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Marketing is sometimes viewed as manipulative and as enticing consumers to live beyond their means. Artificial intelligence (AI)-powered systems can change the image of the marketing discipline and improve the marketing decision-making process. This chapter argues that embedding AI in the marketing process can help to alleviate public and consumer concerns about the marketing discipline. AI has the potential to make the marketing process transparent, but this is dependent on trust and privacy variables. Openness about using AI in the customer experience and how it is applied will put marketing on an objective framework. However, marketing decisions will be a mix of data and information mediated by intuition, reasoning, experience and empathy and these are qualities that are associated with marketers. AI customer experience requires decisions that are objective (personalisation) and those that are empathy related.

This study investigates the impact of behavioural determinants on university-level tourism students in developing economies, notably India, using the Theory of Planned Behaviour (TPB). Findings underscore TPB's efficacy in predicting entrepreneurial ambitions, with attitude, subjective norms and perceived behavioural control (PBC) serving as pivotal precursors shaping students' intentions. Particularly, those aspiring to environmentally sustainable practices exhibit heightened entrepreneurial intent. The implications extend beyond academia, aiding prospective entrepreneurs in informed decision-making and policymakers in fostering green entrepreneurship through tailored initiatives. This study also contributes to academic discourse, laying a foundation for future research in entrepreneurship studies. In sum, it underscores the critical role of behavioural determinants in shaping entrepreneurial intent among university tourism students, especially in developing economies like India. Insights gleaned benefit entrepreneurs, policymakers and scholars alike, driving global sustainable economic growth through emphasis on attitude, subjective norms and PBC.

The study aims to propose a predictive model that combines personality and demographic factors to predict student academic performance (SAP). This research study works on understanding, enhancing and applying techniques to enhance the prediction of SAP.

The authors gathered information from 305 university students from Al-Zintan University Libya. The study uses a survey questionnaire to collect data on essential variables. The purpose of the questionnaire is to discover variables that affect students' academic performance. The survey questionnaire has 44 closed questions with Likert scale designs that were distributed to a variety of college students at the start of the first semester of 2022. It includes questions about demographics, personality, employment and institutional aspects. The authors proposed a predictive model to identify the main fundamental components, consisting of one dependent variable (SAP) and five independent constructs. The suggested model is tested using partial least squares (PLS) and structural equation modeling (SEM), which perform better than covariance-based structural equation modeling (CB-SEM). PLS-SEM performs well with smaller sample sizes, even for complicated models.

The study results show that the proposed model accurately predicted the student's academic performance. The personality trait variables are a key factor that determines the actual student's academic performance. The student's academic performance is significantly impacted by each variable in the personality trait variables as well.

The process of validating research was done empirically through the accuracy and efficiency of model performance. The study differs from previous studies in that it accumulated a wide range of factors from different dimensions, including student demographics and personality trait factors. The authors developed a structural equation model to predict students' academic performance.

The main goal of this study is to employ unsupervised (lexicon-based) learning approaches to identify readers' emotional dimensions and thumbs-up empathy reactions to reviews of online travel agency apps based on appraisal and stimulus–organism–response (SOR) theories.

Using the Google Play Scraper, we gathered a total of 402,431 reviews from the Google Play Store for two travel agency apps, Tripadvisor and Booking.com. Following the filtering and cleaning of user reviews, we used lexicon-based unsupervised machine learning algorithms to investigate the associations between various emotional dimensions of reviews and review readers' thumbs-up reactions.

The study's findings reveal that the sentiment of different sorts of reviews has a substantial influence on review readers' emotional experiences, causing them to give the app a thumbs up review. Furthermore, readers' thumbs-up responses to the text reviews differed depending on the eight emotional aspects of the reviews.

The results of this research can be applied in the development of online travel agency apps. The findings suggest that app developers can enhance users' emotional experiences by considering the sentiment and emotional aspects of reviews in their design and implementation. Additionally, the results can be used by travel agencies to improve their online reputation and attract more customers by providing a positive user experience.

The findings of this research have the potential to have a significant impact on society by providing insights into the emotional experiences of users when they engage with online travel agency apps. The study highlights the importance of considering the emotional aspect of user reviews, which can help app developers to create more user-friendly and empathetic products.

