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The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.

Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.

The emergence of cryptocurrency has developed a new payment system that is changing how financial transactions happen in hospitality. Consumers/travelers have started experimenting with cryptocurrency payments in hotels and restaurants. However, extant research is lacking in understanding the consumer adoption intention of cryptocurrency payments. This study investigates the intention to use cryptocurrency payments in the hospitality industry.

The conceptual model in this study is based on the Behavioral Reasoning Theory, and it explores the motivating and deterring factors influencing the adoption of cryptocurrency payments in the hospitality industry. A quantitative survey was conducted among 1,080 consumers to examine and confirm the model, with data being analyzed through the Partial Least Squares Structural Equation Modeling (PLS-SEM) method.

The outcome of this work showed that the “reasons for” positively influence and “reasons against” negatively influence consumers’ attitudes and use intentions. Consumers’ values of openness to change positively influence the “reasons for” and do not influence the “reasons against” and attitude toward the use of cryptocurrency payments.

This work contributes to practice by providing insights to customers (users/payee), hospitality managers (investors) and organizations/firms (receiving crypto payments) as well as to financial firms and the government.

This research contributes to cryptocurrency payment adoption and behavioral finance literature. The research uniquely provides the adoption and inhibiting factors for cryptocurrency payment in an integrated framework in the hospitality sector.

This study explores the beliefs of ‘high expectation teachers’, and the practices through which teachers aim to build an inclusive learning environment in addition to the ways they develop strategies that do not rely on pre-determined ability labelling. The study is a case study design focused on one phenomenon, that of the beliefs and practices of high-expectation teachers, and one bounded case illustrates the phenomenon. The case is specific and bounded by time and location. It emphasises uniqueness through the in-depth exploration of the participants' experiences. Following the use of thematic analysis to analyse data collected through questionnaires, interviews and focus groups, the phenomena of high teacher expectation remained only partially scrutinised in terms of social justice. Therefore, the social concerns raised throughout this study are also explored through the theories of Bourdieu, to make sense of the wider issues of inequality inherent in this study. Habitus is helped by, and helps shape, pedagogical action. Findings include the requirement to recognise that in education, socially advantaged interests and voices dominate in terms of social mobility agendas. Furthermore, teachers are only granted space in the public domain through technical competency. Teachers must however be emotionally committed to different aspects of their jobs, as their sense of moral responsibility lies at the core of their professional identity.

Care of complex patients requires collaboration across hospital and community settings. Yet there is little recognition of the capabilities that healthcare workers need to effectively implement integrated care. An adaptive expertise theoretical framework can inform educational efforts that aim to give providers the abilities to navigate complexity and ambiguity in the healthcare system, including across hospital and community settings. Prior education research in the HIV sector has demonstrated that adaptive expert skills can be cultivated through education that emphasizes perspective exchange, inviting uncertainty in practice and integration of diverse perspectives on care.

These principles informed the creation of an Extension for Community Healthcare Outcomes (ECHO) in HIV Psychiatry – the first ECHO directed at a non-clinical, community health worker (CHW) audience. The goal was to improve informal collaborations across hospitals and communities.

Participation in the ECHO was robust, with significant on-camera engagement. Participants attributed success of the ECHO to key themes: explicit value placed on all kinds of knowledge (not simply clinical knowledge), emphasis on approaches for navigating ambiguity and complexity and engagement in perspective exchange for provision of integrated, team-based care. Future cycles of ECHO HIV Psychiatry are being pursued, with a focus on the development of adaptive expert capabilities and the impact on integration of care between community and hospital services.

To our knowledge, this is the only ECHO that is specifically aimed at frontline CHWs.

Employees’ personalities towards occupational outcomes have significantly gained academic attention. Personality traits (PTs) of employees as the determinant of corporate entrepreneurship (CE) outcomes research domain is emerging. This paper aims to assess extraversion PT activation for CE outcome under the problem-solving conflict handling style (CHS) situational cue.

The survey was conducted in automotive manufacturing firms in Pakistan for cross-sectional data collection. Structural equation modelling was used to assess the hypothesised theoretical model from 376 employees.

Results revealed a positive and significant impact of extraversion on CE. Also, employees with high extraversion traits were found to prefer the problem-solving style in managing workplace conflicts. The extraversion trait was also found to be activated under the situational cue of the problem-solving style.

The present study draws implications on person-job fit conceptions at the workplace while hiring, promoting and appointing employees. Personality assessment at the workplace is still in infancy in developing countries. The benefits of employees’ personalities can positively impact organisational performance measures like CE.

The present study has proposed the theoretical model based on Trait Activation Theory (TAT) and contributed theoretically through empirically testing the theoretical model by investigating the employees’ CHS role in activating their PTs towards the CE outcomes. Theoretically, using TAT conceptualisations in entrepreneurship and CE is novel as previous knowledge on PTs activation towards CE outcomes is non-existent. CHS as situational cues will attract social science researchers towards studying its role in various predictor outcome links across multiple work settings.

Aged-care work has become an extreme form of work. Anti-violence HRM, comprising practices to combat workplace violence, is important in an industry with widespread violence. In this paper, we employ social exchange theory to better understand the effect of anti-violence HRM and trust in the manager on perceived nurse and PCA cynicism working in Australian aged care facilities and their subsequent intention to leave.

This study used a mixed method with two stages. Stage 1 comprised semi-structured interviews with 10 managers and 50 nurses and PCAs working in Australian aged care facilities. Stage 2 comprised a survey of nurses and PCAs with a total of 254 completed responses in Time 1 (first wave) and 225 completed responses in Time 2 (second wave).

We tested three hypotheses and reported that interestingly anti-violence HRM was positively associated with organisational cynicism. Organisational cynicism mediated the relationship between anti-violence HRM and intention to leave. Worker trust in the manager moderated the relationship between anti-violence HRM practices and organisational cynicism, such that high levels of trust in the manager increased the effect of anti-violence HRM practices to reduce organisational cynicism and subsequently reduce intention to leave.

We find evidence that in aged care, workers' trust in their managers is critical for effectual anti-violence HRM. We argue that implementation of HRM practices may be more complex in extreme work settings. It is crucial to study HRM in situ and understand the root of social exchange(s) as a foundation for HRM to influence employee attitudes and behaviour.