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The study compares how work-integrated learning (WIL) placement positioning, duration, assessment strategies and environment at three Southern African universities influence engineering students' academic and employability outcomes.

The study used a qualitative case study approach that drew on the principles of collaborative autoethnography (CAE). The researchers reflected on WIL placement practices, structure, assessment, environment and outcomes at their universities and then analysed the reflections using comparative descriptive techniques.

The study reports no uniformity among the universities in positioning WIL placement in the curriculum. It is done during end-of-year vacations, between the penultimate and final year or in the last year. The study found WIL placement positioning does not influence academic outcomes; however, the influence on employability outcomes needs further investigation. Components of WIL placement assessment are similar, presentations, logbooks and reports. However, there are differences in the weightings of the various assessment components and the contribution of the industry supervisor. There is a growing trend towards placing students within universities to mitigate the challenges of limited opportunities of placements available in the industry. The impact of this also needs to be further investigated. Lastly, there are policy-related challenges in placing international students. Work restrictions on student visas limit international students’ access to WIL placement. Southern African universities need to lobby the waivers to student visa restrictions that limit their participation in WIL programs if there are to succeed in their internationalisation efforts.

The study highlights the gaps in understanding Southern African universities' WIL placement practices, particularly relating to the positioning of WIL placement in the curriculum, the assessment methods used and the theory to work integration and employability outcomes.

Consumers have pervasively relied on mobile reviews in digital economy. However, little knowledge exists regarding how customers adopt several mobile reviews to make purchasing decisions. With the assistance of reader-response theory, this study investigates how the consistency of product reviews, in terms of their adherence to both other reviews and the prior experience of the customer, affect perceived quality, confirmation of the customer's expectations, the customer's level of trust in the seller and the consequent purchase intention.

Based on a scenario simulation and an online experiment to collect data, the authors employed AMOS to test the proposed hypotheses using survey data collected from 314 customers in Study 1 and 420 consumers in Study 2.

The results indicate that global consistency positively and significantly contributes to confirmation, perceived quality and trust in sellers while sequential inconsistency positively and significantly influences perceived quality. Meanwhile, purchase intention is positively and significantly promoted by confirmation, perceived quality and trust in sellers, and initial valence has some moderating effects on these relationships.

This study contributes to the understanding of how customers apply product reviews to make purchasing decisions from a new angle. It also elucidates the way in which the perceived consistency of product reviews affects how reviewers are perceived and the consequent effect of these perceptions on a customer's purchase intentions.

In most economies, there are rules from the market regulators or government to sell at an equal wholesale price (EWP). But when one upstream channel is facing a negative demand disruption and another positive, EWP can create extra pressure on the disadvantageous supply chain partner, which faces negative disruption. The purpose of this study is to analyse the impact of EWP and the scope of the discriminatory wholesale price (DWP) during disruptions.

For the study, the authors used a dual-channel supply chain consisting of a manufacturer, online retailer (OR) and traditional brick-and-mortar (BM) retailer. Stackelberg game is used to model the interaction between the upstream and downstream channel partners, and the horizontal Nash game to analyse the interaction within downstream channel partners. For modelling asymmetric disruption, the authors took instances from the lock-down and post-lock-down periods of the COVID-19 pandemic, where consumers flow from BM retailer to OR store.

By analysing the disruption period, the authors found that this asymmetric disruption is detrimental to the BM channel, favourable to OR and has no impact on the manufacturer. But with DWP, the authors found that the profit of the BM channel and manufacturer can be increased during disruption. Though the profit of the OR decreased, it was found to be higher than in the pre-disruption period. Under DWP, the consumer surplus increased during disruption, making it favourable for the customers also. Thus, DWP can aid in creating a win-win strategy for all the supply chain partners during asymmetric disruption. Later as an extension to the study, the authors analysed the impact of the consumer transfer factor and found that it plays a crucial role in the optimal decisions of the channel partner during DWP.

Very scant literature analyses the intersection of DWP and disruptions. To the best of the authors’ knowledge, this study, for the first time uses DWP as a tool to help the disadvantageous supply chain partner during asymmetric disruptions. The study findings will assist the government, market regulators and manufacturers in revamping the wholesale pricing policies and strategies to help the disadvantageous supply chain partner during asymmetric disruption.

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.

This study aims to assess both internal and external factors that impact consumer attitudes and intentions with regard to the purchase of non-deceptive counterfeits. More specifically, this study examines the impact of integrity, brand consciousness, performance risk and social risk on the attitude and in turn on the purchase intention of consumers towards non-deceptive counterfeits.

A total of 679 valid responses from the university students in two different Gulf countries, namely, Oman (264) and Qatar (415) were gathered through a self-administered structured questionnaire and analysed through partial least square‐structural equation modeling.

All the predictors of consumer attitude appeared significant in both country samples except integrity. However, brand consciousness appeared insignificant in the sample of Oman. In addition, Purchase intention towards the non-deceptive counterfeits was significantly predicted by attitude and subjective norm in both samples.

In the domain of non-deceptive counterfeit literature, the findings of the study will substantially add value. Particularly, in the Gulf country context, the impact of internal psychological and external risk factors on the attitude and purchase intention of non-deceptive counterfeits will enhance the insights of existing literature and extend and proof the robustness of the theory of reasoned action.

This study aims to examine foreign direct investment (FDI) factors and develops a rational framework for FDI inflow in Western European countries such as France, Germany, the Netherlands, Switzerland, Belgium and Austria.

