The purpose of this study is to explore the effect of electronic service quality subdimensions upon the behavioural and praising intentions of users engaged in internet banking. Using the survey method, 203 responses were collected from users of online banking in Turkey. A partial least square structural equation model was constructed to test both the reliability and validity of the measurement, as well as the structural model. The results indicated that emotional benefits, ease of use, and control subdimensions, which are influenced through graphical quality and layout clarity, have a significant and positive impact upon the behavioural and praising intentions of users of online banking. The study did not find support for the direct effect of layout clarity upon behavioural and praising intentions.

Quality of work life (QWL) is a generic term that includes an employee's feeling about various aspects of their work including the compensation and rewards, job security, interpersonal relations and the intrinsic meaning of being satisfied in their workplace. This research paper was documented with an intention to understand the influence of the QWL on the productivity of employees working in the banking arena. The study examined the most clout able factors that contributed to the quality of work-life of employees and its correlation on their productivity. A multi-stage sampling method was used to draw a sizeable sample from ten major banks situated in the Karnataka state of India. A total of 756 personnel spread across various branches belonging to rural, semi-urban and urban were covered. The study was validated further by testing various hypotheses drawn based on a review of the literature. The study empirically identified eight major attributes that influence the QWL of bankers. The relationship between QWL and productivity was investigated with notable results using SEM approach.

The phenomenon of cloud computing and related innovations such as Big Data have given rise to many fundamental changes that are evident in information and data. Managing, measuring and developing business value from the plethora of this new data has significant impact on many corporate agendas, particularly in relation to the successful implementation of business intelligence and analytics (BI&A). However, although the influence of Big Data has fundamentally changed the IT application landscape, the metrics for measuring success and in particular, the quality of information, have not evolved. The measurement of information quality and the antecedent factors that influence information has also been identified as an area that has suffered from a lack of research in recent decades. Given the rapid increase in data volume and the growth and ubiquitous use of BI&A systems in organisations, there is an urgent need for accurate metrics to identify information quality.

Evaluation of research artefacts (such as models, frameworks and methodologies) is essential to determine their quality and demonstrate worth. However, in the information quality (IQ) research domain there is no existing standard set of criteria available for researchers to use to evaluate their IQ artefacts. This paper therefore describes our experience of selecting and synthesising a set of evaluation criteria used in three related research areas of information systems (IS), software products (SP) and conceptual models (CM), and analysing their relevance to different types of IQ research artefact. We selected and used a subset of these criteria in an actual evaluation of an IQ artefact to test whether they provide any benefit over a standard evaluation. The results show that at least a subset of the criteria from the other domains of IS, SP and CM are relevant for IQ artefact evaluations, and the resulting set of criteria, most importantly, enabled a more rigorous and systematic selection of what to evaluate.

More than a half billion people use the internet in China, and the environment in which these users work, study, and play using the internet is a rapidly changing one. User perceptions of the quality of information accessed through the internet and through more traditional sources of information may shift over time as the underlying social, cultural, and political environment changes. This study reports the results of a longitudinal survey study of perceptions of information quality of young adults using the internet in China. Results suggest that perceptions of the information quality of internet-based information have shifted more from 2007 to 2012 than perceptions of traditional text sources of information. Implications of the findings for researchers, educators, and information providers are discussed.

In order to improve the quality of higher education, student satisfaction, and employment rate, a data mining based high-quality management method for higher education is proposed. Firstly, construct a high-quality evaluation system for higher education based on the principles of education quality evaluation. Secondly, the association rule mining method is used to construct a university education quality management model and determine the weight of the impact indicators for high-quality management of university education. Finally, the fuzzy evaluation method is used to determine the high-quality evaluation function of higher education, and the results of high-quality evaluation of higher education are obtained. High-quality management strategies are developed based on the evaluation results to improve the quality of education. The experimental results show that the student satisfaction rate of this method can reach 99.3%, and the student employment rate can reach 99.9%.