The current study is the first to evaluate the impact of users' thumbs-up empathetic reactions on user evaluations of online travel agency applications using unsupervised (lexicon-based) learning methodologies.

An efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin system using Internet of things (IoT) sensors is generated. The sensors detect the level of waste in the dustbin. The data collected by the IoT sensor is stored in the blockchain. Here, an adaptive deep Markov random field (ADMRF) method is implemented to determine the weight of the wastes. The performance of the ADMRF is boosted by optimizing its parameters with the help of the improved corona virus herd immunity optimization algorithm (ICVHIOA). Here, the main objective of the developed ADMRF-based waste weight prediction is to minimize the root mean square error (RMSE) and mean absolute error (MAE) rate at the time of testing. If the weight of the bins is more than 80%, then an alert message will be sent to the waste collector directly. Optimal route selection is carried out using the developed ICVHIOA for efficient collection of wastes from the smart bin. Here, the main objectives of the optimal route selection are to reduce the distance and time to minimize the operational cost and the environmental impacts. The collected waste is then considered for recycling. The performance of the implemented IoT and blockchain-based smart dustbin is evaluated by comparing it with other existing smart dustbins for e-waste management.

The developed e-waste management system is used to collect the waste and to avoid certain diseases caused by the dumped waste. Disposal and recycling of the e-waste is necessary to decrease pollution and to manufacture new products from the waste.

The RMSE of the implemented framework was 33.65% better than convolutional neural network (CNN), 27.12% increased than recurrent neural network (RNN), 22.27% advanced than Resnet and 9.99% superior to long short-term memory (LSTM).

The proposed E-waste management system has given an enhanced performance rate in weight prediction and also in optimal route selection when compared with other conventional methods.

This study aims to examine the research productivity of Pakistan Agricultural Research Council’s (PARC’s) researchers published during 2001–2020 by using scientometric indicators. The study explored the growth and collaborative trends along with authorship and institutional collaborative patterns at the national and international levels.

The study was conducted in four phases. Firstly, a search strategy was designed to retrieve reliable data sets. During the second phase, data from PARC research was retrieved from Scopus and Web of Science (WoS). In the third phase, the data were combined, and duplications were removed. Finally, the data were analysed using RStudio and VOSviewer.

The study identified 2,868 research publications from 16 communication channels spanning over the period of 2001–2020. The growth rate varied during the study period and the year 2020 was the most productive year of the organization. Most of the research was produced in multi-authorship and five authors were dominant. Pakistan Journal of Botany was the most preferred and cited source. Moreover, PARC research collaboration with Pakistani researchers was more than their international counterparts.

Like other research, this research has some limitations. For example, this research is based on secondary data extracted from WoS and Scopus databases, world-renowned online academic. However, researchers should keep in mind while interpreting the results of this study. Secondly, the research publications published by PARC researchers during 2001–2020 were considered. Finally, this research considered English language literature only.

The study’s key theoretical contribution is its strategy for merging WoS and Scopus in RStudio, while its findings could assist agriculture research stakeholders in identifying new areas of research, awards, promotions and identification of research gaps.

To the best of the author’s knowledge, this study is the first to use scientometric indicators to evaluate PARC’s research productivity. This detailed analysis provides a deeper understanding of PARC’s contribution to agriculture research and its potential implications.

This study aims to investigate the effect of organizational ambidexterity (OA) and organizational green culture (OGC) on corporate sustainability (CS) while incorporating the mediating role of green innovation (GI) to provide a detailed insight into CS. The study also presents a research framework based on the Organizational Ambidexterity theory and Natural Resource-based view to explain the factors contributing to CS.

Using stratified sampling, the study collected data through survey-based empirical research from 307 textile companies registered with the Securities and Exchange Commission of Pakistan (SECP) or the All-Pakistan Textile Mills Association (APTMA). The collected data were analysed using path analysis, mediation analysis and moderation analysis through smart PLS-SEM version 4.0 to assess the composition and causal association of factors.

The study found a significant relationship between OA and OGC with CS. Furthermore, the study revealed that green innovation partially mediates the relationship between OGC and CS. The proposed research framework can be valuable for promoting and recommending actions to enhance CS.

