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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.

The aim of the study is to explore perceptions of the impact of assessment feedback by international undergraduate nursing students. Research to date indicates that summative assessment feedback may impact significantly on student achievement but if it is undertaken sub optimally or does not provide students with the opportunity to engage with the process and reflexively respond, it can also be exceptionally damaging to the learning experience.

A scoping exercise of overall student feedback experience was initially collated via the adoption of an Interpretive Phenomenological Approach (IPA). Participants were recruited via purposive sampling and the LEGO® Serious Play® method was used to collect data. Analysis with Quirkos software was used to examine the salience as well as commonality of findings as an integral part of a recognised five-step thematic analytical approach.

Feedback was perceived, by students, as significantly impacting factor in relation to their overall progression, attainment and retention rates. Themes generated from the findings evidenced student perceptions that summative feedback is a positive driver and source of motivation for academic success and progression. It was perceived that levels of attainment were related to the clarity, quality and individualised nature of feedback that students received and that this was perceived to be evident in their final grades. These were accompanied by perceptions that feedback clarity also determined the potential of breaking down perceived student barriers to learning, their perceived capacity for effective assignment planning and preparation and the likelihood of them having any positive collective or individual interpersonal relationships with their tutors. Summarised, students perceived that feedback ought to lead to student empowerment in managing their studies and as such it ought to be clear, straightforward and non-ambiguous.

The methodological design of the study means that generalisability from its findings was never intended or possible. However, there may be the potential transferability of findings to similar institutions and contexts of nurse education with students who have similar demographic profiling. The study was also a means of providing an insight into the lived experience of students which could be used in the prospective adaptation of feedback mechanisms for staff at a local level within Higher Education.

The study reveals the perceived impact of gamification as a mechanism of summative assessment as conveyed by a designated group of students. Whilst specific recommendations for change can only be made within the context specificity of the research, there may be aspects of the findings which are potentially transferable to other similar contexts of Higher Education delivery whose pedagogical approaches mirror those in operation at the institution where the research was undertaken. It became apparent that the standardisation of feedback approaches offered many opportunities to improve existing systems. The issue of monitoring workloads is also of significance in terms of the level and degree of summative assessment and feedback that academic staff can undertake.

The study revealed the perceived magnitude of assessment feedback on progression, attainment and retention rates, alongside the perceived need for a universal feedback template and the opportunity to provide audio-video feedback. This study adds to existing knowledge in the field of pedagogic practice about both the execution of LEGO® Serious Play® as a research methodology and why the perceptions of feedback as articulated and illuminated by a group of contemporary nursing students ought to matter in the context of Higher Education.

This paper aims to investigate the interrelated role of relational commitment and dependence as drivers of key performance outcomes. Specifically, the authors provide a conceptual model of the impact of commitment on relationship value dependence and switching cost dependence. The authors further investigate how these dimensions of dependence offer differing noneconomic and economic paths to strategic and financial performance.

Survey data was collected from 296 purchasing agents across multiple industries located in the USA. The conceptual model and accompanying hypotheses were tested via partial least squares structural equation modeling.

The results show that the relational path is driven by affective and normative commitment, which are related to relationship value dependence. Conversely, calculative commitment is related to switching cost dependence. This economic path is related to both strategic and financial performance, whereas the relational path is more closely related to strategic as opposed to financial performance outcomes.

This study extends research on Business-To-Business (B2B) relationships by leveraging social exchange theory to examine the interrelated roles played by two forms of dependence on performance outcomes. Thus, the authors answer Scheer et al.’s (2015) call for research into the two distinct types of dependence – relationship value and switching cost dependence – and their roles in determining B2B relationship outcomes. The findings contribute to the literature by integrating social exchange and relationship marketing concepts to develop a dual pathway approach to B2B partnerships.

The results suggest that dependence is not necessarily negative for firms. Specifically, buyers can and do still exhibit positive performance, both strategic and financial, in relationships with suppliers even when dependent on the relationship. Regardless of whether buyers are dependent due to a relationship or economic factors, both can, in different ways, lead to positive strategic and financial outcomes. Together, the authors contribute to the understanding of B2B partnerships by offering guidelines for both buyers and suppliers in the dyad.

