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The purpose of this study is to explore the complex interplay between technology, personal norms and emotional factors in shaping the sustainable housing choices of millennials in emerging economies. It integrates the model of goal-directed behavior, technology acceptance model and norm activation model, incorporating both self-interest and prosocial motivations. Key adaptations involve replacing perceived behavioral control with financial self-efficacy and substituting hedonic motivation for anticipated positive emotions. Moreover, it introduces location as a practical anchor.

A quantitative, cross-sectional and descriptive research design was used in this study. Data were gathered from a sample of 610 millennial residential real estate investors across Indian smart cities. A multistage stratified sampling technique was used to ensure a representative sample. For data analysis, partial least squares structural equation modeling was used. The analysis focused on hypothesis testing to examine the relationships between the constructs of interest. Bootstrap t-values and effect sizes were used to assess the significance and magnitude of these relationships, respectively.

One of the key findings of this study was the establishment of significant positive relationships between awareness of consequences, ascription of responsibility and personal norms with behavioral intentions. This underscored the importance of personal ethical considerations in shaping intentions. Perceived usefulness and ease of use were found to significantly influence attitudes positively, highlighting the relevance of these factors in forming favorable attitudes toward behaviors. Attitude, subjective norms, financial self-efficacy and location played significant positive roles. However, negative anticipated emotions decreased desire. This illustrated the complex role emotions play in motivational processes. The study also revealed that subjective norms did not significantly contribute to shaping personal norms. This indicated a potential decoupling of societal expectations from personal ethical obligations in the decision-making process.

This study offers actionable insights for both policymakers and real estate developers. For policymakers, the findings highlight the need to craft initiatives that go beyond mere awareness, instead fostering a deep sense of personal responsibility and environmental stewardship among potential homebuyers. For real estate developers, the emphasis on financial self-efficacy and location suggests a strategy shift toward designing sustainable homes that not only meet environmental standards but also align with buyers’ financial confidence and geographic preferences. Together, these strategies can drive a more widespread adoption of sustainable housing, making sustainability a tangible and appealing choice for millennials.

To the best of the authors’ knowledge, this empirical research study was one of the first studies that contributed to the literature by integrating the model of goal-directed behavior, technology acceptance model and norm activation model. This study thus offered a nuanced understanding of the interplay between normative influences, usability perceptions, ethical considerations and emotions in the context of behavioral intentions.

This study explores the factors influencing the investment intentions of potential home buyers among paraprofessionals in sustainable housing within urban construction. By combining the technology acceptance model (TAM) and the extended model of goal-directed behavior (EMGB), the research seeks to understand how technological perceptions, personal motivations and behavioral intentions intersect to shape these investment decisions.

A quantitative, cross-sectional and descriptive research design was employed in this study. The study surveyed 641 paraprofessionals as potential home buyers in various Indian cities using a multi-stage stratified sampling technique. It incorporated variables from the TAM and EMGB, modifying some aspects to include financial self-efficacy, hedonic motivation and anticipated regret, alongside adding location as a new factor to examine its impact. For data analysis, partial least squares structural equation modeling was utilized. The analysis focused on hypothesis testing to examine the relationships between the constructs of interest. Bootstrap t-values and effect sizes were used to assess these relationships’ significance and magnitude.

The study found that perceived usefulness and ease of use significantly enhance attitudes toward sustainable homes, while subjective norms have a minimal effect on such investments in India, emphasizing personal rather than societal influences. Financial self-efficacy, anticipated regret and hedonic motivation are key drivers, indicating that economic capacity and the pursuit of a satisfying lifestyle are crucial for investment intentions. Additionally, the importance of location is highlighted, with infrastructural aspects notably affecting sustainable housing appeal. These insights reveal unique dynamics in India’s sustainable housing sector, diverging from trends in developed countries.

The study lies in its unique fusion of the TAM and EMGB specifically tailored to the Indian urban construction context. It introduces financial self-efficacy, hedonic motivation and anticipated regret as novel variables within these frameworks, alongside emphasizing the significant role of location in sustainable housing decisions. This approach offers new insights into the psychological and socioeconomic factors driving sustainable housing investments in developing countries.

The purpose of this study is to understand the investment decision-making of real estate investors in housing, highlighting the interplay between rational and irrational factors. In this study, investment satisfaction was a mediator, while reinvestment intention was the dependent variable.

A quantitative, cross-sectional and descriptive research design was used, gathering data from a sample of 550 residential real estate investors using a multi-stage stratified sampling technique. The partial least squares structural equation modelling disjoint two-stage approach was used for data analysis. This methodological approach allowed for an in-depth examination of the relationship between rational factors such as location, profitability, financial viability, environmental considerations and legal aspects alongside irrational factors including various biases like overconfidence, availability, anchoring, representative and information cascade.

