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With the vigorous development of the e-commerce delivery service industry, delivery service has become an important factor for e-tailers to obtain the market competitive advantage. However, how to choose the best delivery service strategy is a difficult problem for e-tailers in practice. The purpose of this paper is to investigate the effect of delivery service on e-tailers and online platforms.

Based on the Stackelberg game, the research study establishes and solves three models, namely dual self-supporting delivery service model, dual third-party delivery service model and differential delivery service model.

The results show that when the self-supporting and third-party delivery cost coefficients are all small, no matter which delivery service providers the competitor selects, the e-tailer selects delivery with a lower service fee. When the self-supporting and third-party delivery service fee are all low, no matter which delivery service providers the competitor selects, the e-tailer selects delivery with a smaller service cost. Both service fee and service cost determine the choice of e-tailers' delivery strategy. When the commission rate is moderate, both e-tailers are willing to choose the self-supporting delivery strategy, but the platform only prefers to provide self-supporting delivery to them with a lower delivery service sensitivity coefficient.

This paper analyzes the optimal delivery service strategies for e-tailers to compete with competitors, and explores the impacts of parameters for e-tailers and online platforms in their decision-making. The findings provide valuable implications for relevant practices.

The purpose of this paper is to propose a knowledge extraction framework to extract knowledge, including entities and relationships between them, from unstructured texts in digital humanities (DH).

The proposed cooperative crowdsourcing framework (CCF) uses both human–computer cooperation and crowdsourcing to achieve high-quality and scalable knowledge extraction. CCF integrates active learning with a novel category-based crowdsourcing mechanism to facilitate domain experts labeling and verifying extracted knowledge.

The case study shows that CCF can effectively and efficiently extract knowledge from multi-sourced heterogeneous data in the field of Tang poetry. Specifically, CCF achieves higher accuracy of knowledge extraction than the state-of-the-art methods, the contribution of feedbacks to the training model can be maximized by the active learning mechanism and the proposed category-based crowdsourcing mechanism can scale up the effective human–computer collaboration by considering the specialization of workers in different categories of tasks.

This research proposes CCF to enable high-quality and scalable knowledge extraction in the field of Tang poetry. CCF can be generalized to other fields of DH by introducing domain knowledge and experts.

The extracted knowledge is machine-understandable and can support the research of Tang poetry and knowledge-driven intelligent applications in DH.

CCF is the first human-in-the-loop knowledge extraction framework that integrates active learning and crowdsourcing mechanisms; he human–computer cooperation method uses the feedback of domain experts through the active learning mechanism; the category-based crowdsourcing mechanism considers the matching of categories of DH data and especially of domain experts.