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The examination of revisit intentions in hospitality is integral to relationship marketing and customer loyalty. Its measurement and determination have largely been done through closed-ended measures in surveys of customers. However, vast troves of consumer-generated media in the form of open-ended text reviews can also serve as sources for the determination of revisit intentions. The purpose of this paper is to develop and test a rule-based classification model from big data to extract revisit intentions.

Data for this came from 116,241 reviews scraped from Tripadvisor.com using a stratified sampling technique comprising hotels in major cities in the USA. A sample comprising 1,800 reviews was randomly drawn from this larger pool of reviews and manually annotated. A manual-set rule-based model, supervised machine learning (ML) models and hybrid models were developed to extract revisit intention.

The hybrid model of the MSRB method complemented by the gradient boosting ML method performed the best to classify revisit intentions in reviews.

This study’s rule-based classification model can be used by hotels to evaluate revisit intentions from the ever-growing pool of consumer-generated reviews. This can enable hotels to identify drivers of re-patronage and enhance relationship marketing initiatives.

This study is the first to propose an analytical model that taps big data to extracting revisit intentions. In the past, revisit intentions have been assessed using closed-ended questions using traditional survey-based methods.

Off-premise restaurant service has a new addition – food delivery robots. This new technology and off-premise service, in general, has received little research attention, despite continued year-over-year sales growth for both carry-out and delivery. Therefore, this study aims to analyze off-premise service modes, including food delivery robots, for service quality gaps between consumer expectations and actual performance and among the various modes.

Performance of three off-premise restaurant service modes (robot-delivery, human-delivery and carry-out) were evaluated using a mixed-methods approach. Consumer expectations were ascertained about food- and service-related performance factors using a survey, and a field observation study was conducted to obtain actual performance data for these factors. Findings from both approaches were compared to identify gaps and differences.

For food-related performance, consumers reported lower expectations for food safety and food quality from robot- and human-delivered food; however, no differences were observed among the three modes in the field study. Consumers also expected lower service-related performance from robot-delivery for service efficiency and ease of use (than human-delivery and carry-out) and monetary value (than carry-out). Consumers deemed robots the most sustainable and human-delivery the most convenient compared to other modes – however, not all service-related expectations aligned with actual performances.

This study was the first to comparatively examine off-premise restaurant service. Identification of a missing link in service gap analysis was among the theoretical contributions of this study. Managerially, this study provides previously unavailable insights into opportunities for improvement for off-premise service and use of delivery robots.

Drawing on protection motivation theory (PMT) and expectancy theory, this study aims to investigate consumers’ lodging consumption intentions during a pandemic crisis.

The research survey was conducted during the ongoing COVID-19 pandemic (August, 2020) in the USA to investigate consumers’ lodging intentions amidst the pandemic crisis. PLS-SEM and multiple regression were conducted to explore answers to proposed research questions.

The research finds that, during the pandemic crisis, consumers’ lodging consumption intentions are subject to both their internal protection motivation evaluations and the external prevention practices implemented by the lodging facilities regardless of property types and travel purposes. Notably, the research finds that consumers are willing to make efforts and pay extra for a safe stay at lodging facilities.

This research contributes to the literature regarding the applications of PMT and expectancy theory from a crisis management perspective, and it provides implications and guidelines to the crisis management practices in the lodging industry.

The current research examines the specific protection motivation appraisal factors and prevention practices significantly impacting consumers’ decisions in terms of willingness to stay at various lodging facilities (hotels and non-hotel homestay/short-term rental properties) for different purposes (business and leisure) and to pay premiums for a safe stay.

Reading represents a basic way by which humans understand the world and acquire knowledge; it is also central to learning and communicating. However, with the rapid development of mobile reading, an individual's cognition of objective facts may be affected by the reading environment and text genre, resulting in limited memorization and understanding of the reading material. Therefore, this study aimed to investigate the influence of the reading environment and text genre on individuals' cognitive activities from the perspective of motivational activation level using evidence from electroencephalography (EEG) signals.