In order to improve the evaluation effect of current university teaching reform, a new method for evaluating the quality of university course teaching reform is proposed based on data mining algorithms. Firstly, the optimal data clustering criterion was used to select evaluation indicators and a quality evaluation system for university curriculum teaching reform was established. Next, a reform quality evaluation model is constructed using BP neural network, and the training process is improved through genetic algorithm to obtain the model weight and threshold of the optimal solution. Finally, the calculated parameters are substituted into the model to achieve accurate evaluation of the quality of university curriculum teaching reform. Selecting evaluation accuracy and evaluation efficiency as evaluation indicators, the practicality of the proposed method was verified through experiments. The experimental results showed that the proposed method can mine teaching reform data and evaluate the quality of teaching reform. Its evaluation accuracy is higher than 96.3%, and the evaluation time is less than 10ms, which is much better than the comparison method, fully demonstrating the practicality of the method.

Research on the quality evaluation of teaching reforms plays an important role in promoting improvements in teaching quality. Therefore, an evaluation method of teaching reform quality in colleges and universities based on big data analysis is proposed. A multivariate logistic model is used to select the evaluation indicators for the quality evaluation of teaching reforms in universities. And clustering and cleaning of the evaluation indicator data are performed through big data analysis. The evaluation indicator data is used as input vectors, and the results of the teaching reform quality evaluation are used as output vectors. A support vector machine model based on the whale algorithm is built to obtain the relevant evaluation results. Experimental results show that the proposed method achieves a minimum recall rate of 98.7% for evaluation indicator data, the minimum data processing time of 96.3 ms, the accuracy rate consistently above 97.1%.

In order to improve the comprehensiveness of evaluation results and reduce errors, a teaching reform quality evaluation method based on fuzzy comprehensive evaluation is proposed. Firstly, on the premise of meeting the principles of indicator selection, factor analysis is used to construct an evaluation indicator system. Then, calculate the weights of various evaluation indicators through fuzzy entropy, establish a fuzzy evaluation matrix, and calculate the weight vector of evaluation indicators. Finally, the fuzzy cognitive mapping method is introduced to improve the fuzzy comprehensive evaluation method, obtaining the final weight of the evaluation indicators. The weight is multiplied by the fuzzy evaluation matrix to obtain the comprehensive evaluation result. The experimental results show that the maximum relative error of the proposed method's evaluation results is about 2.0, the average comprehensive evaluation result is 92.3, and the determination coefficient is closer to 1, verifying the application effect of this method.

In order to overcome the problems of low evaluation accuracy and long evaluation time in traditional teaching quality evaluation methods, a method of English distance information teaching quality evaluation based on decision tree classification algorithm is proposed. Firstly, construct teaching quality evaluation indicators under different roles. Secondly, the information gain theory in decision tree classification algorithm is used to divide the attributes of teaching resources. Finally, the rough set theory is used to calculate the index weight and establish the risk evaluation index factor set. The result of teaching quality evaluation is obtained through fuzzy comprehensive evaluation method. The experimental results show that the accuracy rate of the teaching quality evaluation of this method can reach 99.2%, the recall rate of the English information teaching quality evaluation is 99%, and the time used for the English distance information teaching quality evaluation of this method is only 8.9 seconds.