The authors derive a comprehensive model depicting primarily relational and economic paths to performance through different types of commitment and dependence. The authors contribute to the literature by demonstrating that relational and economic paths to success are not the same, highlighting how firms could influence performance even when the relationship is not necessarily characterized by generally positive relational benefits and behaviors.

The purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.

This study uses a best–worst discrete choice experiment (BWDCE) and compares the outcome with that of the purchase intention scale, an established probabilistic measure of purchase intention. The total sample consists of 409 fans of three soccer teams sponsored by three different competing brands: Nike, Adidas and Puma.

With sports sponsorship, fans were willing to pay more for the sponsor’s product, with the sponsoring brand obtaining the highest market share. Prominent brands generally performed better than less prominent brands. The best–worst scaling method was also 35% more accurate in predicting brand choice than a purchase intention scale.

Future research could use the same method to study other types of sponsors, such as title sponsors or other product categories.

Sponsorship managers can use this methodology to assess the return on investment in sponsorship engagement.

Prior sponsorship studies on brand equity tend to ignore market share or fans’ willingness to pay a price premium for a sponsor’s goods and services. However, these two measures are crucial in assessing the effectiveness of sponsorship. This study demonstrates how to conduct such an assessment using the BWDCE method. It provides a clearer picture of sponsorship in terms of its economic value, which is more managerially useful.

The prolonged training time of the neural network (NN) has sparked considerable debate regarding their application in the field of optimization. The purpose of this paper is to explore the beneficial assistance of NN-based alternative models in inductance design, with a particular focus on multi-objective optimization and uncertainty analysis processes.

Under Gaussian-distributed manufacturing errors, this study predicts error intervals for Pareto points and select robust solutions with minimal error margins. Furthermore, this study establishes correlations between manufacturing errors and inductance value discrepancies, offering a practical means of determining permissible manufacturing errors tailored to varying accuracy requirements.

The NN-assisted methods are demonstrated to offer a substantial time advantage in multi-objective optimization compared to conventional approaches, particularly in scenarios where the trained NN is repeatedly used. Also, NN models allow for extensive data-driven uncertainty quantification, which is challenging for traditional methods.

Three objectives including saturation current are considered in the multi-optimization, and the time advantages of the NN are thoroughly discussed by comparing scenarios involving single optimization, multiple optimizations, bi-objective optimization and tri-objective optimization. This study proposes direct error interval prediction on the Pareto front, using extensive data to predict the response of the Pareto front to random errors following a Gaussian distribution. This approach circumvents the compromises inherent in constrained robust optimization for inductance design and allows for a direct assessment of robustness that can be applied to account for manufacturing errors with complex distributions.

This paper aims to define a hierarchical and multi-criteria framework based on pillars of the Modernization of Higher Education to evaluate European Accounting Doctoral Programmes (EADE-Model).

The authors applied a quali-quantitative methodology based on the analytic hierarchy process and the survey approach. The authors conducted an extensive literature and regulation review to identify the dimensions affecting the quality of Doctoral Programmes, choosing accounting as the relevant and pivotal field. The authors also used the survey to select the most critical quality dimensions and derive their weight to build EADE Model. The validity of the proposed model has been tested through the application to the Italian scenario.

The findings provide a critical extension of accounting ranking studies constructing a multi-criteria, hierarchical and updated evaluation model recognizing the role of doctoral training in the knowledge-based society. The results shed new light on weak areas apt to be improved and propose potential amendments to enhance the quality standard of ADE.

Theoretical and practical implications of this paper are directed to academics, policymakers and PhD programmes administrators.

The research is original in drafting a hierarchical multi-criteria framework for evaluating ADE in the Higher Education System. This model may be extended to other fields.

Climate change requires the reduction of direct and indirect greenhouse gas (GHG) emissions, a task that seems to clash with increasing supply chain complexity. This study aims to analyse the upstream supply chain complexity dimensions suggesting the importance of understanding the information processing that these may entail. Reducing equivocality can be an issue in some dimensions, requiring the introduction of written guidelines to moderate the effects of supply chain complexity dimensions on GHG emissions at the firm and supply chain level.