The study employed a mixed design experiment with two reading environments (quiet and distracting) between subjects, two text genres (entertaining and scientific) within subjects and two reading tasks (memory recall and comprehension) within subjects. There were 50 participants in the experiment, and the data obtained from 44 participants while they read the materials and completed the reading tasks were analyzed.

The results showed that readers are more positively motivated to read in a quiet reading environment than in a distracting reading environment when facing the memory recall tasks of entertaining genre passages and comprehension tasks of scientific genre passages. Entertaining genres are more likely to arouse readers' reading interest but hinder the memory recall of the content details. While scientific genres are not easy to understand, they are helpful for working memory.

This study not only applies a new technology to mobile reading research in the field of library science and addresses the limitations of self-report data, but also provides suggestions for the further improvement of mobile reading service providers. Additionally, the results may provide useful information for learners with different learning demands.

Production and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy.

In this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data.

The optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%.

This study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components.

Based on the “S-O-R” and attachment theories, this paper constructs a research model of the platform attribute factors of sports short video live e-commerce on consumers' psychological conditions, and explores how platform attributes affect consumer behavior through consumer attachment.

The study carried out questionnaire survey through the “snowball” method, and a total of 422 valid questionnaires were collected. The paper uses SPSS 26.0 and AMOS 26.0 to adapt the data and model, and adopts the method of structural equation modeling for analysis.

The research results show that the interactivity, identity, personalization and entertainment of live sports e-commerce platforms can actively stimulate consumer attachment, and directly drive their consumption behavior through their attachment.

Examining the platform attributes and system functions of short video live broadcast e-commerce from the perspective of consumer attachment can help sports e-commerce understand consumers' needs and satisfaction with the functions provided by the platform. Through timely optimization and improvement of system functions, the platform will make the connection between consumers and e-commerce closer, thereby increasing consumer stickiness and promoting the vigorous development of sports e-commerce.

This study identified and defined the platform attributes contained in the sports live broadcast e-commerce platform, combined the S-O-R model with the attachment theory, expanded the analytical framework of the S-O-R theory and made contributions to the extension of the attachment theory.

This study aims to identify articles examining human-robot interaction and the effects of robotic systems on employment.

In this research, electronic searches were performed for articles published between 2000 and 2022 in Emerald, Springer, PubMed, Science Direct, Wiley and Google Scholar. In the searches of robotic systems with keywords such as “motivation, job satisfaction, job loss, performance, job giving,” 5 quantitative and 5 qualitative studies were included in the systematic review. The selected research was conducted using the Johanna Briggs Analytical Cross-Sectional Studies Checklist from the Joanna Briggs Institute (JBI) critical evaluation lists and the JBI Critical Appraisal Checklist for Qualitative Research, depending on their type. The included studies are mostly on employee-robot collaboration.

Although the majority of the articles examined in this study are included in keywords or titles, it is determined that there is a gap in descriptive quantitative studies in the literature on the effects of employee-robot collaboration, robotic systems and robotic systems on variables such as motivation, job satisfaction, job loss, performance and employment, although they do not mention a framework that directly investigates human-robot interaction and the effects of robotic systems on employment.

There are several limitations in this study. One of them is that, although the databases are comprehensively scanned, only studies published in English between 2000 and 2022 are included in the systematic review. Another limitation is the heterogeneity between studies.

As a result of the authors’ findings, the practical effects of the research are reflected as follows: It serves as a guide for future studies to fill the gap in the field, especially for academics and researchers working in the field of social sciences on robotic systems and intelligent automations. In addition to the qualitative studies on this subject, there is a need for the use of robotic systems in the field of human resources and management and quantitative studies with more sample sizes, especially at the corporate (firms) and individual (employees) level. Considering that the number of studies on this subject is very insufficient, this research is important in terms of shedding light on future studies.

