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This study aims to investigate the configurational effects of five rules – artificial intelligence (AI)-based hiring decision transparency, consistency, voice, explainability and human involvement – on applicants' procedural justice perception (APJP) and applicants' interactional justice perception (AIJP). In addition, this study examines whether the identified configurations could further enhance applicants' organisational commitment (OC).

Drawing on the justice model of applicants' reactions, the authors conducted a longitudinal survey of 254 newly recruited employees from 36 Chinese companies that utilise AI in their hiring. The authors employed fuzzy-set qualitative comparative analysis (fsQCA) to determine which configurations could improve APJP and AIJP, and the authors used propensity score matching (PSM) to analyse the effects of these configurations on OC.

The fsQCA generates three patterns involving five configurations that could improve APJP and AIJP. For pattern 1, when AI-based recruitment with high interpersonal rule (AI human involvement) aims for applicants' justice perception (AJP) through the combination of high informational rule (AI explainability) and high procedural rule (AI voice), there must be high levels of AI consistency and AI voice to complement AI explainability, and only this pattern of configurations can further enhance OC. In pattern 2, for the combination of high informational rule (AI explainability) and low procedural rule (absent AI voice), AI recruitment with high interpersonal rule (AI human involvement) should focus on AI transparency and AI explainability rather than the implementation of AI voice. In pattern 3, a mere combination of procedural rules could sufficiently improve AIJP.

This study, which involved real applicants, is one of the few empirical studies to explore the mechanisms behind the impact of AI hiring decisions on AJP and OC, and the findings may inform researchers and managers on how to best utilise AI to make hiring decisions.

Through the scientific and reasonable evaluation of the site selection of the emergency material reserve, the optimal site selection scheme is found, which provides reference for the future emergency decision-making research.

In this paper, we have chosen three primary indicators and twelve secondary indicators to construct an assessment framework for the determination of suitable locations for storing emergency material reserves. By mean of the improved entropy weight-order relationship weight determination method, the evaluation model of kullback leibler-technique for order preference by similarity to an ideal solution (KL-TOPSIS) emergency material reserve location based on relative entropy is established. On this basis, 10 regional storage sites in Beijing are selected for evaluation.

The results show that the evaluation model of the location of emergency material reserve not only respects the objective knowledge, but also considers the subjective information of the experts, which makes the ranking result of the location of the emergency material reserve more accurate and reliable.

Firstly, the modification factor is added to the calculation formula of traditional entropy weight method to complete the improvement of entropy weight method. Secondly, the order relation analysis method is used to assign subjective weights to the indicators. The principle of minimum information entropy is introduced to determine the comprehensive weight of the index. Finally, KL distance and TOPSIS method are combined to determine the relative entropy and proximity degree of alternative solutions and positive and negative ideal solutions, and the scientific and effective of the method is proved by case study.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

Drawing on conversation of resources theory, the present paper aimed to investigate the effect of strengths-based leadership on follower career satisfaction and the mediating role of follower strengths use as well as the moderating role of emotional exhaustion in the relationship.

Research data were gathered at 3 time points with a sample of 210 participants working in various organizations in China. Structural equation model (SEM) was applied to examine the authors' hypotheses.

The results indicated that strengths-based leadership has a positive impact on follower career satisfaction and follower strengths use fully mediates the effect of strengths-based leadership on follower career satisfaction. More importantly, emotional exhaustion enhanced the direct relationship between strengths use and career satisfaction and the indirect association of strengths-based leadership with follower career satisfaction through follower strengths use.

The main limitation of the present paper was the single source of research data.

The present paper advances strengths-based leadership theory and research and provides a new insight into cultivating employee career satisfaction.

This study aims to provide a series of drivers that prompt the blockchain technology (BT) adoption decisions in circular supply chain finance (SCF) and also assesses their degrees of influence and interrelationships, which leads to the construction of a theoretical model depicting the influence mechanism of BT adoption decisions in circular SCF.

This study mainly uses the technology-organization-environment (TOE) framework, which focuses on the aspects based on the nature of innovation, intra-organizational characteristics and extra environmental consideration, to identify the drivers of blockchain adoption in circular SCF context, while the significance and causality of the drivers are explained using interpreting structural models (ISMs) and the decision-making trial and evaluation laboratory (DEMATEL) method.

