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Sustainable tourism is becoming more popular all over the world. Eco-friendly (green) hotels are properties that are friendly to the environment and are becoming increasingly popular among green travellers. Electronic word-of-mouth is a technique of communicating with consumers in order to share their experiences, and it is a significant marketing tool for hotels. This paper aims to identify the main themes shared in online reviews by tourists visiting eco-friendly hotels, and which of these themes were associated with satisfaction and dissatisfaction ratings.

The research used qualitative content analyses to analyse 1,202 user-generated content of the top 10 hotels in UK shared by guests on an online platform.

The analyses revealed nine themes in descriptions of airline travel experiences. These are “hotel amenities”, “services”, “location”, “staff”, “eco” (eco-friendly activities), “value” and “recommend/revisit” (intentions). Negative comments are associated with the “bathroom”, “mattress”, “water”, “bed”, “price”, “shower”, “Wi-Fi” and “restaurant” concepts.

This study differs from previous research in which it aims to address a void in the literature on the shortcomings of research focused on finding the dominant themes expressed in online reviews by tourists visiting eco-friendly hotels, and it does so using data mining approach.

Drawing upon self-determination theory (SDT) and the proactive motivation model, this study examined how inclusive leadership is related to organizational citizenship behavior (OCB) through psychological need satisfaction (PNS).

Data were collected from a large Dutch private company in the financial sector (N = 264) and analyzed using structural equation modeling (SEM).

Inclusive leadership positively influenced all three PNS dimensions (autonomy, competence and relatedness). Both autonomy and relatedness fully mediated the relationship between inclusive leadership and OCB. However, this was not the case for competence, although additional analyses revealed the serial mediation of all three PNS dimensions.

By highlighting the mediating role of PNS, this study contributes to the inclusive leadership literature by helping unravel the underlying process through which leaders influence team outcomes. The findings emphasize the importance of inclusive leaders in satisfying employees' individual psychological needs, so that they can redirect their attention toward prosocial behaviors.

Considering the vital role of resource-constraint innovation in developing countries, the aim of the study is to examine the mechanism of internal and external heterogeneous knowledge sharing (HKS) in the relationship between sustainable leadership (SL) and frugal innovation (FI). The social exchange theory was used to develop a research framework.

This study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis to examine the relationship among several latent factors based on 263 participants from Pakistani SMEs.

The current findings support the significant positive impact of SL on both internal and external HKS. Moreover, this study also confirms the mediating effect of both types of HKS in the relationship between SL and FI.

To delve further into the benefits and vital role of HKS, it is recommended to conduct further research that would examine the potential impact of heterogeneous knowledge sources on the “SL–FI relationship” and to apply the presented research methodology in other countries and organizations beyond Pakistani SMEs.

This study is one of the first documented attempts to demonstrate HKS as a mechanism in the relationship between a specific type of leadership and FI.

The purpose of this study was to investigate trust within human-AI teams. Trust is an essential mechanism for team success and effective human-AI collaboration.

In an online experiment, the authors investigated whether trust perceptions and behaviours are different when introducing a new AI teammate than when introducing a new human teammate. A between-subjects design was used. A total of 127 subjects were presented with a hypothetical team scenario and randomly assigned to one of two conditions: new AI or new human teammate.

As expected, perceived trustworthiness of the new team member and affective interpersonal trust were lower for an AI teammate than for a human teammate. No differences were found in cognitive interpersonal trust and trust behaviours. The findings suggest that humans can rationally trust an AI teammate when its competence and reliability are presumed, but the emotional aspect seems to be more difficult to develop.

This study contributes to human–AI teamwork research by connecting trust research in human-only teams with trust insights in human–AI collaborations through an integration of the existing literature on teamwork and on trust in intelligent technologies with the first empirical findings on trust towards AI teammates.

Bi-slant submanifolds of S-manifolds are introduced, and some examples of these submanifolds are presented.

Some properties of D_i-geodesic and D_i-umbilical bi-slant submanifolds are examined.

The Riemannian curvature invariants of these submanifolds are computed, and some results are discussed with the help of these invariants.

The topic is original, and the manuscript has not been submitted to any other journal.

This study aims to investigate how art events can enrich novice visitors by transforming their practices.

This research uses an interpretive case study of the art exhibition “1/1/1/1/1” in the Oppenheim gallery in Wroclaw. It draws on multiple sources of evidence, namely, novice visitors’ interviews, observation including photo studies and content analysis of art-makers’ mediation sources. This study is an example of contextual theorizing from case studies and participatory action research with researchers as change agents.

The evidence highlights that aesthetic values and experiences are contextual to practices and are transformable into other values. The findings illustrate the role of practice theory in studying how art-makers inspire the transformation of practices, including values driving the latter.

The findings provide implications for transformations of co-creating contextual values in contemporary visual art consumption and customer experience management.

Practical implications to arts organizations are also provided regarding cultural mediation conducted by art-makers. Exhibition makers should explain the meanings of the particularly visible artefacts to allow visitors to develop a congruent understanding of the meanings. The explanations should not provide ready answers or solutions to the problem art-makers suggest to rethink.

The social implication of our findings is that stakeholders in artistic ventures may undertake adequate, qualified and convergent actions to maintain or transform the defined interactive practices between them in co-creating contextual aesthetic values.

The study provides new insights into co-creating values in practices in the domain of contemporary art exhibitions by bringing the practice theory together with an audience enrichment category, thus illustrating how novice visitors get enriched by transforming their practices led by contextual values of “liking” and “understanding”.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

The objective of this work is to demonstrate the relationships between the two main processes of research and development (R&D) activities: the knowledge generation phase (KPP) and the knowledge commercialization, or transfer, phase (KCP), in a sector that is intensive in this type of activity, such as the pharmaceutical sector. In addition, within the framework of the general objective of this work, the authors propose two other objectives: (1) make advances in network efficiency measurement models, and (2) determine the factors associated with efficiency in the KPP and in the KCP in companies of the pharmaceutical sector in Spain.

A Network Data Envelopment Analysis (NDEA) model (Färe and Grosskopf, 2000) with categorical variables (Lee et al., 2020; Yeh and Chang, 2020) has been applied, and a sensitivity analysis of the obtained results has been performed through a DEA model of categorical variables, in accordance with the work of Banker and Morey (1986), to corroborate the results of the proposed model. The sample is made up of 77 companies in the pharmaceutical sector in Spain.

The results obtained point to a greater efficiency of pharmaceutical companies in the KPP, rather than in the KCP. Furthermore, the study finds that 1) alliances between companies have been the accelerating factors of efficiency in the KCP (but patents have slowed this down the most); 2) the quality of R&D and the number of R&D personnel are the factors that most affect efficiency in the KPP; and 3) the quality of R&D again, the benefits obtained and the position in the market are the factors that most affect efficiency in the KCP.

The authors have not found studies that show whether the efficiency obtained by R&D-intensive companies in the KPP phase is related to better results in terms of efficiency in the KCP phase. No papers have been found that analyse the role of alliances between R&D-intensive companies and patents, as agents that facilitate efficiency in the KCP phase, covering the gap in the research on both problems. Notwithstanding, this work opens up a research path which is related to the improvement of network efficiency models (since it includes categorical variables) and the assessment of the opinions of those who are responsible for R&D departments; it can be applied to decision-making on the aspects to improve efficiency in R&D-intensive companies.

A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

In order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.

First, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.

The cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.

This study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.