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The purpose of this paper is twofold. The first is to review the extant literature on hospitality management by tracking past research patterns and critically reviewing the use of multilevel theory and techniques in this stream of research. The second is to suggest potential research opportunities to stimulate a leap forward in the current multilevel research.

To answer the four main research questions raised by the current review, the author performed a critical analysis of a total of 149 selected articles published between 2011 and 2021 in seven leading hospitality management journals.

Overall, the number of multilevel studies has increased significantly since 2017. However, some deficiencies remain: a lack of fit between the level of theory and the level of measurement, the revelation of insufficient information, misspecification of the multilevel model and small sample sizes at higher levels. Furthermore, several interesting and understudied topics are also identified as ripe for future investigation.

In addition to encourage the scholars in hospitality management to assess the possibility of using the multilevel research design for their research topics, the current article also provides recommendations and opportunities for the future multilevel research.

This article is a pioneer in providing a critical synthesis of multilevel research in the field of hospitality management. Although reviews of the issues involved in multilevel research are available in the existing literature, none of them focuses on the situation and needs of hospitality management. As multilevel research increases in popularity, this review offers a snapshot of the introductory phase and outlines important issue in conducting such research.

This study aims to extend the cognitive appraisal theory by developing and validating a conceptual framework to illustrate how travelers' behavioral intention is generated via a multi-stage evaluation of health-related variables.

SEM and moderator analysis were conducted to examine the theoretical framework (post-intervention event travel intention) and to investigate how the appraisal process differs across travelers with various attitudes toward vaccination.

This study found that cruise travel intention was positively influenced by the perceived hedonic value and perceived trustworthiness and negatively influenced by perceived infection risk. Furthermore, whereas perceived hedonic value, perceived trustworthiness and perceived risk of infection were all predicted by crisis management, the dimensions of crisis management operated differently. In addition, vaccination attitudes amplified the unfavorable effect of perceived risk on intention.

Drawing on the CAT, this study developed and validated a conceptual framework to integrate crisis management with customers' behavioral intentions. This study extends existing cruise travel intention theory by demonstrating how post-pandemic travelers' behavioral intention is generated via a multi-stage appraisal-reappraisal process based on the evaluations of infection risks and cruise line crisis management.

This study investigates into how organizations enable the compatibility between intermediary role of conventional systems with disintermediary potentials of blockchain toward the coordination of multiple actors in operations management.

The data were collected from 31 interviewees of the case organizations. We conduct an in-depth case study of successful BC implementation in operations management, by using affordance-actualization (A-A) theory as the theoretical lens.

This study identifies the incompatibility between the affordances of conventional systems and blockchain in coordination/operations management and offers a process model in which a fusion phase enables the affordances to be compatible and then to be actualized. The fusion phase extends A-A theory by transposing and connecting in the context of operations management. The result also shows that blockchain technology has decentralized potentials to address the issues caused by centralized organizations or information systems, while not to replace the intermediary roles of centralized organizations or information systems.

This study makes important theoretical contributions to the literature on blockchain used in operations management, the roles of blockchain enablement and affordance-actualization theory. The findings can also help IT practitioners to implement BC-based applications effectively.

This paper aims to contribute to the social aspect of corporate social responsibility literature by examining the influence of financial analysts’ spirituality on their socially responsible investing (SRI) decisions relating to a profitable organization, which is alleged by the media to employ children as laborers in hazardous works in Pakistan. This study also investigates whether analysts’ social consciousness mediates between their spirituality and investing decisions.

A scenario-based survey was administered to 124 financial analysts at leading financial institutions in Pakistan. Data were analyzed using regression, analysis of variance and mediation analysis on SPSS 26.

The findings demonstrate that financial analysts’ spirituality negatively influences their SRI decisions to invest in a profitable organization, which is alleged to employ children in hazardous work that may harm them physically and psychologically. The findings also express that analysts’ social consciousness intervenes in the association between analysts’ spirituality and SRI decisions.

The findings of this study may interest regulators, multinational firms and researchers in recognizing the importance of individuals’ values for increasing socially responsible investments and addressing social issues such as the exploitation of children.

This study encourages firms to recognize the importance of spiritual and socially conscious corporate conviction while designing strategies and policies. For example, the financial industry may incorporate fundamental personal values such as stewardship, dignity and fairness into its investment plans.

This study provides rigorous insights and contributes to contemporary studies by providing empirical evidence that individuals’ intrinsic values and consciousness drive their judgments.

This paper aims to conduct a systematic literature review of Socially Conscious Investment (SCI) articles published in premier journals. Its objective is to shed light on the publication trend, leading authors, journals, countries and themes in contemporary SCI research. The article also provides a conceptual model of SCI to enhance understanding of the knowledge structure and the future research direction.