Aim/Purpose: This research aims to evaluate models from meta-learning techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic Meta-Learning (MAML), and Reptile meta-learning, to obtain high-quality metadata. The goal is to utilize this metadata to increase accuracy and efficiency in identifying chili varieties in smart agriculture. Background: The identification of chili varieties in smart agriculture is a complex process that requires a multi-faceted approach. One challenge in chili variety identification is the lack of a large and diverse dataset. This can be addressed using meta-learning techniques, which allow the model to leverage knowledge learned from other related tasks or artificially expand the dataset by applying transformations to existing data. Another challenge is the variation in growing conditions, which can affect the appearance of chili varieties. Meta-learning techniques can help address this challenge by allowing the model to adapt to variations in growing conditions with task-specific embeddings and optimizations. With the help of meta-learning techniques, such as data augmentation, data characterization, selection of datasets, and performance estimation, quality metadata for accurate identification of chili varieties can be achieved even in the presence of limited data and variations in growing conditions. Furthermore, the use of meta-learning techniques in chili variety identification can also assist in addressing challenges related to the computational complexity of the task. Methodology: The research approach employed is quantitative, specifically comparing three models from meta-learning techniques to determine which model is most suitable for our dataset. Data was collected from the variety assembly garden in the form of images of chili leaves using a mobile device. The research successfully gathered 1,974 images of chili leaves, with 697 images of large red chilies, 649 images of curly red chilies, and 628 images of cayenne peppers. These chili leaf images were then processed using augmentation techniques. The results of image data augmentation were categorized based on leaf characteristics (such as oval, lancet, elliptical, serrated leaf edges, and flat leaf edges). Subsequently, training and validation utilized three models from meta-learning techniques. The final stage involved model evaluation using 2-way and 3-way classification, as well as 5-shot and 10-shot learning scenarios to select the dataset with the best performance. Contribution: Improving classification accuracy, with a focus on ensuring high-quality data, allows for more precise identification and classification of chili varieties. Enhancing model training through an emphasis on data quality ensures that the models receive reliable and representative input, leading to improved generalization and performance in identifying chili varieties. Findings: With small collections of datasets, the authors have used data augmentation and meta-learning techniques to overcome the challenges of limited data and variations in growing conditions. Recommendations for Practitioners: By leveraging the knowledge and adaptability gained from meta-learning, accurate identification of chili varieties can be achieved even with limited data and variations in growing conditions. The use of meta-learning techniques in chili variety identification can greatly improve the accuracy and reliability of the identification process. Recommendation for Researchers: Using meta-learning techniques, such as transfer learning and parameter optimization, researchers can overcome challenges related to limited data and variations in growing conditions in chili variety identification. Impact on Society: The findings from this research can help identify superior chili seeds, thereby motivating farmers to cultivate high-quality chilies and achieve bountiful harvests. Future Research: We intend to verify our approach on a more extensive array of datasets and explore the implementation of more resilient regularization techniques, going beyond image augmentation, within the meta-learning techniques. Furthermore, our goal is to expand our research to encompass the automatic learning of parameters during training and tackle issues associated with noisy labels. Building on the insights gained from our observed outcomes, a future objective is to enhance the refinement of model-agnostic meta-learning techniques that can effectively adapt to intricate task distributions with substantial domain gaps between tasks. To realize this aim, our proposal involves devising model-agnostic meta-learning techniques specifically designed for multi-modal scenarios.

In recent years, schools (as well as universities) have added cyber security to their computer science curricula. This topic is still new for most of the current teachers, who would normally have a standard computer science background. Therefore the teachers are trained and then teaching their students what they have just learned. In order to explore differences in both populations’ learning, we compared measures of software quality and security between high-school teachers and students. We collected 109 source files, written in Python by 18 teachers and 31 students, and engineered 32 features, based on common standards for software quality (PEP 8) and security (derived from CERT Secure Coding Standards). We use a multi-view, data-driven approach, by (a) using hierarchical clustering to bottom-up partition the population into groups based on their code-related features and (b) building a decision tree model that predicts whether a student or a teacher wrote a given code (resulting with a LOOCV kappa of 0.751). Overall, our findings suggest that the teachers’ codes have a better quality than the students’ – with a sub-group of the teachers, mostly males, demonstrate better coding than their peers and the students – and that the students’ codes are slightly better secured than the teachers’ codes (although both populations show very low security levels). The findings imply that teachers might benefit from their prior knowledge and experience, but also emphasize the lack of continuous involvement of some of the teachers with code-writing. Therefore, findings shed light on computer science teachers as lifelong learners. Findings also highlight the difference between quality and security in today’s programming paradigms. Implications for these findings are discussed.

