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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.

Compensatory consumption focuses on the psychological value of products. Special agricultural products have symbolic and social functions that effectively meet psychological needs and stimulate compensatory consumption behavior. The social commerce context not only enriches consumer experience but also influences consumer purchase decisions. This study constructs a model based on the elaboration likelihood model (ELM) and the stimulus-organism-response (SOR) theory to explore the mechanism of compensatory consumption behavior of special agricultural products in a social commerce context.

This study uses a two-stage method of partial least squares structural equation model (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) to analyze 523 valid samples collected through random sampling. PLS-SEM was used to examine the relationships and effects between the variables; fsQCA was used to conduct a cohort analysis between the variables to further reveal the complexity and diversity of compensatory consumption behaviors.

PLS-SEM indicates that product attributes and social affordances influence consumers’ triggering of compensatory consumption behavior for control and belongingness needs. fsQCA shows that there are three different modes, and the satisfaction of belongingness or control needs is a necessary condition for triggering compensatory consumption behavior.

There is limited research on compensatory consumption behavior specifically focused on special agricultural products. This study explores the influencing factors and mechanisms of compensatory consumption behavior related to special agricultural products. The occurrence of compensatory consumption behavior is not only influenced by product attributes but also by the social commerce environment. In marketing strategies, it is important to not only consider product characteristics but also pay attention to consumers’ social and psychological needs.

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

This study aims to investigate the mechanism of the misinformation spread based on the elaboration likelihood model and the effects of four factors – emotion, topic, authority and richness – on the depth, breadth and structural virality of misinformation spread.

The authors collected 2,514 misinformation microblogs and 142,006 reposts from Weibo, used deep learning methods to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network using the network analysis method.

Results show that misinformation has a smaller spread size and breadth than true news but has a similar spread depth and structural virality. The differential influence of emotions on the structural characteristics of misinformation propagation was found: sadness can promote the breadth of misinformation spread, anger can promote depth and disgust can promote depth and structural virality. In addition, the international topic, the number of followers, images and videos can significantly and positively influence the misinformation's spread size, depth, breadth and structural virality.

The influencing factors of the structural characteristics of misinformation propagation are clarified, which is helpful for the detection and management of misinformation.

People's socializing behavior in social networking services (SNS) presents dramatically different features, forming differentiated online social interaction patterns (DOSIP) in SNS. This study aims to explore the relationships between users' multidimensional psychological needs and multiple social interaction patterns in SNS.

Based on Maslow's hierarchy of needs and use and gratifications (U&G) theory, the authors develop the research model to examine the effects of psychological needs on DOSIP. A survey is used to collect the data of SNS users' social interaction. The authors adopt structural equation modeling–neural network (SEM-NN) integrated method to examine the research model.

Need to belong, need for self-esteem, need for social contact, need for emotional expression, need for cognition, and need for external-esteem have significant influences on both active and passive social interactions respectively.

Based on the categorization of DOSIP into six types in terms of the level of activity and disclosure of social interaction, the authors construct an integrated research model of multidimensional psychological needs to multiple social interaction patterns, and validate the antecedents of DOSIP from the perspective of psychological needs.

With the deep development of the new technological revolution and industrial transformation, the development, application, expansion and integration of digital technology provide opportunities for transforming the manufacturing industry from traditional manufacturing to intelligent manufacturing. However, little research currently focuses on analyzing the influencing factors of intelligent development in this field. There is a lack of research from the perspective of the digital innovation ecosystem to explore the intrinsic mechanism that drives intelligent development. Therefore, this article starts with high-end equipment manufacturing enterprises as the research subject to explore how their digital innovation ecosystem promotes the effectiveness of enterprise intelligent development, providing theoretical support and policy guidance for enterprises to achieve intelligent development at the current stage.

This article constructs a logical framework for the digital innovation ecosystem using a “three-layer core-periphery” structure, collects data using crawling for subsequent indicator measurement and assessment and uses the fuzzy set Qualitative Comparative Analysis method (fsQCA) to explore how the various components of the digital innovation ecosystem in high-end equipment manufacturing enterprises work together to promote the development of enterprise intelligently.

This article finds that the various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises, through mutual coordination, can help improve the level of enterprise intelligence. Empirical analysis shows four specific configuration implementation paths for the digital innovation ecosystem of high-end equipment manufacturing enterprises to promote intelligent development. The core conditions and their combinations that affect the intelligent development of enterprises differ in each configuration path.

