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Generation Z (Gen Z) is entering the hotel workforce and will soon become the dominant group. This has called for a better understanding of this workforce’s attitudes and perceptions towards working in the hotel industry. This study aims to examine the effect of organizational socialization on the retention of Chinese Gen Z employees, the mediating role of person–environment fit (P-E fit) and the moderating effect of career commitment.

Time-lagged data were collected from 426 Gen Z new employees from 20 upscale hotels at two different times (2 weeks and 12 weeks after the employees entered the hotel). Confirmatory factor analysis, structural equation modeling, bootstrapping analysis and moderated hierarchical regression analyses were used for data analysis.

Organizational socialization positively affects employee retention via person–environment fit. Moreover, career commitment positively moderates the relationship between person–environment fit and employee retention.

Hotels must view organizational socialization as a long-term investment in Gen Z talent management by offering effective training through diverse methods, creating a collaborative environment and helping them develop career plans to enhance their career commitment.

This study unpacks the four dimensions of organizational socialization and investigates their differential effects on Gen Z employees’ retention through P-E fit. The moderating role of career commitment is also examined. This study contributes to the growing body of hospitality human resources management research on this new generation of workforce in China.

This research paper aims to explore the concerns and determinants of travelers’ behavior toward ChatGPT in the hospitality and tourism context. It also examines the weight of risk factors versus that of motivation and innovation characteristics influencing travelers’ approach behaviors toward ChatGPT.

A cumulative prospect theory was used to determine travelers’ responses to ChatGPT. This study, using a fuzzy-set qualitative approach, explored risk, motivation and innovation factors as determinants of approach behaviors for ChatGPT.

Findings revealed that risk, motivation and innovation factors were the key triggers of approach behaviors for ChatGPT. An intricate combination effect of the perceived risk, motivation and innovation characteristics was found, and the necessary predictors were determined.

The findings of this study will expand our current knowledge and offer practical insights for the development of ChatGPT in the hospitality and tourism sector.

This study makes a significant contribution to the existing literature by providing a nuanced understanding of the intricate interplay between the various factors that shape customer behavior in the context of technology adoption in hospitality and tourism studies.

The purpose of this study is to investigate the factors affecting behaviour intention (BI) to use and actual usages of investment-related FinTech services among the zoomers (Gen Z) and millennials (Gen M) retail investors of India.

The study explores the predictive relevance of actual adoption behaviour among the two different age categories of Indian retail investors. It uses the Unified Theory of Acceptance and Use of Technology-2 and the prospect theory framework as guiding frameworks. Data has been collected from 294 retail investors, actively engaged in the investment-related FinTech services. The multi-group analysis using variance-based partial least square structured equation modelling has been used to compare the two groups. The invariance between the two groups was achieved through measurement invariance assessment.

The study reveals distinct factors significantly affecting BI to use investment-related FinTech services among Gen Z and Gen M retail investors are performance expectancy (PE) to BI, perceived risk (PR) to BI, price value (PV) to BI and PR to service trust (ST).

This study provides insights for financial providers and policymakers, emphasizing different factors influencing BI to use investment-related FinTech services in both age groups. Notably, habit emerges as a common factor influencing the actual usage of investment-related FinTech services across Gen M and Gen Z retail investors in India.

This study explores the heterogeneous behaviour of the heterogenous population in the domain of technological adoption of investment-related FinTech services in India.

The purpose of this paper is to explore the support for multi-party collaboration in project construction provided by building information modeling (BIM). Based on the perspective of value co-creation, the research results can provide support for the collaborative application and contract design of BIM platform.

In this paper, an evolutionary game model involving the owner, designer and constructor is constructed by using prospect theory and evolutionary game theory. Through simulation analysis, the evolution law of the strategy choice of each party in the collaborative application of BIM platform is discussed and the key factors affecting the strategy choice of all parties are analyzed.

