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Considering the impact of the Free Trade Zone (FTZ) policy on forecasting the port cargo throughput, this paper constructs a fractional grey multivariate forecasting model to improve the prediction accuracy of port cargo throughput and realize the coordinated development of FTZ policymaking and port construction.

Considering the effects of data randomization, this paper proposes a novel self-adaptive grey multivariate prediction model, namely FDCGM(1,N). First, fractional-order accumulative generation operation (AGO) is introduced, which integrates the policy impact effect. Second, the heuristic grey wolf optimization (GWO) algorithm is used to determine the optimal nonlinear parameters. Finally, the novel model is then applied to port scale simulation and forecasting in Tianjin and Fujian where FTZs are situated and compared with three other grey models and two machine learning models.

In the Tianjin and Fujian cases, the new model outperforms the other comparison models, with the least mean absolute percentage error (MAPE) values of 6.07% and 4.16% in the simulation phase, and 6.70% and 1.63% in the forecasting phase, respectively. The results of the comparative analysis find that after the constitution of the FTZs, Tianjin’s port cargo throughput has shown a slow growth trend, and Fujian’s port cargo throughput has exhibited rapid growth. Further, the port cargo throughput of Tianjin and Fujian will maintain a growing trend in the next four years.

The new multivariable grey model can effectively reduce the impact of data randomness on forecasting. Meanwhile, FTZ policy has regional heterogeneity in port development, and the government can take different measures to improve the development of ports.

Under the background of FTZ policy, the new multivariable model can be used to achieve accurate prediction, which is conducive to determining the direction of port development and planning the port layout.

As after-hours technology-mediated work (ATW) becomes common in organizations, the increased workload and interference to life caused by ATW has induced employee turnover. This research develops a mediated moderation model to explain how employees' intrinsic and extrinsic motivations for ATW affect their turnover intention through work–life conflict.

A survey was conducted to collect data of 484 employees from Chinese companies. Partial Least Square was used to perform data analysis.

The results show that intrinsic motivation for ATW has an indirect negative impact on turnover intention via work–life conflict, whereas extrinsic motivation for ATW has both a positive direct impact and a positive indirect impact (via work–life conflict) on turnover intention. This study also helps find that time spent on ATW can strengthen the positive impact of extrinsic motivation for ATW on turnover intention but has no moderation effect on the impact of intrinsic motivation for ATW. Furthermore, this study reveals that the interaction effect of time spent on ATW and extrinsic motivation on turnover intention is mediated by employees' perceived work–life conflict.

By discovering the distinct impact of employees' intrinsic and extrinsic motivations for ATW on turnover intention, this research provides a contingent view regarding the impact of ATW and offers guidance to managers regarding how to mitigate ATW-induced turnover intention through fostering different motivations.