Teaching object-oriented programming to students in an in-classroom environment demands well-thought didactic and pedagogical strategies in order to guarantee a good level of apprenticeship. To teach it on a completely distance learning environment (e-learning) imposes possibly other strategies, besides those that the e-learning model of Open University of Portugal dictates. This article analyses the behavior of the students of the 1st cycle in Computer Science while interacting with the object-oriented programming (OOP) discipline available to them on the Moodle platform. Through the evaluation of the level of interaction achieved in a group of relevant selected actions by the students, it is possible to identify their relevancy to the success of the programming learning process. Data was extracted from Moodle, numerically analyzed, and, with the use of some charts, behavior patterns of students were identified. This paper points out potential new approaches to be considered in e-learning in order to enhance programming learning results, besides confirming a high level of drop-out and a low level of interaction, thus finding no clear correlation between students’ success and the number of online actions (especially in forums), which reveals a possible failure of the main pillar on which the e-learning model relies.

Web portals have been used as information products to deliver personalized, feature-rich, and flexible information needs to Internet users. However, all portals are not equal. Most of them have relatively a small number of visitors, while a few capture the majority of surfers. This study seeks to uncover the factors that contribute the perceived quality of a general portal. Based on 21 factors derived from an extensive literature review on Information Product Quality (IPQ), web usage, and media use, an experimental study was conducted to identify the factors that are perceived by web portal users as most relevant. The literature categorizes quality factors of an information product in three dimensions: information, physical, and service. This experiment suggests a different clustering of factors: Content relevancy, Communication interactiveness, Information currency, and Instant gratification. The findings in this study will help developers find a more customer-oriented approach to developing high-traffic portals.

Aim/Purpose: Professional companies selling persuasive-communication services via the World Wide Web need to be exemplars of effective informing practices. Their credibility is at risk if their websites do not excel in marketing message and use of medium. Their unique brands need to be expressed through website technology and content, or they cannot compete successfully. Background: Compares marketing communication consultants’ websites with expert criteria. Methodology: Content analysis of 40 advertising agency websites. Contribution: Links an evaluation of advertising agency compliance with expert website criteria to established branding constructs. Findings: Most small advertising agencies could improve their brand reputations through better compliance with experts’ recommended website design and content criteria. Recommendations for Practitioners: A hierarchy of recommendations for practitioners is offered, addressing ease and importance. Impact on Society: Clarity and credibility of message and medium improve our ability to practice effective informing. Future Research: Explore online communications of specialized populations such as digital marketing experts.

Aim/Purpose: The aim of this paper is to identify the possibilities for reducing the second and third levels of the digital divide (or inequality) through conscious application of digital technologies, especially through the promotion of digital means for information, enlightenment, and entertainment. Background: This article reviews studies carried out between 2000 and 2017, which investigate the social benefits of digital technology use for disadvantaged user groups and, especially, of their outcomes in terms of increasing digital skills and motivation to use information and communication technologies. Methodology: The literature review of the selected texts was carried out using thematic content analysis. The coding scheme was open but based on the theory of three levels of digital divide by van Dijk. Contribution: The results of the analysis show the difficulties related to the attempts of reducing the digital divide on the second and third level using only digital interventions, but also reveal the potential of these interventions. Findings: The literature review confirms the connection of different levels of digital divide with other relational and structural inequalities. It provides insights into the strengths and weaknesses of digital interventions aimed at the reduction of digital inequalities. Their success depends on the consideration of the context and participants needs as well as on carefully planned strategies. The paper summarizes and demonstrates the shortcomings and limitations of poorly designed interventions in reducing the digital divide but emphasizes the possibilities of raising the motivation and benefits for the participants of strategically planned and implemented projects. Recommendations for Practitioners: While planning a digital intervention with the aim of reducing digital inequalities, it is necessary to assess carefully the context and the needs of participants. Educational interventions should be based on suitable didactic and learning strategies. Recommendation for Researchers: More research is needed into the factors that increase the effectiveness of digital interventions aimed at reducing the digital divide. Future Research: We will apply the findings of this literature review in an intervention in the context of Lithuanian towns of different sizes.

