Hongying Tan, Umair Akram and Yujia Sui
Uncertain level discount (ULD) is a type of promotion combining regular discount (RD) with uncertainty. The purpose of this paper is to explore the impact of ULD on consumers’…
Abstract
Purpose
Uncertain level discount (ULD) is a type of promotion combining regular discount (RD) with uncertainty. The purpose of this paper is to explore the impact of ULD on consumers’ perceived quality compared with RD and to identify the relevant influencing mechanism and boundary for the effectiveness of ULD.
Design/methodology/approach
Three online experiments were conducted with 445 participants from China. First, experiment 1 compares the attractiveness of ULD and RD. Second, experiment 2 evaluates the impacts of ULD and RD on consumers’ perceived quality and clarifies the mechanism in this process. Finally, experiment 3 examines the moderating effect of product knowledge.
Findings
ULD has the same level of attractiveness as RD with equivalent expected discount value for consumers. Besides, consumers in ULD give higher ratings to product quality compared with those in RD, and the lower diagnosticity of price cues in ULD underlies the differential effects of ULD vs RD. Furthermore, product knowledge moderates the relationship between the two promotions and perceived quality.
Practical implications
The findings provide valuable guidance for managers to conduct promotional campaigns. ULD is an effective promotion to attract consumers to purchase with keeping consumers’ perceived quality high, and such effectiveness will rise for products that consumers are unfamiliar with. Managers can make rational use of ULD to achieve positive promotion results in both the short and long term.
Originality/value
Few studies pay attention to the long-term effects of the uncertain promotion. This research profoundly investigates the impact of ULD on perceived quality, which complements existing studies from a more integrated perspective that combines short- and long-term effects. Also, this research identifies the mechanism based on the cue diagnosticity theory and puts forward a new explanation for positive uncertainty in uncertain promotions. Finally, this research applies the impact of product knowledge on information process strategies into the uncertain promotion, which clarifies the utility boundary of ULD from a new perspective and offers a more comprehensive understanding for this promotion.
Details
Keywords
Linhai Zhu, Liu Jinfu, Yujia Ma, Mingliang Bai, Weixing Zhou and Daren Yu
This paper aims to establish a multi-input equilibrium manifold expansion (EME) model for gas turbine (GT). It proposes that the extension of model input dimension is realized…
Abstract
Purpose
This paper aims to establish a multi-input equilibrium manifold expansion (EME) model for gas turbine (GT). It proposes that the extension of model input dimension is realized based on similarity theory and affine structure in the framework of single-input EME model. The study aims to expand the scope of application of the EME model so that it can be used for the control or fault diagnosis of GTs.
Design/methodology/approach
In this paper, the concepts of corrected equilibrium manifold expansion (CEME) model and multi-cell equilibrium manifold expansion (MEME) model are first proposed. This paper uses theoretical analysis and simulation experiments to demonstrate the effectiveness of the bilayer equilibrium manifold expansion (BEME) model, which is a combination of the CEME and the MEME models. Simulation experiments include confirmatory experiments and comparative experiments.
Findings
The paper provides a new sight into building a multiple-input EME (MI-EME) model for GTs. The proposed method can build an accurate and robust MI-EME model that has superior performance compared with widely used nonlinear models including Wiener model (WM), Hammerstein model (HM), Hammerstein–Wiener model (HWM) and nonlinear autoregressive with exogenous inputs (NARX) network model. In terms of accuracy, the maximum error percentage of the proposed model is just 1.309%, far less than WM, HM and HWM. In terms of the stability of model calculation, the range of the mean error percentage of the proposed model is just a quarter of that of NARX network model.
Originality/value
The paper fulfills the construction of a novel multi-input nonlinear model, which has laid a foundation for the follow-up research of model-based GT fault detection and isolation or GT control.