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In the competitive realm of destination branding, understanding the factors that lead tourists to develop a deep emotional connection with a destination’s brand is essential. At the heart of this emotional bond lies destination brand love – an exceptionally profound sentiment transcending mere satisfaction or preference. This concept has recently garnered attention from both tourism academia and industry practice. However, there remains a void in exploring the factors and mechanisms that pave the way for tourists to develop deep affection for a destination’s brand. This study examines the influences of self-congruence and destination immersion on destination brand love. In addition, the linkages from the four facets of self-congruence to destination brand love via the mediating role of destination brand immersion are also scrutinized.

With data collected from 421 tourists in Vietnam, the partial least squares structural equation modeling (PLS-SEM) approach was applied to test the research model and hypotheses.

The results reveal that (1) ideal and ideal social self-congruence impact destination brand love, (2) all four facets of self-congruence impact destination brand immersion and (3) there is an indirect effect of the four facets of self-congruence on destination brand love, mediated by destination brand immersion.

The study’s findings provide evidence that destination characteristics must be symbolic of the destination brand and consistent with its image among tourists, which is essential in understanding the complex behavior of tourists. Indeed, the results indicate that facets of self-congruence significantly influence both destination brand immersion and destination brand love. Therefore, destination marketers should develop marketing strategies that emphasize personal relevance to the tourist destination to increase their sympathy and love for the destination.

Adapting self-congruence theory and the psychology of flow theory, this study presents a model that elucidates both the direct and indirect relationships among the four facets of self-congruence: destination brand immersion and destination brand love based on the aspects of congruence between tourists and destinations. From this, it helps to explore the factors and mechanisms that pave the way for tourists to develop deep affection for a destination’s brand.

Information security (InfoSec) policy violations are of great concern to all organisations worldwide, especially in the financial industry. Although the importance of InfoSec policy has been highlighted for many decades, InfoSec breaches still occur due to a low level of employee compliance and a lack of engagement and competence in high-level management. However, previous studies have primarily investigated the behavioural aspects of InfoSec policy compliance at the individual level rather than the managerial factors involved in constructing InfoSec policy and developing its effectiveness. Thus, drawing on neo-institutional theory and a transformational leadership framework, this research investigated the influence of external mechanisms and transformational leadership on InfoSec policy effectiveness.

The research model was implemented using field survey data from professional managers in the financial sector.

The results reported that neo-institutional mechanisms and transformational leadership shape InfoSec policy effectiveness in an organisation.

This study broadens current InfoSec policy research from an individual level to a managerial perspective and enhances the existing literature on neo-institutional and transformational leadership in the context of InfoSec. It highlights the need to evaluate InfoSec policy based on external factors and to support transformational leadership styles that promote InfoSec policy enforcement and effectiveness.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

The aim of this study was to measure the level of challenges faced by novice teachers in South Africa. Findings suggest a need for professional development courses to upskill teachers with effective pedagogies that can incorporate the social and emotional components into teaching and learning.

This study applied a descriptive research methodology by administering a questionnaire to 143 novice teachers. The data analysis technique was the Rasch model.

The findings indicated high item and person reliability, with a good item fit and polarity values that are compatible with the Rasch model. The three major challenges identified are uninvolved parents, discipline problems and a lack of guidance and counselling. These challenges can be related to social and emotional learning (SEL) components.

The study used quantitative methods and discovered the challenges that novice teachers face. If the research uses a combination of qualitative methods, it will be possible to better identify the specific causes of the above three challenges of novice teachers.

Due to the complex nature of South African society, many novice teachers are overwhelmed by the challenges they face when entering the profession. These challenges are often embedded in societal risk factors, which complicate the transition from student teacher to novice teacher. The major challenges identified in this study can be related to SEL components, as the challenges are closely linked to the psychological and social backgrounds of the students. Teachers in this study indicated that they found it difficult to deal with these challenges at the beginning of their careers.

By identifying the challenges facing new teachers in South Africa, they will be better prepared for their work in schools. Therefore, they will improve the above situation to continue developing professionally.

The findings indicated high item and person reliability, with a good item fit and polarity values that are compatible with the Rasch model. Teachers in this study indicated that they found it difficult to deal with these challenges in the beginning of their careers. Professional development courses are suggested to help teachers deal with issues such as discipline, uninvolved parents and guidance and counselling effectively. Higher education programmes should also include these topics in their curricula for student teachers. A greater emphasis on training teachers in their pastoral roles is suggested.

The study seeks to conduct an empirical investigation on the impact of entrepreneurship education (EE) through its components, i.e. entrepreneurial knowledge (EK) and business planning (BP) on entrepreneurial intentions (EI) in India.