Firstly, this article discusses the practical problems of intelligent transformation and development in the manufacturing industry and focuses on the intelligent development effectiveness of various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises in the context of digitalization. Secondly, this article uses crawling, text sentiment analysis and other methods to creatively collect relevant data to overcome the research dilemma of being limited to theoretical analysis due to the difficulty in obtaining data in this field. At the same time, based on the characteristics of high-end equipment manufacturing enterprises, the “three-layer core-periphery” digital innovation ecosystem framework constructed in this article helps to gain a deep understanding of the development characteristics of the industry's enterprises, provides specific indicator analysis for their intelligent development, opening the “black box” of intelligent development in the industry's enterprises and bridging the gap between theory and practice. Finally, this study uses the fsQCA research method of configuration analysis to explore the complexity of the antecedents and investigate the combined effects of multiple factors on intelligent development, providing new perspectives and rich research results for relevant literature on the intelligent development of high-end equipment manufacturing enterprises.

Based on the conservation of resource theory, this paper explores the impact of differentiated transformational leadership on employees' taking charge in the context of Chinese organizations, with psychological availability as a mediator and collectivism orientation as a moderator.

The authors distributed paired questionnaires to 67 team managers and 219 team members to obtain research data and established a hierarchical linear model for the hypothesis testing.

The results show that team-focus transformational leadership has a significant positive impact on employees' taking charge, and individual-focus transformational leadership has a significant negative impact on employees' taking charge. Specifically, psychological availability plays a partial mediating role between differentiated transformational leadership and employees' taking charge. Moreover, collectivism orientation has no significant moderating effect between team-focus transformational leadership and psychological availability. But it has a significant moderating effect between individual-focus transformational leadership and psychological availability, i.e. the higher the level of collectivism orientation, the stronger the negative effect of individual-focus transformational leadership on psychological availability.

The paper notes the hierarchy of differentiated transformational leadership and divides it into team-focus transformational leadership and individual-focus transformational leadership. It also provides a new mechanism and boundary condition, i.e. differentiated transformational leadership has an impact on employees' taking charge through psychological availability and collectivism orientation is a moderator.

This paper aims to propose an energetic model parameter calculation method for predicting the materials’ symmetrical static hysteresis loop and asymmetrical minor loop to improve the accuracy of electromagnetic analysis of equipment.

For predicting the symmetrical static hysteresis loop, this paper deduces the functional relationship between magnetic flux density and energetic model parameters based on the materials’ magnetization mechanism. It realizes the efficient and accurate symmetrical static hysteresis loop prediction under different magnetizations. For predicting the asymmetrical minor loop, a new algorithm is proposed that updates the energetic model parameters of the asymmetrical minor loop to consider the return-point memory effect.

The comparison of simulation and experimental results verifies that the proposed parameters calculation method has high accuracy and strong universality.

The proposed parameter calculation method improves the existing parameter calculation method’s problem of relying on too much experimental data and inaccuracy. Consequently, the presented work facilitates the application of the finite element electromagnetic field analysis method coupling the hysteresis model.

This study aims to develop an original green organizational learning capability (GOLC) framework to examine the effects of green transformational leadership (GTL) on competitive advantage (CA) through GOLC by making use of the natural resource-based view (NRBV).

The current research proposes GOLC as a novel construct that simultaneously integrates green absorptive capability (GAC) and green transformative capability (GTC). Furthermore, this study presents a theoretical model that investigates GOLC as an intermediate mechanism in the relationship between GTL and CA based on the NRBV. The partial least squares method is used to test the data collected from 265 firms included in the list of Turkey’s Top 500 Industrial Enterprises in 2019 and having ISO 14001 certificate.

Top management’s GTL positively affects the firm’s GOLC. Moreover, GOLC positively affects the firm’s CA. This study further shows that GTL has a significant indirect effect on CA through GOLC.

This study demonstrates how firm managers can be persuasive in adopting GOLC with a critical role in developing and promoting green products and services to improve the firm’s environmental sustainability and CA by exhibiting GTL.

This research applies the NRBV theory to propose a novel concept, GOLC and develops an integral conceptual model to discover its managerial impacts, antecedent and consequence. No prior literature has examined the impact of top management’s GTL on GOLC and CA.

This study aims to propose an optimization framework using deep neural networks (DNN) coupled with nondominated sorting genetic algorithm II and technique for order preference by similarity to an ideal solution method to improve the tribological properties of camshaft bearing pairs of internal combustion engine.

A lubrication model based on the theory of elastohydrodynamic lubrication and flexible multibody dynamics was developed for a V6 diesel engine. Setting DNN model as fitness function, the multi-objective optimization genetic algorithm and decision-making method were used to optimize the bearing pair structure with the goal of minimizing the total friction loss and the difference of the average values of minimum oil film thickness.

The results show that the lubrication state corresponding to the optimized bearing pair structure is elastohydrodynamic lubrication. Compared with the original structure, the optimized structure significantly reduces the total friction loss.

The optimized performance and corresponding structural parameters are obtained, and the optimization results were verified through multibody dynamics simulation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0417/