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The corporate atmosphere in recent times speaks volumes about the crises of confidence and credibility brewing among professionals due to the rising incidences of unethical pro-organizational behavior (UPB). The study developed a model to demonstrate the underlying mechanisms through which unethical organizational culture (UOC) influences UPB through the mediating roles of idealism and relativism.

Using a cross-sectional approach, data were collected through questionnaires that were distributed to small and medium-sized enterprises (SMEs) operating in the Plateau state in Nigeria. A total of 269 responses were obtained and analyzed using the partial least squares structural equation modeling (PLS-SEM) technique via Smart-PLS software.

The results revealed that the relationship between UOC and UPB was significant. The indirect predictive role of UOC on UPB was established via relativism but not through idealism. The results indicate that the preponderance of UPB among SMEs is a product of UOC which breeds a relativist ideology that ultimately promotes UPB. Finally, implications and suggestions for further research are discussed.

This study contributes to UPB in two unique ways. First, the authors bring to the fore the critical role of UOC in the debate on UPB which has been under-explored. Second, the study also established the mediating role of relativism in the relationship between UOC and UPB.

With so many placement opportunities around the students in their final or prefinal year, they start to feel the strain of the season. The students feel the need to be aware of their position and how to increase their chances of being hired. Hence, a system to guide their career is one of the needs of the day.

The job role prediction system utilizes machine learning techniques such as Naïve Bayes, K-Nearest Neighbor, Support Vector machines (SVM) and Artificial Neural Networks (ANN) to suggest a student’s job role based on their academic performance and course outcomes (CO), out of which ANN performs better. The system uses the Mepco Schlenk Engineering College curriculum, placement and students’ Assessment data sets, in which the CO and syllabus are used to determine the skills that the student has gained from their courses. The necessary skills for a job position are then extracted from the job advertisements. The system compares the student’s skills with the required skills for the job role based on the placement prediction result.

The system predicts placement possibilities with an accuracy of 93.33 and 98% precision. Also, the skill analysis for students gives the students information about their skill-set strengths and weaknesses.

For skill-set analysis, only the direct assessment of the students is considered. Indirect assessment shall also be considered for future scope.

The model is adaptable and flexible (customizable) to any type of academic institute or universities.

The research will be very much useful for the students community to bridge the gap between the academic and industrial needs.

Several works are done for career guidance for the students. However, these career guidance methodologies are designed only using the curriculum and students’ basic personal information. The proposed system will consider the students’ academic performance through direct assessment, along with their curriculum and basic personal information.

A focus on how visual cultures in domestic spaces influence older migrants' identification with the homeland has been noted in previous research, ¹ yet migrant youth have not typically been the sole subjects of investigation in this regard. This chapter seeks to fill this gap by offering insight into the practices of young Indian women in domestic spaces and how these practices influence their sense of belonging to India. This chapter highlights the practices of young Indian women living in Brisbane, Australia, through an exploration into how the young women recreate their histories and cultural attachments in domestic spaces. The research presented in this chapter illustrates the processes of emotional attachment for young migrants and how these processes demonstrate new ways of practising diaspora, including the use of the internet to learn about their cultural histories. With the use of PhotoVoice, where photographs were retrieved from mobile phones and the internet, the participants discussed their everyday lives relating to their emotional attachments to material objects in domestic spaces and the connection to their identities. This chapter's main argument is to highlight the need for researchers to avoid the tendency to place young migrants into the same diasporic categories as their parents and to recognise the diverse ways in which young migrants actively shape their own cultural attachments.

With the increasing concern over environmental pollution and global warming, companies are required to act responsibly to mitigate these environmental issues. Their activities should adhere to the standards of environmental sustainability. Thus, this study aimed to investigate the impact of green supply chain management (GSCM) and total quality management (TQM) on environmental sustainability, with environmental management practices (EMP) as the moderating factor.

A quantitative study was adopted using the management data from various manufacturing companies in Jordan. A total of 362 responses were collected, and the proposed hypotheses were tested using a structural equation model.

The study findings revealed that both GSCM and TQM significantly and positively influenced environmental sustainability. The impact of TQM on environmental sustainability was higher than that of GSCM. Moreover, no evidence was found on the moderating role of EMP.

