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This paper aims to investigate multidimensional customer engagement behaviors (CEBs) as antecedents of action loyalty in hospitality contexts and examine service conditions that inhibit and facilitate the former relationship.

This paper tests a holistic framework based on transaction data from 5,855 active members of a hotel firm. The hypotheses are examined using ordinary least squares regression.

By integrating transaction-related CEBs with non-transaction-related CEBs, this paper found that three CEB constructs (i.e. feedback, mobilizing and cross-buying) contribute significantly to action loyalty in hospitality contexts. These effects vary depending on the inhibitor (service failure) and the facilitator (service customization).

Hotel managers should value customer engagement as a marketing tool to retain customers. When engaged customers encounter service failure and customization, managers can react differently to facilitate consumers’ action loyalty.

Contrary to prior studies focusing on the effects of general CEBs on attitudinal loyalty, this study examines the diverse impacts of multidimensional CEBs on customers’ action loyalty and confirms boundary conditions to coordinate the effects between CEBs from a hotel firm’s perspective.

The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The problem how to choose the key elements of complex products has always been concerned on many fields, such as cost assessment, investment decision making, etc.

Using Grey System Theory to establish a cost estimation model of complicated equipment is more reasonable under the few data and poor information. Therefore, this paper constructs cost index’s system of complex equipment, and then quantitative and qualitative analysis methods are utilized to calculate the grey entropy between the characteristic parameter and the behavior parameters. Further, establish the grey relational clustering matrix of the behavior sequences by using the grey relative incidence analysis. Finally, the authors select key indicators according to the grey degree.

The experiment demonstrates that the cost key parameters of complex equipment can be successfully screened out by the proposed approach, and the cost estimation accuracy of complicated products is improved.

The method proposed in this paper could be utilized to solve some practical problems, particularly the selection of cost critical parameters for complex products with few samples and poor information. Taking the cost key indexes of civil aircraft as an example, the results verified the validity of the GICM model.

In this paper, the authors develop the method of GICM model. Taking the data of civil aircraft as an example, the authors screen the key indicators of complex products successfully, and improve the prediction accuracy of the GM (1, N) model by using the selected parameters, which provides a reference for some firms.