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Innovative behavior is a microfoundation of an organization’s innovation. Knowledge workers are the main creators of innovations. With the boundaries between work and family becoming increasingly ambiguous, the purpose of this study is to explore how the work–family conflict affects knowledge workers’ innovative behavior and when such a conflict arises.

To test the theoretical model, this study collected data from a time-lagged matched sample of 214 dual-career couples. The data were analyzed with the bias-corrected bootstrapping method.

The results of this study showed that work-to-family conflict had not only a direct negative effect on knowledge workers’ innovative behavior but also an indirect effect through spouses’ within-family emotional exhaustion and knowledge workers’ family-to-work conflict. If wives’ gender role perceptions are traditional, then the indirect serial mediating effect is weakened, but if such perceptions are egalitarian, then the mentioned effect is aggravated.

In terms of organizational implications, managers could alter their approach by reducing detrimental factors such as work–family conflict to improve knowledge workers’ innovative behavior. Emotional assistance programs for both knowledge workers and their spouses can be used to prevent the detrimental effect of work–family conflict on innovative behavior. As to social implications, placing dual-career couples into a community of likeminded individuals and promoting their agreement on gender role identity will greatly reduce the negative effects of work–family conflict.

Starting from the perspective of the behavior outcome of knowledge management, this study advances the existing knowledge management literature by enriching the antecedents of knowledge workers’ innovative behavior, illuminating a spillover–crossover–spillover effect of work–family conflict on knowledge workers’ innovative behavior and identifying the boundary condition of this transmission process.

The purpose of this paper is to predict bibliometric indicators based on ARIMA models and to study the short-term trends of bibliometric indicators.

This paper establishes a non-stationary time series ARIMA (p, d, q) model for forecasting based on the bibliometric index data of 13 journals in the library intelligence category selected from the Chinese Social Sciences Citation Index (CSSCI) as the data source database for the period 1998–2018, and uses ACF and PACF methods for parameter estimation to predict the development trend of the bibliometric index in the next 5 years. The predicted model was also subjected to error analysis.

ARIMA models are feasible for predicting bibliometric indicators. The model predicted the trend of the four bibliometric indicators in the next 5 years, in which the number of publications showed a decreasing trend and the H-value, average citations and citations showed an increasing trend. Error analysis of the model data showed that the average absolute percentage error of the four bibliometric indicators was within 5%, indicating that the model predicted well.

This study has some limitations. 13 Chinese journals were selected in the field of Library and Information Science as the research objects. However, the scope of research based on bibliometric indicators of Chinese journals is relatively small and cannot represent the evolution trend of the entire discipline. Therefore, in the future, the authors will select different fields and different sources for further research.

This study predicts the trend changes of bibliometric indicators in the next 5 years to understand the trend of bibliometric indicators, which is beneficial for further in-depth research. At the same time, it provides a new and effective method for predicting bibliometric indicators.

This paper aims to investigate how information and communication technology (ICT) services and digital finance affect the development of international tourism.

The two-way fixed effect panel regression model, spatial econometric model, panel threshold regression model and panel quantile regression model are used. Data on tourism, economic and social development in 198 Chinese cities from 2011 to 2020 are analyzed.

This study finds that digital economy including ICT services and digital finance has significantly promoted the development of international tourism industry, while there is a negative spatial spillover effect. The promotion effect of international tourism increases significantly after digital innovation reaches the threshold value. International tourism is benefiting more from digital economy with the development of international tourism industry.

The development quality of international tourism industry has not been analyzed due to data limitations, and the mechanism has not been tested.

This study creatively reveals the development of international tourism industry in the digital economy era from ICT services and digital finance perspectives. This study also shows the spatial, nonlinear and asymmetric relationship between digital economy and international tourism.

The purpose of this paper is to select the optimal feature parameters to further improve the identification accuracy of cashmere and wool.

To increase the accuracy, the authors put forward a method selecting optimal parameters based on the fusion of morphological feature and texture feature. The first step is to acquire the fiber diameter measured by the central axis algorithm. The second step is to acquire the optimal texture feature parameters. This step is mainly achieved by using the variance of secondary statistics of these two texture features to get four statistics and then finding the impact factors of gray level co-occurrence matrix relying on the relationship between the secondary statistic values and the pixel pitch. Finally, the five-dimensional feature vectors extracted from the sample image are fed into the fisher classifier.

The improvement of identification accuracy can be achieved by determining the optimal feature parameters and fusing two texture features. The average identification accuracy is 96.713% in this paper, which is very helpful to improve the efficiency of detector in the textile industry.

In this paper, a novel identification method which extracts the optimal feature parameter is proposed.

The authors consider a dynamic emission-reduction technology investment decision-making problem for an emission-dependent dyadic supply chain consists of a manufacturer and a retailer under subsidy policy for carbon emission reduction. The consumers are assumed to prefer to low-carbon products and formulate a supply chain optimal control problem.

The authors adopt differential game to analyze investment strategies of cost subsidy coefficient with respect to vertical incentive of a manufacturer and a retailer. A comparison analysis under four different decision-making situations, including decentralized decision-making, centralized decision-making, maximizing social welfare, is obtained.

The results show that the economic benefit and environmental pressure have a win–win performance in centralized decision-making. In four different game models, equilibrium strategies, profits and social welfare show changing diversity and have a consistent development trend as time goes on.