The results show that there is an ideal local equilibrium point with progressive stability in the evolutionary game between the three parties: “the construction party shares information, the designer receives the information and optimizes the project and the owner does not provide incentives”; in addition, the opportunistic behaviors of the design and construction parties, as well as the probability of such behaviors being detected and the subsequent punishment have a significant impact on the evolutionary outcome.

This method can provide support for the collaborative application and contract design of BIM platform.

The use of technology in 4th industrial revolution is at its peak. Industries are trying to reduce the consumption of resources by effectively utilizing information and technology to connect different functioning agents of the manufacturing industry. Without digitization “Industry 4.0” will be a virtual reality. The present survey-based study explores the factual status of digital manufacturing in the Northern India.

After an extensive literature review, a questionnaire was designed to gather different viewpoints of Indian industrial practitioners. The first half contains questions related to north Indian demographic factors which may affect digitalization of India. The latter half includes the queries concerned with various operational factors (or drivers) driving the digital revolution without ignoring Indian constraints.

The focus of this survey was to understand the current level of digital revolution under the ongoing push by the Indian government focused upon digital movement. The analysis included non-parametric testing of the various demographic and functional factors impacting the digital echoes, specifically in Northern India. Findings such as technological upgradations were independent of type of industry, the turnover or the location. About 10 key operational factors were thoughtfully grouped into three major categories—internal Research and Development (R&D), the capability of the supply chain and the capacity to adapt to the market. These factors were then examined to understand how they contribute to digital manufacturing, utilizing an appropriate ordinal logistic regression. The resulting predictive analysis provides seldom-seen insights and valuable suggestions for the most effective deployment of digitalization in Indian industries.

The country-specific Industry 4.0 literature is quite limited. The survey mainly focuses on the National Capital Region. The number of demographic and functional factors can further be incorporated. Moreover, an addition of factors related to ecology, environment and society can make the study more insightful.

The present work provides valuable insights about the current status of digitization and expects to facilitate public or private policymakers to implement digital technologies in India with less efforts and the least resistance. It empowers India towards Industry 4.0 based tools and techniques and creates new socio-economic dimensions for the sustainable development.

The quantitative nature of the study and its statistical predictions (data-based) are novel. The clubbing of similar success factors to avoid inter-collinearity and complexity is seldom seen. The predictive analytics provided in this study is quite elusive as it provides directions with logic. It will help the Indian Government and industrial strategists to plan and perform their interventions accordingly.

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

The paper aims is to see why social conflict caused by class segregation did not occur in Gading Serpong? What factors prevent conflict from occurring? This research seeks to find the causes of the nonoccurrence of social conflict due to class segregation in the Gading Serpong cluster area and explore the factors that restrain conflict there.

This research is qualitative research with data collection techniques through in-depth interviews with several parties identified as brokerages in the research object area. In this context, one of the media and analytical tools is to recognize agents or brokers who connect two groups of people. Brokerage occurs in sectors, patterns or forms of informal, personal relationships; to understand it, one must pay close attention to micro-level relationships and social psychological processes. However, brokerage can have a significant impact on macro-level social relations, as it is generally associated with social integration processes.

The lack of involvement of developers in overcoming social conflicts that occur between Gading Serpong natives and migrants in Gading Serpong housing has given rise to new actors. These new actors are what we can call brokers, where they have a role as brokers who are able to connect between migrants and natives in the Gading Serpong area. The broker phenomenon is actually familiar in academia, where in practice the broker acts as someone who is able to find solutions to problems. The broker is the reason even social segregation is created between migrant citizens and native citizens in Gading Serpong but never becomes a conflict between them.

Even if the brokerage phenomenon is the reason why there is no conflict over social segregation brokerage is not the only factor in this nonconflict segregation. Therefore, to cover the larger area of these suburban segregation problems, there must be further research on this topic.

The practical implication of this research is to encourage the housing developers that create urban housing, such as clusters or other gated communities, to evaluate the social factors, such as potential segregation and conflict management. Also to encourage the developers to get involved and create some social engineering systems, like brokerage, market and other social agents, to create some nonconflict segregation or even more inclusive communities.