A three-year panel data was built with information obtained from Bloomberg, Trucost and Compustat. Hypotheses were tested using random effect regressions with robust standard errors on a sample of 394 SP500 companies, addressing endogeneity through the control function approach.

Horizontal complexity reduces GHG emissions at the firm level, whereas vertical and spatial complexity dimensions increase GHG emissions at the firm and supply chain level. Although the introduction of written guidelines neutralises the negative effects of vertical complexity on firm and supply chain GHG emissions, it is not sufficient in the presence of spatial complexity.

This paper offers novel insights by suggesting that managers need to reconcile the potential trade-off effects on GHG emissions that horizontally complex supply chain structures can present. Their priority in vertically and spatially complex supply chain structures should be to reduce equivocality.

The global impact of the COVID-19 pandemic has been well documented, but there is still limited insight into the complex interaction of factors that determine its longer-term effects on the most vulnerable sectors of society. The current study therefore develops an integrated conceptual framework to investigate how consumers' fear of mortality and the perceived risk of severe illness associated with COVID-19 act as critical determinants of consumer food choices and perceived well-being.

An online survey was utilized to collect data from a sample of 407 adult, low-income consumers across Trinidad and Tobago. The PROCESS macro was used to empirically test the hypothesized relationships in a moderated mediation model.

The results confirm that an increase in the perceived risk of severe illness has a significant negative effect on the consumption of healthy foods and perceived well-being. Moreover, consumers' choice of healthy foods mediates the negative relationship between consumers' perceived risk of severe COVID-19 illness and subjective well-being. Finally, the negative relationship between perceived risk of illness and healthy food choice weakens as an individual's fear of pandemic-related mortality increases.

This research integrates multiple related theoretical constructs to provide a more nuanced understanding of the lingering impact that risk perceptions and fear have on consumer food choices and associated well-being among a vulnerable Caribbean population. The changes identified have important implications for researchers interested in consumer food preferences as well as policymakers seeking to promote a healthy lifestyle among individuals coping with psychologically stressful circumstances.

This study aims to test the complaints of tourists who visit five-star hotels in Antalya, Turkey, on those same hotels’ websites.

In the study, the data were collected with qualitative methods but analyzed with the mixed analysis method. In this context, the authors collected 1,012 comments on the website between 2016 and 2019.

According to the results of the study, the most intense complaints were found to be concentrated in categories such as ambience, food and staff.

First of all, it is thought that it will make an important contribution to the literature, since different methodologies are adopted in the study. In addition, online shares, evaluations and comments produce positive or negative results for the destination or business in question. It is necessary to closely monitor such activities in electronic environments, as they may have negative consequences, thus revealing the need to take corrective or preventive measures. For this reason, the research is important in terms of not having such a large-scale study in the literature and contributing to the hospitality industry.

The purpose of this review is to evaluate existing learning agility measures and offer recommendations for their use in organizational and scholarly contexts.

This is a general review paper assessing the psychometric qualities of prevalent learning agility measures. Measures were selected based on their predominance and use in the learning agility literature and organizational settings.

Learning agility measurement is an area requiring further research. Multiple conceptualizations of learning agility exist, making the true structure of learning agility unclear. The learning agility measures in the academic literature deviate from learning agility’s traditional conceptualization and require further validation and convergent validity studies. Commercial measures of learning agility exist, but their development procedures are not subjected to peer review and are not widely used in academic research, given the cost associated with their use.

Learning agility is prevalently used in organizational settings and is receiving increased scholarly attention. Various conceptualizations and measurement tools exist, and it is unclear how these theories and measures relate and differ. This paper contributes to practice by providing practical guidelines and limitations for measuring learning agility.

Learning agility was initially conceived as a multidimensional construct comprising people agility, results agility, change agility and mental agility. As the construct has evolved, the dimension structure of the measure has evolved as well. This study addresses a gap in our current understanding of how to conceptualize and measure learning agility.