This study strongly supports the adaptive market hypothesis, showing that residential real estate investor behaviour is dynamic, combining rational and irrational elements influenced by evolutionary psychology. This challenges traditional views of investment decision-making. It also establishes that behavioural biases, key to adapting to market changes, are crucial in shaping residential property market efficiency. Essentially, the study uncovers an evolving real estate investment landscape driven by evolutionary behavioural patterns.

This research redefines rationality in behavioural finance by illustrating psychological biases as adaptive tools within the residential property market, urging a holistic integration of these insights into real estate investment theories.

The study reshapes property valuation models by blending economic and psychological perspectives, enhancing investor understanding and market efficiency. These interdisciplinary insights offer a blueprint for improved regulatory policies, investor education and targeted real estate marketing, fundamentally transforming the sector’s dynamics.

Unlike previous studies, the research uniquely integrates human cognitive behaviour theories from psychology and business studies, specifically in the context of residential property investment. This interdisciplinary approach offers a more nuanced understanding of investor behaviour.

As an alternative to hiring financial specialists or investment consultants, robo-advisors offer financially automated investment services. This study aims to investigate how robo-advisors' service attributes, risk attitude and financial self-efficacy influence customers' choice preferences of adopting robo-advisors.

Two hundred fifty-one online surveys were used to collect data, and choice-based conjoint analysis was conducted.

Results show that increasing annual fees negatively impact customers' choice preferences. Promotion, general investment education and additional human assistance have a positive impact. Furthermore, risk-seeking and risk-averse customers require more human assistance than risk-neutral customer and customers with high levels of financial self-efficacy prefer more general investment education and additional human assistance than those with lower levels. In addition, customers in the older age group prefer promotion, general investment education and additional human assistance, while wealthy customers prefer lower annual fees, higher general investment education and more additional human assistance compared to middle-class and low-income groups.

This study contributes to robo-advisor providers to provide appropriate service attributes for each customer group.

The purpose of this paper is to explore the effectiveness of the use of spiritual intelligence into women academic leadership practices. The study designed to provide a clear understanding of the effectiveness of the use of spiritual intelligence practices within women academic leadership practices. In addition, the study will be an ideal for women in academic environment, considering that more women will have the opportunities to hold leadership positions in higher learning institutions. Understanding the unique skills and attributes of spiritual intelligence could increase their confidence towards taking on leadership positions in future. This study will also provide greater clarification on how spiritual intelligence when translated through leadership practice will contribute to a more balanced and harmonious working environment.

The study assimilated a qualitative approach guided by phenomenological inquiry to explore the effectiveness of the use of spiritual intelligence practices among the women leaders. Phenomenology best fit the researchers’ assumptions that it is possible to know, define and categorize women academic leader’s experiences in a more structured manner. It is by entering into their field of perception that the researcher pursues to understand spiritual intelligence as the leaders saw it.

The finding established three main effectiveness of integrating spiritual intelligence into leadership practices: employees inspired by vision; increase credibility and long-term sustainability of institution; and heightened moral values and reduces ethical issues. The authors conclude that bringing the attributions of spiritual intelligence will transform the workplace into a more meaningful and purposeful atmosphere by constructing balanced and harmonious relationship within employees.

The study rely profoundly on women academic leaders as a primary source of data. Due to their higher position, there may be some elements of their works which are similar to each other or different compared with other women leaders who may not have reached the higher position. The responses was mainly based on the self-perception of women academic leaders grounded on their insight of leadership and experience. This experiences probably could not be verified by others to see if their perceptions of leadership were in line with how others perceive them. The study also limited in terms of generalizability as the sample was purposively selected.

The study will be applicable for human resource personnel to develop policies and procedures that are needed to improve the holistic strategies of leading not only in public university but also throughout all the other higher educational institutions. The study assist researcher and the practitioner in the Human Resource Development (HRD) field to understand the issues related to leadership practice in current era. Hence, the information in this study could be used to aid them in advocating employee training programs and formulating HRD intervention remedial programs input for designing and facilitating of intervention for professional academic leaders, academicians and change agent to understand and analyse the characteristics of personal and organizational situation in order to contribute to the long run survival of the higher educational learning institutions.

This paper has provided unique evidence of women leadership particularly in Malaysian higher educational context on their experiences of spiritual intelligence and its effectiveness into their leadership practices.

This paper aims to address the pressing problem of prediction concerning shipment times of therapeutics, diagnostics and vaccines during the ongoing COVID-19 pandemic using a novel artificial intelligence (AI) and machine learning (ML) approach.

The present study used organic real-world therapeutic supplies data of over 3 million shipments collected during the COVID-19 pandemic through a large real-world e-pharmacy. The researchers built various ML multiclass classification models, namely, random forest (RF), extra trees (XRT), decision tree (DT), multilayer perceptron (MLP), XGBoost (XGB), CatBoost (CB), linear stochastic gradient descent (SGD) and the linear Naïve Bayes (NB) and trained them on striped datasets of (source, destination, shipper) triplets. The study stacked the base models and built stacked meta-models. Subsequently, the researchers built a model zoo with a combination of the base models and stacked meta-models trained on these striped datasets. The study used 10-fold cross-validation (CV) for performance evaluation.

