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This research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities.

The authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance.

The major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs.

This paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities.

The findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings.

This research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.

Managing mega infrastructure projects (MIPs) is more complex because of time, size, social, environmental and financial implications. This study aims to address the management approaches, complexity and risk factors involved in MIPs. The study focuses on project success criteria and their individual effects on the success of MIPs.

To address the challenges and identify the most influencing factor for the success of MIPs, the study deployed a cross-sectional survey approach. Six hundred eighty-two usable samples were collected from the respondents to understand the impact of predetermined factors on the success of MIPs. The structural equation model and artificial neural network approach were used to derive the importance of factors affecting the success of MIPs.

The study's outcome confirms that all three influencing factors: feasibility studies, community engagements and contract selection, have a significant positive impact on the success of MIPs. Community engagement amongst all three has the most influential predictor for the success of MIPs.

The developed model will enable practitioners and policymakers from Indian construction companies and other emerging nations to concentrate on recognized risk reduction variables to enhance project success criteria and project management success, especially for MIPs.

Purchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially for e-retailers. To date, little is known about how e-retailers can scientifically detect users' intents within a purchase conversion funnel during their ongoing sessions and strategically optimize real-time marketing tactics corresponding to dynamic intent states. This study mainly aims to detect a real-time state of the conversion funnel based on graph theory, which refers to a five-class classification problem in the overt real-time choice decisions (RTCDs)—click, tag-to-wishlist, add-to-cart, remove-from-cart and purchase—during an ongoing session.

The authors propose a novel graph-theoretic framework to detect different states of the conversion funnel by identifying a user's unobserved mindset revealed from their navigation process graph, namely clickstream graph. First, the raw clickstream data are identified into individual sessions based on a 30-min time-out heuristic approach. Then, the authors convert each session into a sequence of temporal item-level clickstream graphs and conduct a temporal graph feature engineering according to the basic, single-, dyadic- and triadic-node and global characteristics. Furthermore, the synthetic minority oversampling technique is adopted to address with the problem of classifying imbalanced data. Finally, the authors train and test the proposed approach with several popular artificial intelligence algorithms.

The graph-theoretic approach validates that users' latent intent states within the conversion funnel can be interpreted as time-varying natures of their online graph footprints. In particular, the experimental results indicate that the graph-theoretic feature-oriented models achieve a substantial improvement of over 27% in line with the macro-average and micro-average area under the precision-recall curve, as compared to the conventional ones. In addition, the top five informative graph features for RTCDs are found to be Transitivity, Edge, Node, Degree and Reciprocity. In view of interpretability, the basic, single-, dyadic- and triadic-node and global characteristics of clickstream graphs have their specific advantages.

The findings suggest that the temporal graph-theoretic approach can form an efficient and powerful AI-based real-time intent detecting decision-support system. Different levels of graph features have their specific interpretability on RTCDs from the perspectives of consumer behavior and psychology, which provides a theoretical basis for the design of computer information systems and the optimization of the ongoing session intervention or recommendation in e-commerce.

To the best of the authors' knowledge, this is the first study to apply clickstream graphs and real-time decision choices in conversion prediction and detection. Most studies have only meditated on a binary classification problem, while this study applies a graph-theoretic approach in a five-class classification problem. In addition, this study constructs temporal item-level graphs to represent the original structure of clickstream session data based on graph theory. The time-varying characteristics of the proposed approach enhance the performance of purchase conversion detection during an ongoing session.

The advent of technology has propelled audit firms to incorporate AI-based audit services, bringing the relationship between audit clients and firms into sharper focus. Nonetheless, the understanding of how AI-based audit services affect this relationship remains sparse. This study strives to probe how an audit client's satisfaction with AI-based audit services influences their trust in audit firms. Identifying the variables affecting this trust, the research aspires to gain a deeper comprehension of the implications of AI-based audit services on the auditor-client relationship, ultimately aiming to boost client satisfaction and cultivate trust.

A conceptual framework has been devised, grounded in the client-company relationship model, to delineate the relationship between perceived quality, perceived value, attitude and satisfaction with AI-based audit services and their subsequent impact on trust in audit firms. The research entailed an empirical investigation employing Facebook ads, gathering 288 valid responses for evaluation. The structural equation method, utilized in conjunction with SPSS and Amos statistical applications, verified the reliability and overarching structure of the scales employed to measure these elements. A hybrid multi-analytical technique of structural equation modeling and artificial neural networks (SEM-ANN) was deployed to empirically validate the collated data.

The research unveiled a significant and positive relationship between perceived value and client satisfaction, trust and attitude towards AI-based audit services, along with the link between perceived quality and client satisfaction. The findings suggest that a favorable attitude and perceived quality of AI-based audit services could enhance satisfaction, subsequently augmenting perceived value and client trust. By focusing on the delivery of superior-quality services that fulfill clients' value expectations, firms may amplify client satisfaction and trust.