Data for this study were collected from the World development indicators (WDI) database from 1995 to 2018. Factors such as economic growth, pollution, trade, domestic capital investment, gross value-added and the financial stability of the country that influence FDI decisions were selected through empirical literature. A framework was developed using interpretable machine learning (IML), decision trees and three-stage least squares simultaneous equation methods for FDI inflow in Western Europe.

The findings of this study show that there is a difference between the most important and trusted factors for FDI inflow. Additionally, this study shows that machine learning (ML) models can perform better than conventional linear regression models.

This research has several limitations. Ideally, classification accuracies should be higher, and the current scope of this research is limited to examining the performance of FDI determinants within Western Europe.

Through this framework, the national government can understand how investors make their capital allocation decisions in their country. The framework developed in this study can help policymakers better understand the rationality of FDI inflows.

An IML framework has not been developed in prior studies to analyze FDI inflows. Additionally, the author demonstrates the applicability of the IML framework for estimating FDI inflows in Western Europe.

Demonstrate proof-of-concept for conducting NFL Draft trades on a blockchain network using smart contracts.

Using Ethereum smart contracts, the authors model several types of draft trades between teams. An example scenario is used to demonstrate contract interaction and draft results.

The authors show the feasibility of conducting draft-day trades using smart contracts. The entire negotiation process, including side deals, can be conducted digitally.

Further work is required to incorporate the full-scale depth required to integrate the draft trading process into a decentralized user platform and experience.

Cutting time for the trade negotiation process buys decision time for team decision-makers. Gains are also made with accuracy and cost.

Full-scale adoption may find resistance due to the level of fan involvement; the draft has evolved into an interactive experience for both fans and teams.

This research demonstrates the new application of smart contracts in the inter-section of sports management and blockchain technology.

In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

The purpose of this study is to explore teen-adult dialogic interactions during the co-design of data literacy activities in order to determine the nature of teen thinking, their emotions, level of engagement, and the power of relationships between teens and adults in the context of data literacy. This study conceives of co-design as a learning space for data literacy. It investigates the teen–adult dialogic interactions and what these interactions say about the nature of teen thinking, their emotions, level of engagement and the power relationships between teens and adults.

The study conceives of co-design as a learning space for teens. Linguistic Inquiry and Word Count (LIWC-22), a natural language processing (NLP) software tool, was used to examine the linguistic measures of Analytic Thinking, Clout, Authenticity, and Emotional Tone using transcriptions of recorded Data Labs with teens and adults. Linguistic Inquiry and Word Count (LIWC-22), a natural language processing (NLP) software tool, was used to examine the linguistic measures of Analytic Thinking, Clout, Authenticity and Emotional Tone using transcriptions of recorded Data Labs with teens and adults.

LIWC-22 scores on the linguistic measures Analytic Thinking, Clout, Authenticity and Emotional Tone indicate that teens had a high level of friendly engagement, a relatively low sense of power compared with the adult co-designers, medium levels of spontaneity and honesty and the prevalence of positive emotions during the co-design sessions.

This study provides a concrete example of how to apply NLP in the context of data literacy in the public library, mapping the LIWC-22 findings to STEM-focused informal learning. It adds to the understanding of assessment/measurement tools and methods for designing data literacy education, stimulating further research and discussion on the ways to empower youth to engage more actively in informal learning about data.

This study applies a novel approach for exploring teen engagement within a co-design project tasked with the creation of youth-oriented data literacy activities.

Nursing students encounter a combination of academic rigor, clinical demands and emotional hurdles. Juggling coursework, practical training and patient interaction can be stressful, and exposure to such situations may impact their psychological well-being. This study aims to highlight the top strengths among nursing students and identify the strengths associated with well-being.

Convenience sampling was used to select a sample of 150 nursing students studying in first, second and third year from colleges of Gujarat and Rajasthan. Students were administered the Values In Action character strengths inventory, the satisfaction with life scale and scale of positive and negative experience. Data was analyzed using descriptive statistics and correlation.

Results show that among nursing students, kindness emerged as the foremost strength with the highest mean, followed by honesty, creativity, spirituality and teamwork, and the strengths of curiosity, gratitude, perseverance, self-regulation, social intelligence, and zest were positively associated with life satisfaction and positive emotions and negatively related to negative emotions.

The small sample size was a limitation; however, this study has been conducted at different locations to improve generalizability.

This study has profound implications for nursing students, both in their personal development and their future roles as health-care professionals, as fostering these attributes can contribute to the students’ growth, well-being and effectiveness as compassionate and competent caregivers. Working on strengths is associated with well-being; therefore, using strengths identified by this study will have a beneficial effect on the students’ well-being.

Curiosity and social intelligence, for instance, can help nurses better understand patient needs and emotions, developing strengths like perseverance and self-regulation can equip nursing students with tools to cope effectively with the challenges inherent in health-care settings. Traits such as gratitude and social intelligence can enhance communication and empathy which are vital skills for establishing rapport with patients and their families. Emphasizing teamwork as a strength aligns with the collaborative nature of health care. By embodying values like kindness and spirituality, nursing students can create a more compassionate and meaningful experience for patients, as well as themselves.

The research paper identifies and emphasizes the five character strengths that are most commonly observed in a sample of Indian nursing students. In addition, this study delves deeper into these identified strengths to understand how they relate to the overall well-being of nursing students within this specific population. The existing literature has not explored it exhaustively.