Aim/Purpose: Although a literature review is the fundamental base for any research, it is often considered tedious and conducted with a lack of methodology and rigor. The paper presents a method for systematically searching and screening literature using modern search technologies. The method focuses on minimizing the amount of manual screening by employing the references among papers. Background: A method to select quality literature effectively using modern search technologies is presented and evaluated. Methodology: The method starts with a keywords search in which the most suitable keywords are identified. In the backward search, promising resources are collected based on the keywords and their reference sections are searched for duplicates to find often cited basic literature. Then, the forward search identifies current literature that cites the basic sources. Contribution: Modern search technologies have the potential to improve the effectiveness of the use of information channels significantly and thus of traditional literature searches. Findings: The selection method was applied to the field of literature review itself and to the field of functional modelling. In both cases, relevant literature was identified within a surprisingly short time. Recommendation for Researchers: Literature reviews should be done systematically by using modern search technologies. Future Research: The presented method may be adapted according to the evolution of search technologies. The tool support for the automated extraction of references should be improved and a quantitative evaluation of the method in comparison to traditional reviews may foster the findings.

Aim/Purpose: This study seeks to uncover the perceived quality factors of computer-mediated communication in business planning in which communication among teammates is crucial for collaboration. Background: Computer-mediated communication has made communicating with teammates easier and more affordable than ever. What motivates people to use a particular CMC technology during business planning is a major concern in this research. Methodology: This study seeks to address the issues by applying the concept of Information Product Quality (IPQ). Based on 21 factors derived from an extensive literature review on Information Product Quality (IPQ), an experimental study was conducted to identify the factors that are perceived as most relevant. Contribution: The findings in this study will help developers find a more customer-oriented approach to developing CMC technology design, specifically useful in collaborative work, such as business planning. Findings: This study extracted the three specific quality factors to use CMC technology in business planning: informational, physical, and service. Future Research: Future research will shed more light on the generality of these findings. Future studies should be extended to other population and contextual situations in the use of CMC.

Aim/Purpose: This study offers an important contribution to the literature on couple social comparisons by showing how different aspects of comparisons are related to relationship quality. Background: Making social comparisons is a daily tendency of human beings that does not only occur on an individual level but also in the context of romantic relationships. This phenomenon is widespread among couples, though partners differ in terms of their propensity to make couple social comparisons. The literature has shown that all these facets of couple social comparison play an important role in relationship functioning. Methodology: In the current study of 104 young adults in a heterosexual relationship, we investigated the association of couple social comparison propensity, explicit couple social comparisons, and implicit couple social comparisons with couple relationship quality in terms of commitment and relationship satisfaction. Contribution: So far, studies have not tested all these aspects in predicting partners’ relationship quality. Findings: Results showed that commitment was negatively predicted by relationship social comparison propensity and positively predicted by implicit couple social comparisons, while relationship satisfaction was positively predicted by both implicit and explicit couple social comparisons. Recommendation for Researchers: Our results have implications for couple interventions. In preventive interventions, sustaining a positive view of one’s relationship may promote relationship satisfaction and commitment. Future Research: Future research should adopt a dyadic design to investigate cross-partner associations.

Hierarchy is a reality of group life, for humans as well as for most other group-living species. And yet, there remains considerable debate about whether and when hierarchy can promote group performance and member satisfaction. We suggest that progress in this debate has been hampered by a lack of clarity about hierarchy and how to conceptualize it. Whereas prevailing conceptualizations of hierarchy in the group and organization literature focus on inequality in member power or status (i.e., centralization or steepness), we build on the ethological and social network traditions to advance a view of hierarchy as cascading relations of dyadic influence (i.e., acyclicity). We further suggest that hierarchy thus conceptualized is more likely to capture the functional benefits of hierarchy whereas hierarchy as inequality is more likely to be dysfunctional. In a study of 75 teams drawn from a wide range of industries, we show that whereas acyclicity in influence relations reduces conflict and thereby enhances both group performance and member satisfaction, centralization and steepness have negative effects on conflict, performance, and satisfaction, particularly in groups that perform complex tasks. The theory and results of this study can help to clarify and advance research on the functions and dysfunctions of hierarchy in task groups.