An electronic questionnaire was used to collect data from 340 engineering students and partial least square-structural equation modeling (PLS-SEM) was used to analyze the collected data.

The findings revealed that EK and BP have no direct impact on EI, however, they have an indirect influence through attitude towards entrepreneurship (ATE) and perceived behavioral control (PBC), whereas subjective norms (SN) have no mediation impact on the relationships.

This research has been conducted on students of engineering background only, future studies can be carried out by incorporating more attitudinal and environmental determinants with larger data sizes from diverse educational streams.

This study is of immense significance to policymakers and educational establishments in designing the purposefully designed EE courses that can drive the entrepreneurial intentionality of students.

The study adds to the paucity of research on the systematic elaboration of EE construct underlining the specific impact of EK and BP as EE dimensions on students' EI. To the best of authors' awareness, this kind of investigation has not been conducted in indian higher educational institution (HEI) context.

This study aims to investigate the factors affecting technological innovation intentions at banks listed in Vietnam, a developing country, to develop business activities and accounting benefits according to the trend of the 4th Industrial Revolution.

To collect and analyze the data for this study, qualitative and quantitative methods were used. Specifically, 20 finance and banking experts and 45 managers in the field of information technology were interviewed in qualitative research over a period of three months. Then, 1,000 questionnaires were sent to banks within six months, with the final sample for quantitative research being 324 respondents. Finally, the structural equation modeling (SEM) was used to check the hypotheses. Regarding the tools used, the qualitative study used a semistructured questionnaire to collect information. Meanwhile, SPSS software was used to analyze quantitative research information, including checking common method bias, nonresponse bias, evaluating scale quality and checking SEM.

The findings show that the usefulness, ease of application, credibility, innovation and efficiency of technology have certain impacts on technological innovation intentions at banks listed in Vietnam. Using the SEM analysis, the results showed that the five factors had a favorable influence on the technological innovation intentions. More specifically, this study proposed adding an efficiency factor, and the results showed that it has the greatest impact on technological innovation intentions.

This study would be considered a continuation of prior studies because it provides empirical evidence for business models at banks listed in developing countries (for example, Vietnam) and so provides useful advice for bank management not only in Vietnam but across Asia. In fact, bank managers should consider introducing new technology as appropriate to make their reports more clear and up-to-date, therefore improving their performance. Banking managers, in particular, should focus on enhancing the bank’s application technology indicators to obtain a competitive edge.

This is a pioneering study that uses a combination of the reasoned action theory, planned behavior theory, transaction cost theory and unified theory of acceptance and use of technology to expand knowledge about technological innovation intentions at listed banks in the context of a developing country. The study also discovered and added the efficiency factor as a key factor affecting the intention to innovate technology at listed banks. These contribute to improving the literature of technological innovation intentions.

The purpose of this paper is to systematically analyze the literature around regulatory compliance and market manipulation in capital markets through the use of bibliometrics and propose future research directions. Under the domain of capital markets, this theme is a niche area of research where greater academic investigations are required. Most of the research is fragmented and limited to a few conventional aspects only. To address this gap, this study engages in a large-scale systematic literature review approach to collect and analyze the research corpus in the post-2000 era.

The big data corpus comprising research articles has been extracted from the scientific Scopus database and analyzed using the VoSviewer application. The literature around the subject has been presented using bibliometrics to give useful insights on the most popular research work and articles, top contributing journals, authors, institutions and countries leading to identification of gaps and potential research areas.

Based on the review, this study concludes that, even in an era of global market integration and disruptive technological advancements, many important aspects of this subject remain significantly underexplored. Over the past two decades, research has lagged behind the evolution of capital market crime and market regulations. Finally, based on the findings, the study suggests important future research directions as well as a few research questions. This includes market manipulation, market regulations and new-age technologies, all of which could be very useful to researchers in this field and generate key inputs for stock market regulators.

The limitation of this research is that it is based on Scopus database so the possibility of omission of some literature cannot be completely ruled out. More advanced machine learning techniques could be applied to decode the finer aspects of the studies undertaken so far.

Increased integration among global markets, fast-paced technological disruptions and complexity of financial crimes in stock markets have put immense pressure on market regulators. As economies and equity markets evolve, good research investigations can aid in a better understanding of market manipulation and regulatory compliance. The proposed research directions will be very useful to researchers in this field as well as generate key inputs for stock market regulators to deal with market misbehavior.

This study has adopted a period-wise broad-based scientific approach to identify some of the most pertinent gaps in the subject and has proposed practical areas of study to strengthen the literature in the said field.