This research is uncovering the main reason why social segregation between migrant and native people in Gading Serpong, which could potentially lead to conflict, is never a conflict. The main reason is social actors like brokerage.

This paper aims to explore the opportunities and challenges associated with adopting artificial intelligence (AI) in libraries in Bangladesh and provide recommendations to guide the responsible integration of AI to enhance library services and accessibility.

The paper reviews relevant literature on the applications of AI in libraries, the current state of technology adoption in Bangladeshi libraries and the ethical considerations surrounding AI implementation. It analyzes the potential benefits of AI tools such as chatbots, intelligent search engines, text-to-speech and language translation for improving user services and inclusion. The challenges of infrastructure constraints, lack of resources and skills, data privacy issues and bias are also examined through the lens of the Bangladeshi context.

AI offers transformative opportunities to automate operations, strengthen user services through 24/7 virtual assistants and personalized recommendations and promote accessibility for diverse users in Bangladeshi libraries. However, significant challenges such as inadequate technology infrastructure, funding limitations, shortage of AI-skilled staff, data privacy risks and potential biases must be addressed. Strategically planning for sustainable implementation, building AI capacity, prioritizing ethical AI development and fostering collaborations are critical factors for successful AI adoption.

This paper provides an in-depth analysis of the prospects and obstacles in leveraging AI specifically for libraries in Bangladesh. It offers original insights and context-specific recommendations tailored to the needs and constraints of a developing nation working to harness AI’s potential to create dynamic, inclusive knowledge centers serving all communities.

This study aims to analyze the factors driving Syrian refugees into the informal labor market in Türkiye despite the existence of regulations and programs to facilitate their integration into the formal labor market.

This study presents results from a literature review of secondary sources and primary data collection through semi-structured interviews with key stakeholders and Syrian refugees.

The study shows that the implementation of policies and programs to boost formal employment among refugees has yielded limited results. Many refugees continue to operate within the informal economy. This informality is due to various socio-economic challenges, including anti-refugee sentiments, geographical restrictions and economic crises. The 2023 twin earthquakes have further exacerbated the vulnerable situation of refugees, intensifying the difficulty of achieving self-reliance.

The study’s drawbacks include a small sample size. This implies that the sample is not representative; therefore, results may lack generalizability.

The study’s findings could stimulate greater engagement in public policy, facilitate the management of public perceptions regarding refugees and provide support to the private sector, all to enhance the integration of Syrian refugees into the formal labor market.

This study addresses crucial areas previously unexplored, including the impact of economic and natural disaster crises on the labor market integration of refugees. To the best of the author’s knowledge, by investigating these factors for the first time, this study offers novel insights into their influence on refugees’ labor market integration.

The purpose of this paper is to scrutinise the effectiveness of four derivative exchanges’ enforcement efforts since 2007. These exchanges include the Commodity Exchange Inc. and ICE Futures US from the United States and ICE Futures Europe and the London Metal Exchange from the UK.

The paper examines 799 enforcement notices published by four exchanges through a behavioural science lens: HUMANS conceived by Hunt (2023) in Humanizing Rules: Bringing Behavioural Science to Ethics and Compliance.

The paper finds the effectiveness of the exchanges’ enforcement efforts to be a mixed picture as financial markets transition from the digital to artificial intelligence era. Humans remain a key cog in the wheel of market participants’ trading operations, albeit their roles have changed. Despite this, some elements of exchanges’ enforcement regimes have not kept pace with the move from floor to remote trading. However, in other respects, their efforts are or should be, effective, at least in behavioural terms.

The paper’s findings are arguably limited to exchanges based in Anglophone jurisdictions. The information published by the exchanges is variable, making “like-for-like” comparisons difficult in some areas.

The paper makes several recommendations that, if adopted, could help exchanges to increase the potency of their enforcement programmes.

A key aim of the paper is to shift the lens through which the debate concerning the efficacy of exchange-level oversight is conducted. Hitherto, a legal lens has been used, whereas this paper uses a behavioural lens.