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This research sought to examine the differential effect of chatbot banking artificial intelligence (AI) on consumer experience in the banking industry. A positivist paradigm was adopted to sample 389 consumers who were previously exposed to chatbot banking in Zimbabwe. A causal research design was employed whilst a quantitative approach was followed. In analysing data, the research study applied the structural equation modelling (SEM) technique. The authors found that chatbot banking significantly improves customer experience (CX) in the banking industry. Reliability and responsiveness of the chatbot need to be enhanced for effective improvements in CX. A need was also identified to enhance CX through the development of an ease-to-use chatbot which is embedded in everyday messaging applications of consumers. A significant association was also found between perceived benefits of chatbot banking and CX. This study informs the development of competitive advantage by banks and other related companies through AI-based CX management strategies. In times of pandemics and beyond, chatbot banking can be very instrumental in improving CX.

This study aims at understanding how higher education institutions (HEIs) can contribute to sustainable development, by designing their programmes for bringing about a transformative impact on communities and students, and also to examine what alternative pedagogical approaches could be used for that. In the past decades, HEIs have increasingly created social innovation (SI) programmes, as a way to achieve United Nations Sustainable Development Goals. These community-oriented and field-based programmes are difficult to ally with conventional classroom education. This study explores how these programmes could integrate the participatory approach and what would be the benefits. It also investigates the effectiveness of the experiential learning approach for teaching sustainability.

A case study method is used to document SI projects initiated by an HEI programme in rural India.

It was found that the participatory approach contributes to empowering communities and also benefits the students in terms of academic, professional and personal growth. Empirical findings show that experiential learning is an efficient method to teach sustainability. Ultimately, both pedagogical approaches are found to be mutually beneficial.

This study fills a gap in the literature, by providing empirical evidence on how HEI can implement innovative educational strategies such as participatory approach and experiential learning in their programmes towards teaching sustainability. A conceptual model for HEI interested in developing similar programmes is also proposed. To the best of the authors’ knowledge, this study is one of the first studies focusing on the context of Indian HEI.

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.

This study aims to investigate the determinants of United Arab Emirates (UAE) and Qatar consumers’ perceptions of organic food.

Using a survey approach and focusing on six known determinants identified from empirical studies, data were analysed using a mean difference analysis and a multi-regression model.

Citizens in both countries had similar access to organic food products, yet consumer perceptions differed significantly. Given the geographical, cultural and topographical proximity and similarities of these countries, differences in the mean values indicate that targeted or bespoke marketing is required to attract customers.

One limitation of this study its sample size that may limit the generalizability of its results. However, the highly statistically significant results offset this to some extent as the differences the authors have demonstrated are likely to be real. These do require confirmation by further similar studies. The study relied on a random sample of respondents who might have exhibited bias in answering questions.

The two countries central to this research study, the UAE and Qatar, have vested interests in supporting the organic food product market that mainly concern supporting and promoting the health of their citizens and the environment. As shown in this paper, the key obstacles preventing consumers’ accessing organic products are availability and price. With appropriate governmental subsidies and logistics support, however, these barriers can be overcome.

If governments are sincere in their declarations that the health and welfare of their citizens is of prime importance, they will support attempts to promote the sale and consumption of organically grown food. The results of this study can help to inform such policies.

This study provides insights into the most important factors that influence consumers’ attitudes towards organic foods. This study fills a gap in the existing research on consumer buying behaviour for organic food in two Middle Eastern countries.

Employees facing challenges in their careers – i.e. female, migrant, elderly and lower-educated employees – might expect job searches to have a low likelihood of success and might therefore more often stay in unsatisfactory positions. The goal of this study is to discover inequalities in job mobility for these employees.

We rely on a large sample of Dutch public sector employees (N = 30,709) and study whether employees with challenges in their careers are hampered in translating job dissatisfaction into job searches. Additionally, we assess whether this is due to their perceptions of labor market alternatives.

Findings show that non-Western migrant, elderly and lower-educated employees are less likely to act on job dissatisfaction than their advantaged counterparts, whereas women are more likely than men to do so. Additionally, we find that although they perceive labor market opportunities as limited, this does not affect their propensity to search for different jobs.

