases on forecasting the total energy consumption of Southwest Provinces in China illustrate that the proposed model outperforms the other seven models based on different modelling methods.

The HAGM is used to forecast the total energy consumption of the Southwest Provinces of China from 2010 to 2020. The results indicate that the HAGM with HA has higher prediction accuracy and broader applicability than the seven comparative models, demonstrating its potential for use in the energy field.

The HAGM(1,1) is used to predict energy consumption of Southwest Provinces in China with the raw data from 2010 to 2020. The HAGM(1,1) with HA has higher prediction accuracy and wider applicability compared with some existing models, implying its high potential to be used in energy field.

Theoretically, this paper presents, for the first time, a hybrid accumulation grey univariate model based on a new hybrid accumulation operator. In terms of application, this work provides a new method for accurate forecasting of the total energy consumption for southwest provinces in China.

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.