A systematic review followed the PRISMA guidelines and encompasses 264 full-text articles indexed in A* and A category journals listed in ABDC is reviewed. The literature synthesis adopts the theories, contexts, characteristics and methodology (TCCM) framework.

The article has identified the research trends related to author impact, journal impact, article impact and the outcomes derived from the TCCM framework. Additionally, it highlights three key themes: Performance of SCI, Behavioural issues and SCI development literature.

The insight on various aspects of SCI was explored for a comprehensive understanding. The authors also developed a conceptual model for socially conscious investment.

The study aims to evaluate how different types of assurors and investors’ Big Five personality traits affect the relationship between levels of assurance for Greenhouse Gas (GHG) reporting and individual investors' decision-making in social responsible investment (SRI).

This study adopted an experimental approach with 315 individual investors as participants.

The results show that there are some differences in the individual investors’ decision-making. Accountants are the preferred assuror. Type of assuror and level of assurance for GHG reporting affect investors’ decision-making, in the presence of different levels of investors’ personality traits, extraversion, openness, conscientiousness and neuroticism. However, individual investors with different levels of agreeableness do not have similar influence.

This study extends the literature on individual investors’ decision-making in socially responsible investment by examining the combination of the type of assuror, level of assurance and investors’ personality traits. This study also observes three different assurors, accountants, engineers and specialists and four different assurance levels – reasonable, hybrid, limited and not specified.

This study analyses the role of individuals' objective financial knowledge in shaping preferences for ethical intermediaries and sustainable investments in Italy. Another goal of this study is to assess the impact of individuals' misperceptions about their own financial knowledge and to test for gender-related differences in attitudes towards socially responsible investing (SRI).

Using nationally representative microdata from the Bank of Italy’s “Italian Literacy and Financial Competence Survey” (IACOFI), the authors use probit models, extended to account for potential endogeneity issues, to assess the causal effects of financial knowledge and confidence on stated preferences for SRI. Empirical models also allow to explicitly assess the moderating role of gender on the effects of financial knowledge and confidence on attitudes towards sustainable investing.

Results indicate that individuals' preferences for sustainable finance significantly increase with financial knowledge, suggesting that inadequate financial competencies represent a barrier to participation in SRI. At the same time, lack of confidence in one’s own financial knowledge significantly hampers attitudes towards sustainable investments. Furthermore, the authors show that women have a greater preference for sustainable finance than men and point out that financial knowledge and confidence exert heterogenous effects on attitudes towards SRI.

This study provides several contributions to the literature on SRI. First, the authors give evidence of the causal effect of financial knowledge on preferences for both ethical financial intermediaries and sustainable investments. Moreover, this is the first study to investigate the role of financial underconfidence bias in shaping individuals' SRI attitudes. Finally, extending previous research, the authors assess differences in SRI preferences between women and men and provide novel evidence on gender-related heterogeneity in the effects of financial knowledge and underconfidence.

We applied a true-experimental randomized control posttest design to collect and analyze quantitative and qualitative data to compare the effects of the AR and traditional board games on students’ SDG learning achievements.

We applied a true-experimental randomized control posttest design to collect and analyze quantitative and qualitative data to compare the effects of AR and traditional board games on students' SDG learning achievements.

Our analysis of the quantitative and qualitative data revealed that the effects of AR and traditional board games on the students' cognitive outcomes differed significantly, indicating the importance of providing a situated learning environment in SDG education. Moreover, the students perceived that the incorporation of the AR game into SDG learning improved their learning effectiveness – including both cognitive and affective dimensions – thus confirming its educational value and potential in SDG learning.

To the best of our knowledge, this is the first study to explore the effectiveness of different learning tools (AR and traditional board games) and to evaluate the importance of providing a situated learning environment through a true-experimental randomized control posttest design.

This study aims to enhance the lubrication performance of thrust bearings. The influence of columnar convex–concave compound microtexture on bearing performance is investigated

Based on the compound microtexture model of thrust bearings, considering surface roughness and turbulent effect, the variation of lubrication characteristics with the change in the compound microtexture parameters is studied.

The results indicate that, compared with circular microtexture, the maximum pressure of compound microtexture of thrust bearings increases by 7.42%. Optimal bearing performance is achieved when the internal microtexture depth is 0.02 mm. Turbulent flow states and surface roughness lead to a reduction in the optimal depth. The maximum pressure and load-carrying capacity of the bearing decrease as the initial angle increases, whereas the friction coefficient increases with the increase in the initial angle. The lubrication performance is best for bearings with a circumferential parallel arrangement of microtexture.

The novel composite microtexture with columnar convex-concave is proposed, and the computational model of thrust bearings is set. The influence of surface roughness and turbulent flow on the bearing performance should be considered for better conforming with engineering practice. The effect of microtexture depth, arrangement method and distribution position on the lubrication performance of the compound microtexture thrust bearing is investigated, which is of great significance for improving tribology, thrust bearings and surface microtexture theory.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.