This paper is novel in discovering inequalities in job mobility by analyzing whether employees facing challenges in their careers are less likely to act on job dissatisfaction and therefore more likely to remain in unsatisfactory positions.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

This study’s main objective is to explore the ISO 9001 implementation model and identify a future research agenda. This is important because not all organizations find it easy to implement ISO 9001, and not all organizations get positive benefits after implementing it.

The paper presents a comprehensive review of the literature on ISO 9001 implementation models using the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on ISO 9001 implementation models. Relevant studies published from 2003 to early 2023 are explored to reveal the research landscape, gaps and trends.

Many ISO 9001 implementation methods have been developed for actual implementation in organizations, including models, frameworks, special variable considerations, application uses and integration. These methods were developed and applied to cover gaps regarding constraints, unbeneficial, special conditions, implementation objectives and organization types in ISO 9001 implementation. Current issues and future research on ISO 9001 implementation models were found, namely ISO 9001 implementation models specific to SMEs, ISO 9001 implementation levels, ISO 9001 implementation models that are agile to change, and affordable certification models.

Only a few researchers have systematically reviewed the literature or taken a bibliometric approach in their analyses to provide an overview of the current trends and links to ISO 9001 implementation models. The ISO 9001 standard is a general standard and can be applied by all organizations with the implementation method left to the implementer. Many implementation methods have been developed, but several implementation obstacles and disadvantages are still found. It is important to know the extent of current research and discover future research gaps regarding methods of implementing the ISO 9001 standard.

This study aims to appraise and synthesize evidence examining the effects of toxic leadership on the nursing workforce and patient safety outcomes.

This is a systematic review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Five electronic databases (SCOPUS, PubMed, Web of Science, CINAHL and Psych INFO) were searched to identify relevant articles. Two independent researchers conducted the data extraction and appraisal. A content analysis was used to identify toxic leadership outcomes.

The initial literature search identified 376 articles, 16 of which were deemed relevant to the final review. Results of the content analysis identified 31 outcomes, which were clustered into five themes: satisfaction with work; relationship with organization; psychological state and well-being; productivity and performance; and patient safety outcomes. Seven mediators between toxic leadership and five outcomes were identified in the included studies.

Organizational strategies to improve outcomes in the nursing workforce should involve measures to build and develop positive leadership and prevent toxic behaviors among nurse managers through theory-driven strategies, human resource management efforts and relevant policy.

The review findings have provided modest evidence suggesting that working under a leader who exhibits toxic behaviors may have adverse consequences in the nursing workforce; however, more research examining if this leadership style influences patient safety and care outcomes is warranted.

Artificial intelligence is one of the most significant and active fields of study in the last few years. Artificial intelligence-derived robotic technologies known as chatbots are gaining interest from both academic and industry sectors. By analyzing the development and patterns of research on the chatbot phenomena within the tourism field, this study seeks to develop a theoretical framework for the interaction between chatbots and tourism.

The Web of Science (WoS) database’s 33 articles on chatbots related to travel and hospitality were examined between 2019 and 2024 using VOSviewer software for bibliometric and thematic content analysis.

Research on chatbots for tourism and hospitality appears to be in its early stages. The factors influencing tourists' intentions to use chatbots have been thoroughly researched; the attitudes, perceptions and behavioral intentions of destinations, travel agencies and restaurant patrons regarding chatbots were examined, and it was found that the quantitative research approach was dominant. In addition, the majority of the studies are based on a particular theory or model.

This is one of the first attempts to directly comprehend and depict the interconnected structures of studies on the interaction between chatbots and tourism through the use of network analysis. Furthermore, the study’s findings can offer academics a comprehensive viewpoint and a reference manual for more accurate assessment and oversight of the chatbot-tourism interaction. Regarding the lack of research on the topic and the fragmented structure of the studies that exist, it is imperative to provide both a comprehensive overview and a roadmap for future investigations into the usage of chatbots in the travel and hospitality sector.