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This study aims to provide a method for classifying travellers’ requirements to help hoteliers understand travellers’ requirements and improve hotel services. Specifically, this study develops a strength-frequency Kano (SF-Kano) model to classify the requirements expressed by travellers in online reviews.

The strength and frequency of travellers’ requirements are determined through sentiment and statistical analyses of the 13,217 crawled online reviews. The proposed method considering the interaction between strength and frequency is proposed to classify the different travellers’ requirements.

This study identifies 13 travellers’ requirements by mining online reviews. According to the results of the improved Kano model, the six travellers’ requirements belong to one-dimensional requirements; two travellers’ requirements belong to must-be requirements; three travellers’ requirements belong to attractive requirements; two travellers’ requirements belong to indifferent requirements.

Results of this research can guide hoteliers to address hotel service improvement strategies according to the types of travellers’ requirements. This study can also expand the analysis scope of hotel online reviews and provide a reference for hoteliers to understand travellers’ requirements.

By mining online reviews, this study proposes an SF-Kano model to classify travellers’ requirements by considering both the strength and frequency of requirements. This study uses the optimisation model to determine the classification thresholds. This process maximises travellers’ satisfaction at the lowest cost. The classification results of travellers’ requirements can help hoteliers gain a deeper understanding of travellers’ requirements and prioritise service improvements.

This paper aims to investigate the impact of corporate culture on stock price crash risk and explore the underlying mechanisms.

This paper uses a novel firm-level culture measure of Li et al. (2020), which evaluates corporate culture from the perspectives of integrity, teamwork, innovation, respect and quality. Using a sample of 4,017 US firms from 2001 to 2018, this paper uses panel data regressions to explore the impact of corporate culture on stock price crash risk.

This paper finds that among five cultural dimensions, integrity reduces crash risk and quality increases crash risk. The mitigating effect of integrity culture on crash risk is concentrated among firms with a strong incentive or ability to hoard bad news. The exacerbating effect of quality culture on crash risk is concentrated among firms with low managerial flexibility.

This paper helps investors and regulators to understand the determinants of stock price crash risk, which facilitates investors’ wealth management and stabilizes social welfare.

To the best of the authors’ knowledge, this is the first study that uses time-varying firm-level measure of corporate culture to investigate its impact on stock price crash risk, contributing to the literature on the determinants of crash risk. Besides, this is the first study that explores the possible mechanism of managerial flexibility in influencing stock price crash risk.

This study investigates the influence of artificial intelligence (AI) stimuli on customer stickiness (CS), the mediation effects of social presence (SP) and the moderating impacts of customer traits in this influencing process.

Drawing on the arousal theory and social response theory, a conceptual model was established and tested by a data set of 268 customers in the catering industry.

The results indicate that AI stimuli, such as perceived personalization and perceived interactivity, positively affect CS. SP partially mediates the influence of AI stimuli on CS. Customer traits such as customers' need for interaction (NFI) and novelty seeking (NS) actively moderate the mediating effects of SP.

This study advances the interactive marketing literature from three aspects. Firstly, instead of focusing on the functional aspects of AI stimuli, it extends our understanding of AI-enabled interactive marketing by examining the effects of social and emotional aspects of AI stimuli on customer response. Secondly, it extends our understanding of social response by illuminating the mediating effects of SP between AI stimuli and CS. Finally, it provides new insights and empirical evidence for the research focus on customer traits in AI-enabled interactive marketing.

To solve the problem that the traditional methods miss key information in the process of bearing fault identification, this paper aims to apply the phase-space reconstruction (PSR) theory and intelligent diagnosis techniques to extend the one-dimensional vibration signal to the high-dimensional phase space to reveal the system information implied in the univariate time series of the vibration signal.

In this paper, a new method based on the PSR technique and convolutional neural network (CNN) is proposed. First, the delay time and the embedding dimension are determined by the C-C method and the false nearest neighbors method, respectively. Through the coordinate delay reconstruction method, the two-dimensional signal is constructed, and this information is saved in a set of gray images. Then, a simple and efficient convolutional network is proposed. Finally, the phase diagrams of different states are used as samples and input into a two-dimensional CNN for learning modeling to construct a PSR-CNN fault diagnosis model.

The proposed PSR-CNN model is tested on two data sets and compared with support vector machine (SVM), k-nearest neighbor (KNN) and Markov transition field methods, and the comparison results showed that the method proposed in this paper has higher accuracy and better generalization performance.

The method proposed in this paper provides a reliable solution in the field of rolling bearing fault diagnosis.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2023-0113/