The study on CS in the textile sector of Pakistan has limitations such as a narrow focus, cross-sectional design and reliance on self-reported data. Future research should explore additional factors, conduct longitudinal research, investigate contextual factors, scrutinize specific green innovation practices and broaden the scope of the study to include SMEs and other textile organizations.

The research framework can help senior executives to foster CS by promoting OGC, OA and GI. Practitioners and academicians can also utilize or further investigate the proposed framework for validation and to foster CS.

This study fills gaps in the existing literature by investigating the mediating effect of GI between OGC and CS. The proposed research framework provides a comprehensive understanding of the factors contributing to CS based on the Organizational Ambidexterity theory and Natural Resource-based view.

This study acclaims the social entrepreneurship based on cow rearing experiential tourism in Himachal Pradesh. This study aims to illustrate the role of indigenous cows in the Indian society, especially in the Northern Mountain regions by taking Kangra district of Himachal Pradesh as an exemplar. This study highlights the relevance of experiential tourism that elucidates on the basis of cow tourism pertaining to health, mental and spiritual rejuvenation. Lastly, the paper is an attempt to integrate social entrepreneurship and cow tourism highlighting the relevance of experiential economy in empowering the local community.

The case study elucidates on the whence of Swadeshi Kamdhenu Gaushala (SKG), an initiative of Mr Rishi Dogra and Mr Rajesh Dogra, their immaculate micro-management and its benefits to the local community. It highlights how SKG is uplifting the socio-economic standards of the local villagers and providing a distinctive learning experience of indigenous knowledge to visitors. This study is qualitative in nature that uses narrative analysis of secondary data to recognise the importance of indigenous Indian cows, and case study analysis of interviews of SKG proprietors to understand the micro-management, production of organic products and community engagement in their social entrepreneurship.

The SKG is not only helping the local community in their livelihood but also creating value and positioning to the place on the tourist map. This study sheds some light on the importance of cow products in sectors such as agriculture, green energy and for human health and nutrition. The study also crystallizes the challenges faced by the cow rearers, at last the paper sorted out the benefits of cow tourism and how it can result in community empowerment and development.

The case study on SKG helps us in understanding the importance of social entrepreneurs in community empowerment and also the intervention of tourism in the sector that can bring new and different vertical to the tourism industry with experiential learning of the tourist, which results in knowledge sharing about the benefits of Indian cows and helps in creating and placing such destinations on tourist maps. This study attempts towards contributing to the existing knowledge, highlighting the benefits of social entrepreneurship and cow tourism for the society in general and local community in particular.

The purpose of this study is to examine the trends and developments of subscription business models (SBMs) over the past two decades.

The study extracted 469 documents (articles and reviews) from the Scopus database during 2000–2022 and analysed 132 documents (articles and reviews). A bibliometric methodology of scientific mapping was employed, including a cluster analysis based on the bibliographic coupling of documents. Content analysis was also conducted to reveal emerging trends in SBMs.

The study revealed six emerging themes in SBMs related to consumer behaviour, digital advertising, online news media, journal publications, circular economy and sustainability strategies.

The results of this study provide new and unique insights into the development and trends of SBMs over the past two decades and offer guidance for future researchers to investigate further the phenomenon of SBMs in emerging areas.

The purpose of this study is to provide a comprehensive analysis of scientific knowledge in educational research over the past decade. The analysis aims to identify contributions to the field of education and trends in the literature.

Bibliometric analysis was conducted on 117,870 publications from 335 education journals in the Scopus database between 2013 and 2022.

This study shows educational research has increased consistently over the past decade. The USA showed high productivity, while the Netherlands produced the most impactful publications. The USA, UK and Australia have the highest research collaboration. Country collaboration network is divided into two blocks, comprising Western and Eastern countries, with the USA and the UK acting as bridges between these country groups. The bibliographic coupling analysis revealed that educational research is categorized into 11 clusters. Recent educational research aims to address the challenges in education, adapt to the changing technological, economic and social landscape and capitalize on emerging opportunities.

This study analysed over 100 thousand publications to identify the latest trends in educational research and highlight current developments in the field.