The authors estimate the demand function is a linear function in this paper. According to the consumers’ preference to low-carbon products, consumer’s awareness meets the law of diminishing marginal utility like advertising goodwill accumulation. The carbon-sensitive coefficient might be a quadratic expression, which will complicate the problem and be consistent with reality.

It captures that there is a necessity to strengthen cooperation and exchange of carbon emission technology among the enterprises by simulation of different decision-makings when government granted cost subsidy.

The results provide significant guidelines for the supply chain to make decision-makings of emission-reduction technology investment and relevant government departments to determine emission subsidies costs.

An endogenous subsidies coefficient is produced by the social welfare function. Distinguished from previous study, it also considered the influences of carbon emission trade policy and consumer preference.

This study aims to investigate the most effective approach for governments and enterprises to combat desertification by considering the governance cycle. The focus is on understanding how the government can incentivize enterprises to actively engage in desertification combat efforts.

Both the government and the enterprise are treated as rational entities, making strategic choices for joint participation in combating desertification. Recognizing the dynamic nature of the desertification combat area, differential game models are employed to identify the optimal mode for combating desertification.

The findings underscore the significant influence of the governance cycle duration on the selection of desertification combat modes for government and enterprise. A cooperative mode is best suited to a short governance cycle, while an ecological subsidy mode is optimal for a longer cycle. Enhancing governance technology and shortening the governance cycle are conducive to combating desertification. Reducing taxes alone may not be an effective control strategy; rather, the government can better motivate enterprises by adopting tax rate policies aligned with the chosen governance mode.

This research contributes by elucidating the impact mechanism of the government cycle’s length on the desertification combat process. The results may offer valuable insights for governments in formulating strategies to encourage corporate participation in combating desertification and provide theoretical support for selecting optimal desertification combat modes.

This study adopts the perspective of dynamic capabilities to investigate influencing factors and proposes improvement strategies of supply chain resilience of prefabricated construction.

The structural equation model (SEM) is used to identify and verify the relationship between factors influencing supply chain resilience of prefabricated construction from the perspective of dynamic capabilities. The system dynamics (SD) model is constructed to dynamically simulate the specific effects of different influencing factors.

Results indicate that: (1) An evaluation index system for supply chain resilience of prefabricated construction containing five first-level indicators and 36 second-level indicators is constructed; (2) Ability to anticipate, ability to respond, ability to adapt, ability to recover and ability to learn are positively correlated with the supply chain resilience of prefabricated construction and (3) ANT3 (information system), RES1 (quick response), ADA3 (buffer stock) and LEA4 (trust) are the most leading factors influencing supply chain resilience of prefabricated construction over time.

This study fulfills the need for an in-depth exploration of the various influencing factors on supply chain resilience of prefabricated construction from the perspective of dynamic capabilities. Furthermore, this study provides improvement strategies to enhance supply chain resilience of prefabricated construction in China.

This paper aims to test the effects of social presence (SP) and other related factors, including trust, self-construal and brand familiarity, in affecting consumers’ purchase intention of live-streaming workout courses.

Three between-subjects, Web-based experimental studies were conducted. There were separately 108, 208 and 284 valid questionnaires collected in the three studies. Moderation and mediation analyses were performed to test the research hypotheses.

Users’ sense of SP when watching live-streaming courses positively affects their willingness to purchase courses, and users’ sense of trust plays a mediating role in the influence of users’ SP on their purchase intention. In addition, when users belong to dependent self-construal rather than independent self-construal, their trust in live-streaming courses plays a stronger mediating role in the influence of users’ SP on their purchase intentions. What is more, when users have high brand familiarity rather than low brand familiarity, their trust in online live-streaming plays a stronger mediating role in the influence of users’ SP on their purchase intentions.

This research delineates the effects of SP on the consumption of live-streaming courses, thus further adding to the understanding of the role of real-time interaction in determining consumer behavior. It also highlights the roles of self-construal and brand familiarity as mediating influences on the relationship between SP and consumer trust.

Quantitative and spatial-explicit flood risk information is of great importance for strengthening climate change adaptation and flood resilience. Shanghai is a coastal megacity at large estuary delta with rising flood risks. This study aims to quantify the overall economic-societal risks of storm flooding and their spatial patterns in Shanghai.

Based on multiple storm flood scenarios at different return periods, as well as fine-scale data sets including gridded GDP, gridded population and vector land-use, a probabilistic risk model incorporating geographic information system is used to assess the economic-societal risks of flooding and their spatial distributions.

Our results show that, from 1/200 to 1/5,000-year floods, the exposed assets will increase from USD 85.4bn to USD 657.6bn, and the direct economic losses will increase from USD 3.06bn to USD 52bn. The expected annual damage (EAD) of assets is around USD 84.36m. Hotpots of EAD are mainly distributed in the city center, the depressions along the upper Huangpu River in the southwest, the north coast of Hangzhou Bay, and the confluence of the Huangpu River and Yangtze River in the northeast. From 1/200 to 1/5,000-year floods, the exposed population will rise from 280 thousand to 2,420 thousand, and the estimated casualties will rise from 299 to 1,045. The expected annual casualties (EAC) are around 2.28. Hotspots of casualties are generally consistent with those of EAD.

In contrast to previous studies that focus on a single flood scenario or a particular type of flood exposure/risk in Shanghai, the findings contribute to an understanding of overall flood risks and their spatial patterns, which have significant implications for cost-benefit analysis of flood resilience strategies.