The burgeoning economic inequality between the richest and the poorest is a cause of concern for social, political, and ethical reasons. While businesses are both implicated and affected by growing inequality, business schools have largely neglected to subject the phenomenon to sufficient critique. This is, in part, because far too many management educators rely on orthodox economic perspectives—often represented by neoliberal capitalism—which have dominated the curricula and the teaching philosophy of business schools. To address this issue, this article underscores the need for business schools to critically examine the relationship between neoliberal capitalism and economic inequalities, and to overtly engage with this nexus in pedagogical practice. The article concludes by revisiting the concepts of relationality and answerability as paths by which to address the current predicament. Relationality and answerability collectively offer: i) conceptual and reflexive tools by which to re-imagine business school education, and, ii) space for business schools to debate important questions about the taken-for-granted, but problematic, assumptions underlying the ideology of neoliberal capitalism

PUTRAJAYA: Active collaboration from industry players is needed for the Technical and Vocational Education and Training (TVET) institution to offer programmes that are of a higher quality and more relevant to market needs.

Deputy Prime Minister Datuk Seri Fadillah Yusof said strong cooperation between the industry players and the TVET institution was crucial to ensure the comprehensive matching of demand and supply.

He said consistent input from the industry players on the workforce skills and needs was also needed by the TVET institution to develop suitable curricula and programmes.

“Forging close cooperation with industry players can create a new skilled workforce to master the latest technology, which will have a spillover effect on encouraging economic growth.

“I call for the active involvement of industry players in Malaysia to collaborate with the government in supporting the agenda to empower TVET,” he said at the 2024 Prime Minister’s Gold Hand Award and Skilled Person Award ceremony here today.

Meanwhile, Fadillah said the government is aware that the TVET stream in Malaysia needs to be improved for it to be more systematic and effective.

He said the organisation of skills competitions was one of the government’s efforts to promote and ensure the quality of delivery of TVET training in Malaysia is in line with international standards.

“I call on all TVET agencies to hold skills competitions at institutional levels so that we can pick the best talent for national and international-level competitions,” he said.

In his speech, Fadillah also thanked and congratulated the national contingent which made sure the Jalur Gemilang was hoisted proudly at the WorldSkills Competition Lyon 2024 at the Euroexpo Lyon in France from Sept 10-15.

In the competition, Malaysia, represented by 15 participants across 14 categories, captured five medals - one bronze medal in the Beauty Therapy category through Wong Hsun Wei and four Medallion for Excellence.

The four Medallion for Excellence recipients were Muhammad Nasran Ahmad in the Hairdressing category; Ahmad Muizuddin Mohd Razi in the Bricklaying category; Muhammad Hakimi Abu Bakar in Electrical Installations; and Stephen Sim Shan Siong in the IT Software Solutions for Business category.

Globally, only 7% of women have financial services as per the World Bank Global Index Report 2017

Streaming video still provides some meaningful advantages to traditional cable: it’s generally cheaper (assuming you don’t sign up for every service under the sun); customer satisfaction ratings are generally higher; and users have more power to pick and choose and cancel services at a whim. But the party simply isn’t poised to last. Thanks to […]

In this Quality for life video, Kevin Holston, a certified Black Belt, shares how he uses Six Sigma tools in his everyday life, including providing humorous examples of how keeps his life in order and on track.

High-quality biodiversity data is essential for answering key questions on biodiversity in Europe, for example regarding the state and trends of species or for evaluating ecosystem services and functions on various scales. A new EU BON report "Gap analysis and priorities for filling identified gaps in data coverage and quality" evaluates the state of available biodiversity information and points out gaps of available biodiversity information sources.

The report aims to assess relevant data sources on biodiversity on a European and global scale. The assessment particularly evaluates the gaps of available biodiversity information sources and, after outlining the most important ones, identifies priorities for improving the data availability and gives recommendations of how they can be closed.

The report is divided into three main sections, starting first with an overall overview of gaps and limitations of biodiversity datasets. After outlining some general limitations of biodiversity data in Europe, the key findings from the specific analyses are summarized along with recommendations of how existing gaps can be closed. The last part presents a chapter containing the specific gap analysis for a selection of several main global and European datasets. The datasets represent some main sources for biodiversity data, either for specific realms (terrestrial, marine, freshwater), taxonomic groups, thematic fields (taxonomy, genetic databases) or networks of European test sites.