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This study aims to investigate the online customer review behavior and determinants of overall satisfaction with hotels of travelers in various travel group compositions.

The author collected data from online reviews of travelers in various travel group compositions from 600 hotels in 100 of the largest cities in the USA from Booking.com and used latent semantic analysis (LSA) to identify the positive and negative factors from online reviews of travelers in various travel group compositions. Then, text regression was used to determine the influential factors of overall satisfaction of travelers in various travel group compositions.

It was found in this study that not all the positive and negative textual factors mined from travelers’ online reviews significantly influenced their overall satisfaction. In addition, the determinants of traveler satisfaction were different when travelers were in different travel group compositions.

The author found similar online review behavior, but different basic, excitement and performance factors of travelers in different travel group compositions.

This study helps hoteliers understand customers’ perception of the specific attributes of their products and services, which provides a guideline for businesses to design the priority rule to improve these corresponding attributes and use market segmentation strategy when dealing with customers in different travel group compositions.

The author examined and compared the online review behavior and determinants of satisfaction using the factors mined from online reviews between travelers in various travel group compositions. This study combined customer ratings with textual reviews and predicted customer ratings from the factors extracted from textual reviews using LSA and text regression.

Additive manufacturing (AM) has been increasingly used in various applications in recent years. However, it is still challenge when it comes to selecting a suitable AM process. This is because the outcome may vary due to not only different materials and printers but also different parameters and post-processes. This paper aims to develop an efficient method to help users understand trade-offs and make right decisions.

A hybrid method is proposed to help users select appropriate options from a large-scale and discrete option space in an interactive way. First, the design-by-shopping approach is applied to allow users exploring and refining the option space. The analytical hierarchical process method is then used to capture customers’ preferences. After analyzing the results of different normalization methods, a modified Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach is proposed to rank solutions and provide suggestions.

The usefulness of proposed method is illustrated in a case study. The results show that it can help customers understand performance distributions and find most suitable options accurately. The ranking of the modified TOPSIS method is more reasonable.

Due to the complexity of AM technologies, the process selection is considered at the parameter level. A new system framework is proposed for decision support. The TOPSIS method is modified to achieve a stable performance.

This study aims to review the existing methods for additive manufacturing (AM) process selection and evaluate their suitability for design for additive manufacturing (DfAM). AM has experienced a rapid development in recent years. New technologies, machines and service bureaus are being brought into the market at an exciting rate. While user’s choices are in abundance, finding the right choice can be a non-trivial task.

AM process selection methods are reviewed based on decision theory. The authors also examine how the user’s preferences and AM process performances are considered and approximated into mathematical models. The pros and cons and the limitations of these methods are discussed, and a new approach has been proposed to support the iterating process of DfAM.

All current studies follow a sequential decision process and focus on an “a priori” articulation of preferences approach. This kind of method has limitations for the user in the early design stage to implement the DfAM process. An “a posteriori” articulation of preferences approach is proposed to support DfAM and an iterative design process.

This paper reviews AM process selection methods in a new perspective. The users need to be aware of the underlying assumptions in these methods. The limitations of these methods for DfAM are discussed, and a new approach for AM process selection is proposed.

The purpose of this paper is twofold: to raise and address an important change for the human capital in the future of Industry 4.0, and to propose a human-focused perspective for companies underneath the new Industrial Revolution.

The research study follows a state-of-the-art literature review process. The nature of the selected approach enables to cover the extensive aim of the paper with sufficient scientific solidity that should support the understanding of every topic.

This work has presented three relevant aspects for Industry 4.0 and its human labour force: a workforce architecture with new interactions, a term to embrace the human capital of the future and a typology for referencing the required competences for Industry 4.0.

The paper sheds light on an important aspect for the emerging Industrial Revolution, the human force. The result and conclusion sections suggest future implications for academia and the private sector, due to changes at the conceptual and practical levels of human operation in the industry – for example, new structural interactions among employees, additional qualities to human capital and different ways to identify the competences for the workforce.

This is an interdisciplinary study that tries to bring together a modern industrial term, a social focus and a company scenario. From this, it was possible to obtain a new social term, a novel typology of competences and a new company-scenario interaction.

The purpose of this paper is to review the food supply chain management (FSCM) in terms of systems and implementations so that observations and lessons from this research could be useful for academia and industrial practitioners in the future.

A systematical and hierarchical framework is proposed in this paper to review the literature. Categorizations and classifications are identified to organize this paper.

This paper reviews total 192 articles related to the data-driven systems for FSCM. Currently, there is a dramatic increase of research papers related to this topic. Looking at the general interests on FSCM, research on this topic can be expected to increase in the future.

This paper only selected limited number of papers which are published in leading journals or with high citations. For simplicity without generality, key findings and observations are significant from this research.

Some ideas from this paper could be expanded into other possible domains so that involved parties are able to be inspired for enriching the FSCM. Future implementations are useful for practitioners to conduct IT-based solutions for FSCM.

As the increasing of digital devices in FSCM, large number of data will be used for decision-makings. Data-driven systems for FSCM will be the future for a more sustainable food supply chain.

This is the first attempt to provide a comprehensive review on FSCM from the view of data-driven IT systems.

The overuse and scarcity of resources emphasize the importance of the circular economy. The technology facilitated by Industry 4.0 stimulates the implementation of the circular economy that aims to reduce resource use and enhance operational efficiency. This study focuses on enhancing delivery efficiency in an online-to-offline (O2O) context from an Industry 4.0 technology-facilitated personal configuration perspective, that is, comparing in-house and crowdsourced delivery efficiency in China's O2O on-demand food delivery context.