The findings reveal that the turn-around-time provided by therapeutic supply logistics providers is only 62.91% accurate when compared to reality. In contrast, the solution provided in this study is up to 93.5% accurate compared to reality, resulting in up to 48.62% improvement, with a clear trend of more historic data and better performance growing each week.

The implication of the study has shown the efficacy of ML model zoo with a combination of base models and stacked meta-models trained on striped datasets of (source, destination and shipper) triplets for predicting the shipment times of therapeutics, diagnostics and vaccines in the e-pharmacy supply chain.

The novelty of the study is on the real-world e-pharmacy supply chain under post-COVID-19 lockdown conditions and has come up with a novel ML ensemble stacking based model zoo to make predictions on the shipment times of therapeutics. Through this work, it is assumed that there will be greater adoption of AI and ML techniques in shipment time prediction of therapeutics in the logistics industry in the pandemic situations.

This paper aims to explore the factors influencing the purchasing patterns of eco-friendly cosmetics and beauty care products among female customers in Mauritius. It also investigates upon the relative significance of these factors in predicting the preference to buy and recommend eco-friendly cosmetics and beauty care products to others.

The paper applies the data reduction technique by using exploratory factor analysis on a sample of 150 female consumers and condenses a set of 35 attributes into a list of eight comprehensible factors influencing the purchasing patterns of eco-friendly and beauty care products among females in Mauritius. Multiple regression analysis was also conducted to investigate upon the importance of the eight dimensions in influencing the behavioural intentions of females to purchase eco-friendly products and their likeliness to engage in referral for eco-friendly products.

The factor analysis identified that the purchasing patterns for eco-friendly cosmetics and beauty care products is influenced by a combination of eight factors namely: “women lifestyles, self-image health and economic considerations”, “ethical consumerism among females”, “pharmacological essence of green cosmetics and beauty care products”, “visual appeal and physical cues in cosmetic stores”, “price-conscious decisions and effective promotion”, “belief on ethical claims in green messages”, “brand image and usage experience” and “sales representatives and social influences”. The results of the regression analysis have also suggested that the behavioural intention and referral of female customers is primarily derived from one predictor factor related to “women lifestyles, self-image, health and economic conditions”.

In terms of marketing strategies, cosmetic and beauty care organisations should promote greater ethical concerns among female customers through effective green advertising messages. Greater emphasis should be placed on the pharmacological essence of green advertising. Cosmetic executives should also focus on health-related benefits while marketing cosmetics and beauty care products.

The paper aims to fill up the significant gap in the literature on purchasing patterns for eco-friendly cosmetics and beauty care products among female customers. This study remains one of few research work designed to address different factors influencing the purchasing patterns for green cosmetics and beauty care products in the context of developing countries such as Mauritius. Yet, it would serve as a roadmap for cosmetics and beauty care companies to understand the factors impacting on purchasing patterns of eco-friendly cosmetics and beauty care products in similar contexts.

The purpose of this paper is to analyse the impact of green marketing strategies on consumer purchasing patterns in Mauritius. The present research also explores the possibility of introducing greener patterns of consumption into contemporary lifestyles in the current context where green products are increasingly available.

This paper reviews the theory of green marketing to identify how customers are persuaded to purchase greener products. It also reports the empirical results of a consumer survey on green marketing strategies by the questionnaire method. The survey questionnaires were administered to 150 respondents visiting various hypermarkets and supermarkets. The questionnaires were processed and analysed with the statistical programme SPSS, using descriptive, correlation and factor analysis.

The findings provide some interesting clues regarding customers’ perceptions on environmental concerns and green products. Further findings suggest that most consumers have expressed a great interest in the protection of the environment and they are strongly concerned about the environment and its degradation. Hence, business executives should host workshops and seminars so as to educate customers on environmental protection and environmental‐friendly products. Results also indicated an overall positive correlation between effective green marketing strategies and customers’ purchasing patterns for green products. Therefore, there is a powerful urge for companies to promote green branding, eco‐labelling and green packaging strategies in order to encourage a greener pattern of consumption among consumers in Mauritius.

The paper suggests a stepping approach to consumer purchasing patterns for greener products could be exploited more within consumer groups that have strong environmental concerns and beliefs in Mauritius. The results also offer precious knowledge on the effectiveness of green marketing strategies which can assist both private and public enterprises in developing and designing appealing green products that will be favoured by customers.

Although green marketing has been an important research topic for several decades, hardly any research has been focused on the impact of green marketing strategies on consumer purchasing patterns in the context of developing countries. This paper has analysed the strength of environmental concerns and beliefs and has provided some insights on green marketing strategies and the various managerial implications have been fully addressed in order to favour the consumption of greener products in Mauritius.

The purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.

The researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.

The proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.

The implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.

E-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.

The present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.

Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.

Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.

Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.

Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.

Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.