Further inquiries are required to appraise the influence of advanced technology adoption within audit firms on client trust-building mechanisms. Moreover, an understanding of why the impact of perceived quality on perceived value proves ineffectual in the context of audit client trust-building warrants further exploration. In interpreting the findings of this study, one should consider the inherent limitations of the empirical analysis, inclusive of the utilization of Facebook ads as a data-gathering tool.

The research yielded insightful theoretical and practical implications that can bolster audit clients' trust in audit firms amid technological advancements within the audit landscape. The results imply that audit firms should contemplate implementing trust-building mechanisms by creating value and influencing clients' stance towards AI-based audit services to establish trust, particularly when vying with competing firms. As technological evolutions impinge on trustworthiness, audit firms must prioritize clients' perceived value and satisfaction.

To the researcher's best knowledge, no previous study has scrutinized the impact of satisfaction with AI-based audit services on cultivating audit client trust in audit firms, in contrast to past research that has focused on the auditors' trust in the audit client. To bridge these gaps, this study employs a comprehensive and integrative theoretical model.

Dirty workers occupy jobs and perform tasks that are unpleasant and considered distasteful or “tainted” to other members of society. However, while they experience challenges in managing stigma, they are generally successful in creating positive self-identities. Among these dirty jobs is prostitution. As dirty workers, women sex workers in American history have been treated with humor, ridicule and derision. This study aims to explain the social contexts and the limited economic choices these women faced and examine how they may have managed their dirty work’s stigma to create positive self-identities.

This paper uses primary and secondary sources to examine a 53-year period of American history and to frame these women’s stigma management within a “dirty work” perspective.

The author suggests that sex workers in riskier roles (e.g. street walkers, crib workers or “upstairs girls” in saloons) would have been less able to effectively manage stigma and create positive self-identities as compared to brothels workers, due to the brothel’s strong social support, healthier work culture and richer resources.

While sex work has changed significantly in the past century, the principles of identity management in this difficult and dirty work remain. Understanding the economic, social and individual challenges faced by these dirty workers will aid our understanding of the difficulties confronted by today’s sex workers.

Sex work is nearly absent from scholarly management literature. The lack of historical perspective and knowledge in this field limits a full understanding of how various types of dirty workers manage stigma.

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

The main aim of this study is to examine the effect of virtual store atmospheric factors on impulsive purchasing in the metaverse context.

Grounded in purposive sampling, 451 individuals with previous metaverse experience were recruited to accomplish the objectives of this research. Next, to identify both linear and nonlinear relationships, the data were analyzed using partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) approaches.

The findings underscore the significance of the virtual store environment and online trust in shaping impulsive buying behaviors within the metaverse retailing setting. Theoretically, this study elucidates the impact of virtual store atmosphere and trust on impulsive buying within a metaverse retail setting.

From the findings of the study, because of the importance of virtual shop content, practitioners must address its role in impulse purchases via affective online trust. The study’s findings are likely to help retailers strategize and improve their virtual store presentations in the metaverse.

The discovery adds to the understanding of consumer behavior in the metaverse by probing the roles of virtual store atmosphere, online trust and impulsive buying.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

This study investigates economic sustainability through orientation and absorptive capacity.

The researchers developed a conceptual framework based on vigorous literature for this investigation. This study targeted managers from Pakistan's SME sector as respondents and employed cross-sectional data. In total, the authors based this study's findings on 192 valid cases.

The structural equation modeling (SEM) results highlight that innovation orientation (IO), customer orientation (CO), supplier orientation (SO), network orientation (NO) and absorptive capacity (AC) have significant effects on economic sustainability (ES). Moreover, this study's findings show that ES significantly predicts environmental sustainability (ENS). Finally, the results also demonstrate that ES and ENS positively and substantially affect financial performance (FP).

This study's findings help SMEs continue sustainable business practices by avoiding adverse environmental effects and ongoing climate changes. This study's findings contribute also to the manufacture of eco-friendly environmental products to reduce the contamination of the environment. Financial institutions and policymakers would boost SME owners' capacity and the obtainability of financial resources to improve Pakistani SMEs’ sustainable economic and environmental performance.

This study's findings help to enrich environmental and economic sustainability and, more significantly, for developing countries.

This chapter focuses on the middle level of organizations. It is a critical synthesis level for culture. The authors explain how this level is influenced by the higher and lower levels of the organizational culture. The authors discuss how factors from the higher and lower levels may affect this level. It is at this level that culture has the greatest effect on an organization’s business capabilities, and therefore its operations and performance.