The authors collect 128,152 orders from 38 restaurants of an online restaurant chain in China. The authors adopt multiple regression analysis to examine the delivery efficiency gap between in-house and crowdsourced deliverymen and the determinants of this efficiency gap.

The findings of this study reveal that crowdsourced deliverymen exhibit higher delivery efficiency, in terms of a shorter delivery time, than in-house deliverymen. In addition, the authors find that platforms providing monetary incentives or implementing late delivery penalties enlarge this efficiency gap. Furthermore, the authors show that external factors, such as working on weekends and bad weather conditions, contribute to the narrowing of this performance efficiency.

The study's findings suggest that platforms should use advanced technologies facilitated by Industry 4.0 to optimize their personnel configuration to enhance their delivery efficiency and reduce carbon emissions. The effective approaches include using financial incentives and improving working schedules.

The authors' findings contribute to the online fulfillment literature by focusing on delivery efficiency in the O2O context from the Industry 4.0 technology-facilitated personnel configuration perspective. The authors examine how internal and external factors moderate the performance efficiency between these two types of deliverymen.

The structure stiffness is greatly affected by the fixture constraints during assembly due to the flexibility of large-scale thin-walled structures. The compliant deformation of structures is usually not consistent for the non-uniform stiffness in various clamping schemes. The purpose of this paper is to investigate the correlation between the assembly quality and the clamping schemes of structures with various initial deviations and geometrical parameters, which is based on the proposed irregular quadrilateral plate element via absolute nodal coordinate formulation (ANCF).

Two typical clamping schemes are specified for the large-scale thin-walled structures. Two typical deviation modes are defined in both free and clamping states in the corresponding clamping schemes. The new irregular quadrilateral plate element via ANCF is validated to analyze the compliant deformation of assembled structures. The quasi-static force equilibrium equations are extended considering the factors of clamping constraints and geometric deviations.

The initial deviations and geometrical parameters strongly affect the assembly deviations of structures in two clamping schemes. The variation tendencies of assembly deviations are demonstrated in details with the circumferential clamping position and axial clamping position in two clamping schemes, providing guidance to optimize the fixture configuration. The assembly quality of structures with deviations can be improved by configuration synthesis of the clamping schemes.

Typical over-constraint clamping schemes and deviation modes in clamping states are defined for large-scale thin-walled structures. The plate element via ANCF is extended to analyze the assembly deviations of thin-walled structures in various clamping schemes. Based on the proposed theoretical model, the effects of clamping schemes and initial deviations on the deformation and assembly deviation propagation of structures are investigated.

The purpose of this paper is to search an energy balance routing in the wireless sensor networks (WSN) and lengthen the life of the networks.

To save energy in the WSN, some routing protocols search routing with the minimum total energy consumption of the network, and others reduce data redundancy by data aggregation. But if the distribution of energy consumption was not even, the energy of some nodes would be exhausted rapidly and thus the whole network would break down. Thus, an energy balance routing notion, including communication energy cost of the routing, remaining energy of communication sensors and sensor load have been involved. Then a new algorithm, mouse colony optimization and simulated annealing (SA), is advanced to solve the problem of energy balance routing in the network.

The energy balance routing, based on mouse colony optimization and SA, performs well and yields better performance than other congener algorithms.

The appointed times of the algorithm is the main limitation which increase the complexity of the algorithm.

A very useful routing in wireless sensor networks.

The new approach of energy balance routing notion, including communication energy cost of the routing, remaining energy of communication sensors and sensor load. The new algorithm, mouse colony optimization algorithm, simulated mice action, was proposed to solve the energy balance routing of the network.

The purpose of this paper is to apply what can be learned from the emergence of nature tourism to understand some current and future trends of tourism.

This study adopted the evolutionary paradigm for investigation.

The emergence of nature tourism in early medieval China can be attributed to four major factors, including transformation of value orientations, seeking longevity, interest in suburbs and population migration.

Historical studies help understand the current and future trends. When the contributing factors for nature tourism are linked to the contemporary world, it can be found that these factors are still playing a part in shaping tourism trends or patterns in their original or alternative forms. These trends or patterns are worthy of scholarly investigations.

This paper offers a comprehensive understanding of the origins of nature tourism.

Environmental sustainability is quickly becoming one of the most critical issues in industry development. This study aims to conduct a systematic literature review through which the author can provide various research areas to work on for future researchers and provide insight into Industry 4.0 and environmental sustainability.

This study accomplishes this by performing a backward analysis using text mining on the Scopus database. Latent semantic analysis (LSA) was used to analyze the corpus of 4,364 articles published between 2013 and 2023. The authors generated ten clusters using keywords in the industrial revolution and environmental sustainability domain, highlighting ten research avenues for further exploration.

In this study, three research questions discuss the role of environmental sustainability with Industry 4.0. The author predicted ten clusters treated as recent trends on which more insight is required from future researchers. The authors provided year-wise analysis, top authors, top countries, top sources and network analysis related to the topic. Finally, the study provided industrialization’s effect on environmental sustainability and the future aspect of automation.

The reliability of the current study may be compromised, notwithstanding the size of the sample used. Poor retrieval of the literature corpus can be attributed to the limitations imposed by the search words, synonyms, string construction and variety of search engines used, as well as to the accurate exclusion of results for which the search string is insufficient.

This research is the first-ever study in which a natural language processing technique is implemented to predict future research areas based on the keywords–document relationship.