The study’s results highlighted to the decision-makers the main practices to expand the quality implementation across their supply chain to improve environmental sustainability. The study also demonstrated the reasons behind the insignificance of EMPs in strengthening the relationships between GSCM, TQM, and environmental sustainability.

While there are very few studies examining the relationships between GSCM and TQM on environmental sustainability. This study adds to the literature body as one of a few empirical studies that tested the integrated effect of GSCM and TQM practices within the context of the manufacturing industry in a developing country. Moreover, this study takes a holistic approach by tapping into EMP to confirm whether it moderated the relationships between GSCM, TQM, and environmental sustainability.

Many educators are increasingly acquainting themselves and becoming adept with interactive technologies like augmented reality and virtual reality. Some of them are also looking forward to using Metaverse applications, as they want to benefit from its immersive three-dimensional capabilities. Therefore, the purpose of this study is to critically review the extant literature to investigate how, why, where and when the Metaverse can be used for educational purposes. This study also discusses opportunities, challenges and risks related to this disruptive technology.

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses rigorous protocol is used to search, extract, scrutinize and synthesize content from high-impact articles focused on the use of the Metaverse technology in the realms of education. Afterwards, this study theorizes on the costs and benefits of using this interactive technology with students.

A number of researchers are already experimenting with virtual technologies that are very similar to the Metaverse, in different contexts. This research indicates that most students are lured by immersive multi-sensory three-dimensional environments as well as by virtual reality applications that could simulate real-life situations and provide engaging experiences with virtual representations of people, places and objects. On the other hand, this study reveals that educators ought to consider the potential pitfalls of the Metaverse, including privacy breaches and security risks, as well as possible addictions and the development of mental health issues, among others.

Students and educators can use the Metaverse to catapult themselves in a simulated digital universe that could reconfigure their sensory inputs, definitions of space, time and points of access to information. This research calls for the development of regulatory instruments, including sound principles, guidelines and procedures that are intended to safeguard and protect Metaverse users.

This contribution implies that there is scope for educators to continue developing the Metaverse’s virtual spaces to improve their students’ motivations, aptitudes and learning outcomes. This study clarifies that the use of the Metaverse in education can create infinite possibilities to enhance their knowledge, competences and abilities through its immersive applications. Yet this paper also raises awareness about possible challenges in the short term as well on other risks associated to the prolonged use of this captivating technology.

The digital revolution has changed consumer–service provider interaction, spawning a new generation of FinTech. This paper analyzes consumers' reactions to artificial intelligence (AI) (vs human) decisions.

The authors tested their predictions by conducting two experimental studies with FinTech consumers (n = 503).

The results reveal that consumers' responses to AI (vs human) credit decisions depend on the type of credit product. For personal loans, the rejection by an AI provider triggers higher levels of satisfaction compared to a credit analyst. This effect is explained via the perceived role congruity. In addition, the findings reveal that consumers’ rejection sensitivity determines how they perceive financial services role congruity.

To the best of the authors' knowledge, this research is the first to jointly examine AI (vs human) credit decisions in FinTech and role congruity, extending prior research in the field.

The purpose of this paper is to systematically review the functionalities, safety regulations and product applications of herb Stevia rebaudiana extract. This plant material is embedded with multiple functionalities such as antioxidant, antidiabetics, anti-inflammation and antimicrobial. The regulations released from global authorities are covered to ensure the safety premise of stevia. Besides, the product applications of the extract of aerial parts of the herb S. rebaudiana helps us to recognize its value from commercial side.

Relevant literatures are selected and obtained from main scientific databases such as Google Scholar, Web of Science, PubMed and trade magazines published between 2000 and 2023. The keywords and their possible combinations such as sweetening, antioxidant, antidiabetics, anti-inflammation, safety and product development were used to ensure the preciseness and completeness of literature searching. Major data such as sweetness, total phenolic content and dose together with latter critical conclusions from searched publications were appropriately used and discussed. In this review, approximately 150 scientific literatures were meticulously ordered and analyzed. In applications, it is the first time that sentiment analysis was used to obtain a market assessment of the stevia-containing products.