The authors believe that the impact of robotic systems on employment is one of the few conceptual articles that systematically examines 6 dimensions (job satisfaction, performance, job loss, employment, motivation, employment).

Digital transformation is a confidence booster in intrapreneurship, but few have examined its impact on intrapreneurship. Further, quantitative analyses exploring the impact of Chinese enterprises' digital transformation on intrapreneurship at the micro-level are rare. Most enterprises do not have the dividend for digital transformation, and few enterprises have successfully achieved digital transformation through intrapreneurship, internal management re-engineering and technological innovation. This study investigates the effect of digital transformation on intrapreneurship in Chinese real economy enterprises.

The study develops and tests a theoretical model that digital transformation impacts intrapreneurship by promoting working capital turnover and furtherly influencing labor input. Panel data of 1,638 Chinese-listed companies between 2007 and 2020 were used to complete the empirical test.

Digital transformation impacted labor input, with an inverted-U shaped relationship between the two, and labor input significantly stimulated intrapreneurship. This effect promoted labor input's impact on working capital. Chinese real economy enterprises generally increase labor investment to promote intrapreneurship. Heterogeneity analysis revealed that enterprises' asset scale and ownership attributes uniformly affected labor input.

This study provided empirical evidence of the promotional effect of real economy enterprises' digital transformation on intrapreneurship. Further, it advanced the literature by examining this relationship at the micro-level. Moreover, the data sample was long-term and included most industries, thus providing representative results with practical implications.

Existing research neglected examining the environmental effect of an event on the effectiveness of sponsorship activation in a competitive setting. The purpose of this study is to explore how the event environment impacts consumers’ attitudinal and behavioral responses to competitive brands that co-present at an event.

The research comprised an exploratory pre-test and two studies at a sport event with a retailing environment. The exploratory pre-test was used to examine the competitive relationship in the local market between the market leader and the lesser-known sponsoring brand. Study 1 used structural equation modelling to test how the event environment impacts consumers’ attitudes toward both brands at the post-consumption stage. Study 2 compared actual sales data of the two competing brands to examine the immediate effect of the sponsorship space on consumption.

The results revealed the event environment had an impact on consumers’ brand attitude toward both the lesser known sponsoring brand and the non-sponsoring market leader. However, the effect on the sponsoring brand that activated its sponsorship was influenced by consumer involvement with the event and was more salient. Furthermore, the product sales of the less-known sponsoring brand outperformed that of the market leader that co-presented at the event.

This study addresses a call to go beyond exploring the brand image of the sponsoring brands in isolation and holistically examine sponsorship effectiveness. The study contributes to knowledge on both attitudinal and actual behavioural outcomes of sponsorship activation in a competitive environment.

With the rapid development of paid online social question and answer (Q&A) communities, monetary social functions have been introduced and have potential benefits for both platforms and users. However, these functions' impact on knowledge contribution remains uncertain. This study proposes a conceptual model based on the stimulus–organism–response framework, according to which monetary and non-monetary social functions can help nurture short-term and long-term relationships among community users, and thereafter improves social identity and knowledge-sharing intentions.

This study selects Zhihu, a famous online social Q&A community in China, and conducts an online survey to collect data from its frequent users. A sample of 286 valid questionnaires was collected to test our research model by using a structural equation modeling method. In addition, a bootstrapping approach is used to test the mediation effect.

Results indicate that monetary social functions help nurture short-term and long-term relationships among community users. However, non-monetary social functions only affect short-term relationships directly. Short-term and long-term relationships both have a positive relationship with social identity and thereafter improve users' knowledge-sharing intentions.

This study focuses on users' knowledge-sharing intentions in Q&A communities from the perspective of social. Specifically, we separated social functions in Q&A platforms into monetary and non-monetary ones and explored their impact on the development of short-term and long-term relationships. Results demonstrate the importance of monetary social functions and explain how monetary and non-monetary social functions affect users' knowledge-sharing intentions in different approaches.