The findings of this study indicate that government policy and technological comparative advantage are the underlying reasons for BT adoption decisions, management commitment and financial expectations are the critical drivers of BT adoption decisions while other factors are the receivers of the mechanism.

This study provides theoretical references and empirical insights that influence the technology adoption decisions of both BT and circular SCF by practitioners.

The theoretical research contributes significantly to current research and knowledge in both BT and circular SCF fields, especially by extending the existing TOE model by combining relevant enablers from technological, organizational and external environmental aspects with the financial performance objectives of circular SCF services, which refer to the optimization of the financial resources flows and financing efficiency.

Computer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.

The developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.

Three DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.

This study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

Although the importance and variety of emotions have been emphasized in existing literature, studies on discrete emotions remain limited. This study aims to propose a method for more precise recognition and calculation of emotions in massive amounts of online data on attraction visitor experiences and behaviour, by using discrete emotion theory.

Using HowNet’s word similarity calculation technique, this study integrated multiple generic dictionaries, including the sentiment vocabulary ontology database of the Dalian University of Technology, the National Taiwan University Sentiment Dictionary and the Boson Dictionary. Word2vec algorithm filters emotion words unique to hospitality and tourism in 1,596,398 texts from Sogou News, Wikipedia and Ctrip reviews about attractions, and 1,765,691 reviews about attractions in China.

The discrete sentiment dictionary developed in this study outperformed the original dictionary in identifying and calculating emotions, with a total vocabulary extension of 12.07%, demonstrating its applicability to tourism.

The developed new dictionary can be used by researchers and managers alike to quickly and accurately evaluate products and services based on online visitor reviews.

To the best of the authors’ knowledge, this study is the first to construct a sentiment dictionary based on discrete emotion theory applicable to hospitality and tourism in the Chinese context. This study extended the applicability of affective psychology to hospitality and tourism using discrete emotion theory. Moreover, the study offers a methodological framework for developing a domain-specific sentiment dictionary, potentially applicable to other domains in hospitality.

The purpose is to provide decision support for tourists recommending scenic spots and corresponding suggestions for the management of scenic spots.

Based on the Baidu index data generated, this paper analyzes the temporal and spatial characteristics of network attention of 5A scenic spots in Sichuan Province. The online comment data are used to build the assessment model of scenic spots based on network attention, and the comment information of tourists is mined and analyzed through statistical analysis. At the same time, the key attributes of scenic spots from the perspective of network attention are evaluated and analyzed by using the probabilistic linguistic term set. Finally, this paper further constructs a recommendation model based on the key attribute set of scenic spots.

This paper uses different types of tourism network information, integrates multi-types of data and methods, fully excavates the value information of tourism network information, constructs the research framework of “scenic spot assessment + scenic spot recommendation” from the perspective of network attention, analyzes the network attention characteristics of scenic spots, evaluates the performance of scenic spots, and implements scenic spot recommendation.

This paper integrates multi-source data and multidisciplinary theoretical methods to form a scenic spot research framework of “assessment + recommendation” from the perspective of network attention.

This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

While the phenomenon of technostress has received significant attention from researchers in recent years, empirical findings concerning the consequences of specific forms of techno-stressors have remained scattered and contradictory. The authors aim to integrate the conclusions of previous studies to understand the effects of specific techno-stressors on strain and job performance.

This study employs meta-analytic techniques to calibrate the findings of 67 studies investigating more than 63,100 employees.

In general, not all techno-stressors have adverse effects. In particular, techno-uncertainty does not impact job performance. In addition, relative weight analyses reveal the relative importance of techno-complexity and techno-insecurity as predictors of both strain and job performance. Finally, this study finds that the effects of specific techno-stressors on job performance vary depending on research participants' gender, educational attainment and employment status.

First, this study provides a more nuanced view of the effects of specific techno-stressors. Second, this research clarifies the relative importance of specific techno-stressors as predictors of strain and job performance. Finally, this study reveals the moderating effects of demographic variables on the relationships between specific techno-stressors and job performance.