This review paper helps rearrange the scientific affairs of those stevia extract’s functions like sweetening, antioxidant, antidiabetics and inflammation. Sweetness indexes of steviol glycosides were summarized together for comparison while various in vitro and in vivo approaches were reviewed to quantify those functions’ capacities and to depict the related mechanism. The regulation of steviol glycoside compounds such as rebaudioside A was established by global authorities such as US Food and Drug Administration and Joint FAO/World Health Organization Expert Committee to ensure the safety endorsement before commercialization. Then, this study discussed about the market performance of stevia ingredients or products with the self-developed data analytics. This study also investigated the product development progress of stevia-containing food products in the categories of beverage, bakery, dairy and confectionery. Those stevia-containing food consumer goods can be acceptable by certain consumers.

This review paper precisely presents the evidential information about the stevia’s multiple functionalities with mechanisms and global regulation milestones. To the best of the authors’ knowledge, it is then the first time to probe the stevia-containing products’ market performance through data analytics.

During the previous two decades, “Green Supply Chain Management” (GSCM) has been gaining the attention of researchers and practitioners from various fields (e.g. operations, logistics and supply chain management). Its significance is constantly growing, and various studies are conducted in order to capture its overall organizational contribution. The present study attempts to bring together various organizational aspects that have never been collectively investigated before in the relevant literature. Under that rationale, a robust conceptual framework is developed and empirically tested. This framework includes 17 factors that are classified in three dimensions: (1) drivers of GSCM practices, (2) GSCM practices and (3) firm performance (GSCM outcomes).

The examination of the proposed conceptual framework was performed using a newly developed structured questionnaire that was distributed to a sample of Greek manufacturing organizations. Supply Chain managers and Chief Executive Officers (CEOs) were used as key respondents, due to their knowledge and experience. After the completion of the three-month research period (last quarter of 2019), 292 useable questionnaires were returned. The empirical data were analyzed using the “Structural Equation Modeling” technique. The study is empirical (based on primary data), explanatory (examines cause and effect relationships), deductive (tests research hypotheses) and quantitative (includes the analysis of quantitative data collected with the use of a structured questionnaire).

Empirical results point out that internal environmental management, green innovative practices and environmental proactivity are GSCM practices with the most significant impact on firm performance. Moreover, the mediating role of GSCM practices in the relationship between GSCM drivers and firm performance is also highlighted. Finally, it was found that GSCM practices can explain 35% of the variance in firm performance and the drivers of GSCM practices can explain 78% of the variance of these practices.

The proposed three-dimensional conceptual framework of this empirical study and its underlining rationale has rarely been adopted in the relevant literature. Moreover, the study investigates which GSCM practices have an impact on firm performance, thus offering value to practitioners of the field. Also, it is one of the few similar studies that have been conducted on a European country.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

There is always a need to discover how a paradox between a customer’s desire for a more personalized experience and their privacy and security concerns would shape their intention to continue using contactless payment methods. However, personalization–privacy paradox has not been well-covered over the area of contactless payment. Therefore, this study aims to empirically examine the impact of personalization–privacy paradox on the customers’ continued intention (CIN) to use contactless payment.

/methodology/approach – The empirical part of the current study was conducted in Saudi Arabia by collecting the primary data using online questionnaire from a convenience sample size of 297 actual users of contactless payment methods.

Based on structural equation modeling, personalization and privacy invasion were approved to significantly impact perceived value of information disclosure (PVD). Strong causal associations were confirmed between perceived severity, structural assurance and response cost with privacy invasion. Finally, both PVD and privacy invasion significantly predict CIN.

There are other important factors (i.e. technology interactivity, technology readiness, social influence, trust, prior experience, etc.) were not tested in the current study. Therefore, future studies would pay more attention regarding the impact of these factors. The current study data were also collected using a convenience sample of actual users of contactless payment methods. Therefore, there is a concern regarding the generalizability of the current study results to other kind of customers who have not used contactless payment.

This study has integrated both personalization–privacy paradox and protection motivation theory in one model. The current study holds value in providing a new and complete picture of the inhibitors and enablers of customers’ CIN to use contactless payment, including new types of inhibitors. Furthermore, personalization–privacy paradox has not been fully examined over the related area of Fintech and contactless payment in general. Therefore, this study was able to extend the theoretical horizon personalization–privacy paradox to new area (i.e. contactless payment) and new cultural context (Saudi Arabia).