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Drawing on the theory of memory-dominant logic, this study aims to examine how the substantive staging of the servicescape, experience co-creation, experiential satisfaction and experience intensification affect experience memorability and hedonic well-being in the case of unmanned smart hotels.

An online survey was used, with the target respondents being hotel guests people aged 18 years and older who had been recent guests of the FlyZoo Hotel in Hangzhou, China. Data were collected online from 429 guests who had stayed in the hotel between April and June 2023. Data analysis was undertaken using structural equation modelling.

The results suggest that all the proposed four constructs are positive drivers of a memorable unmanned smart hotel experience. The relationship between the memorability of the hotel experience and hedonic well-being was found to be significant and positive.

Unmanned smart hotels should ensure that all smart technologies function effectively and dependably and offer highly personalised services to guests, allowing them to co-create their experiences. This will lead to the guest receiving a satisfying and memorable experience. To enable experience co-creation using smart technologies, unmanned smart hotels could provide short instructional videos for guests, as well as work closely with manufacturers and suppliers to ensure that smart technology systems are regularly updated.

This study investigates the antecedents and outcomes of a novel phenomenon and extends the concept of memorable tourism experiences to the context of unmanned smart hotels.

This study aimed to assess the influence of the community-based campaign on weight loss and healthy lifestyle adoption among Bahrain's adult population.

A cross -sectional self-reported online questionnaire completed in February 2021. The survey evaluated the impact of the community-based campaign health program which includes (exercise, diet plan and psychological eating behavior) weight reduction using social media platforms. The authors employed data from young and middle-aged healthy adults (n = 842) between the ages of 18-55 years, of both sexes. The intervention group (n = 842) was made up of the supporters of the voluntary community initiative called Obesity does not Suit Me (n = 194), and the control group (n = 648) was made up of non-followers of the campaign.

The study showed a statistically significant difference among the followers of the community-based campaign health program in the following parameters: 3.90-4.23 kg less, 1.46-1.59 difference in BMI and 0.05-0.06 WHR. All changes were of low effect size.

Diet and exercise had significant impact on weight, BMI and WHR among the followers of the community campaign. However, more research is required for sponsorship to increase the motivation and rewards for the community campaign.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

In the Global North, affordable housing has evolved and thrived, and it is now gaining traction in the Global South, where governments have been vocal supporters of the concept. Therefore, this paper aims to investigate the important criteria for selecting affordable housing units in Ghana.

A quantitative research approach was used, and a survey was administered to the residents. The data was analysed using both descriptive and inferential statistics. The relative importance index technique was used to rank the important criteria, and the EFA technique was used to create a taxonomy system for the criteria.

The hierarchical ranking of the most significant criteria for selecting affordable housing includes community safety, waste management and access to good-quality education. Furthermore, the important criteria for selecting affordable housing are classified into two groups, namely, “sustainability criteria” and “housing demand and supply and social service provision”.

This study has implications for the real estate industry and construction stakeholders, as this will inform decision-making in terms of the design of affordable housing and the suitability of the location for the development.

These findings provide a baseline to support potential homeowners and tenants in their quest to select affordable housing. Furthermore, these findings will aid future longitudinal research into the indicators or criteria for selecting suitable locations for the development of low- and middle-income housing.

The user experience during the use of activities and services is a fundamental aspect for sports managers and can provide a competitive advantage. The purpose of this study was to identify the dimensions of experiential quality and the relationship of this construct with customer trust and customer satisfaction in achieving behavioral intention.

Using a convenience sampling technique, a total of 322 gym users in Turkey participated. A two-step approach was used to test both the model and the research hypotheses [confirmatory factor analysis (CFA) and structural equation modeling (SEM)].

The interaction quality, physical environmental quality, outcome quality and enjoyment quality were positively related to experiential quality. Similarly, the experimental quality was positively related to customer satisfaction and customer trust. Finally, customer satisfaction was related to behavioral intentions.

This study provides empirical evidence about the importance of experiential quality to gain a competitive advantage in the context of fitness centers.

This paper explores the potential that block teaching offers to enhance employability in the context of large-scale classes. It suggests that block teaching, with its condensed structure, necessitates curriculum innovation, fosters participatory learning and peer-to-peer networking, and has been shown to increase student focus and enhance engagement and attainment, especially amongst diverse learners. As these are the same challenges that large-scale teaching faces, it is proposed that intensive modes of delivery could be scaled up in a way that may help to mitigate such problems as cohorts in business schools continue to increase in size.

The paper is based on secondary research and provides an overview of literature that looks at block teaching, followed by that which explores the challenges of large-scale teaching contexts. It compares and contrasts the gaps in both to suggest a way that they could be combined.

The paper provides key insights into changes in the contemporary landscape of teaching within UK business schools, which have seen increasingly large cohorts and draws out the key strengths of intensive modes of delivery, which include helping students to time manage effectively, encouraging curriculum innovation and the creation of participatory learning opportunities as well as providing closer personal relationships between students and staff. Outlining some of the well-documented issues that can arise when teaching larger cohorts, the paper suggests that scaling up blocked delivery may offer a new way help to overcome them.

Because of the chosen research approach, the research results are subject to generalisation. Therefore, researchers are encouraged to test the proposed propositions in large-scale teaching scenarios.

This paper includes implications for the development of innovative modes of teaching in the context of large cohorts, an experience that is increasingly common amongst British business schools and beyond.

This paper brings together two bodies of literature for the first time – that of intensive modes of teaching and that focuses on large-scale teaching contexts – for the first time to show how the former may help to overcome some of the key issues arising in the latter.

Evidence-based practice is a professional standard for occupational therapists, but limited time, resources and knowledge challenge its implementation. This study aims to identify what free evidence summary sources (FESS) can be found through a simple online search, related to child/youth interventions surrounding cerebral palsy (CP), autism spectrum disorder (ASD), developmental coordination disorder (DCD), mental health or attention-deficit/hyperactivity disorder (MH/ADHD). Evidence summaries share research in concise, time-efficient manners.

An internet-based scoping review was conducted between February 2022 and July 2022, using Google, and known evidence summary producers. Evidence summaries meeting the inclusion criteria were located and catalogued. Type of agency, target audiences, purpose and distribution of evidence summaries related to diagnosis were identified for each FESS.

Ten FESS were found, which produced 113 intervention-focused evidence summaries within the past 10 years. These FESS were aimed at a variety of target audiences: service providers, service users, parents/families, researchers and commissioners, and were produced primarily by non-profit/charity organisations (6 of 10) who were trying to fill a gap in evidence. Forty-eight evidence summaries were related to ASD, 34 to CP, 29 to MH/ADHD and two to DCD.

A catalogue of FESS that exist online was produced, to support evidence-based practice for paediatric occupational therapists with limited resources, and may support improved health promotion and informed decision-making for service users. No consistent framework for FESS evidence summaries exists at this time.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

The growing awareness of climate risks associated with food safety issues has drawn the attention of stakeholders urging the food industry to carry out a sustainable food safety management system (FSMS). This study aims to investigate whether the critical success factors (CSFs) of sustainable FSMS can contribute to achieving climate neutrality, and how the adoption of FSMS 4.0 supported by the Industry Revolution 4.0 (IR 4.0) technologies moderates the impact of the CSFs on achieving climate neutrality.

Survey data from 255 food production firms in China and Vietnam were utilised for the empirical analysis. The research hypotheses were examined using structural equations modelling (SEM) with route analysis and bootstrapping techniques.

The results show that top management support, human resource management, infrastructure and integration appear as the significant CSFs that directly impact food production firms in achieving climate neutrality. Moreover, the results demonstrate that the adoption of FSMS 4.0 integrated with the three components (ecosystems, quality standards and robustness) significantly moderates the impact of the CSFs on achieving climate neutrality with lower inputs in human resources, infrastructure investment, integration and external assistance, and higher inputs in strengthening food safety administration.

This study provides empirical findings that fill the research gap in understanding the relationship between climate neutrality and the CSFs of sustainable FSMS while considering the moderating effects of the FSMS 4.0 components. The results provide theoretical and practical insights into how the food production sector can utilise IR 4.0 to attain sustainable FSMS for achieving climate neutrality.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.