Aim/Purpose: This study presents some activities that integrate computational thinking (CT) into mathematics lessons utilizing GeoGebra to promote constructionist learning. Background: CT activities in the Indonesian curriculum are dominated by worked examples with less plugged-mode activities that might hinder students from acquiring CT skills. Therefore, we developed mathematics and CT (math+CT) lessons to promote students’ constructionist key behaviors while learning. Methodology: The researchers utilized an educational design research (EDR) to guide the lesson’s development. The lesson featured 11 applets and 22 short questions developed in GeoGebra. To improve the lesson, it was sent to eight mathematics teachers and an expert in educational technology for feedback, and the lesson was improved accordingly. The improved lessons were then piloted with 17 students, during which the collaborating mathematics teachers taught the lessons. Data were collected through the students’ work on GeoGebra, screen recording when they approached the activities, and interviews. We used content analysis to analyze the qualitative data and presented descriptive statistics to quantitative data. Contribution: This study provided an example and insight into how CT can be enhanced in mathematics lessons in a constructionist manner. Findings: Students were active in learning mathematics and CT, especially when they were engaged in programming and debugging tasks. Recommendations for Practitioners: Educators are recommended to use familiar mathematics software such as GeoGebra to support students’ CT skills while learning mathematics. Additionally, our applets are better run on big-screen devices to optimize students’ CT programming and debugging skills. Moreover, it is recommended that students work collaboratively to benefit from peer feedback and discussion. Recommendation for Researchers: Collaboration with teachers will help researchers better understand the situation in the classroom and how the students will respond to the activities. Additionally, it is important to provide more time for students to get familiar with GeoGebra and start with fewer errors to debug. Future Research: Further research can explore more mathematics topics when integrating CT utilizing GeoGebra or other mathematics software or implement the lessons with a larger classroom size to provide a more generalizable result and deeper understanding.

Aim/Purpose: This exploratory qualitative case study examines the perceptions of high-school learners of English regarding a pedagogical intervention involving progressive reduction of captions (full, sentence-level, keyword captions, and no-captions) in enhancing language learning. Background: Recognizing the limitations of caption usage in fostering independent listening comprehension in non-captioned environments, this research builds upon and extends the foundational work of Vanderplank (2016), who highlighted the necessity of a comprehensive blend of tasks, strategies, focused viewing, and the need to actively engage language learners in watching captioned materials. Methodology: Using a qualitative research design, the participants were exposed to authentic video texts in a five-week listening course. Participants completed an entry survey, and upon interaction with each captioning type, they wrote individual reflections and participated in focus group sessions. This methodological approach allowed for an in-depth exploration of learners’ experiences across different captioning scenarios, providing a nuanced understanding of the pedagogical intervention’s impact on their perceived language development process. Contribution: By bridging the research-practice gap, our study offers valuable insights into designing pedagogical interventions that reduce caption dependence, thereby preparing language learners for success in real-world, caption-free listening scenarios. Findings: Our findings show that learners not only appreciate the varied captioning approaches for their role in supporting text comprehension, vocabulary acquisition, pronunciation, and on-task focus but also for facilitating the integration of new linguistic knowledge with existing background knowledge. Crucially, our study uncovers a positive reception towards the gradual shift from fully captioned to uncaptioned materials, highlighting a stepwise reduction of caption dependence as instrumental in boosting learners’ confidence and sense of achievement in mastering L2 listening skills. Recommendations for Practitioners: The implications of our findings are threefold: addressing input selection, task design orchestration, and reflective practices. We advocate for a deliberate selection of input that resonates with learners’ interests and contextual realities alongside task designs that progressively reduce caption reliance and encourage active learner engagement and collaborative learning opportunities. Furthermore, our study underscores the importance of reflective practices in enabling learners to articulate their learning preferences and strategies, thereby fostering a more personalized and effective language learning experience. Recommendation for Researchers: Listening comprehension is a complex process that can be clearly influenced by the input, the task, and/or the learner characteristics. Comparative studies may struggle to control and account for all these variables, making it challenging to attribute observed differences solely to caption reduction. Impact on Society: This research responds to the call for innovative teaching practices in language education. It sets the stage for future inquiries into the nuanced dynamics of caption usage in language learning, advocating for a more learner-centered and adaptive approach. Future Research: Longitudinal quantitative studies that measure comprehension as captions support is gradually reduced (full, partial, and keyword) are strongly needed. Other studies could examine a range of individual differences (working memory capacity, age, levels of engagement, and language background) when reducing caption support. Future research could also examine captions with students with learning difficulties and/or disabilities.

Aim/Purpose: This mixed-methods study aims to examine factors influencing academicians’ intentions to continue using AI-based chatbots by integrating the Task-Technology Fit (TTF) model and social network characteristics. Background: AI-powered chatbots are gaining popularity across industries, including academia. However, empirical research on academicians’ adoption behavior is limited. This study proposes an integrated model incorporating TTF factors and social network characteristics like density, homophily, and connectedness to understand academics’ continuance intentions. Methodology: A qualitative study involving 31 interviews of academics from India examined attitudes and the potential role of social network characteristics like density, homophily, and connectedness in adoption. Results showed positive sentiment towards chatbots and themes on how peer groups accelerate diffusion. In the second phase, a survey of 448 faculty members from prominent Indian universities was conducted to test the proposed research model. Contribution: The study proposes and validates an integrated model of TTF and social network factors that influence academics’ continued usage intentions toward AI chatbots. It highlights the nuanced role of peer networks in shaping adoption. Findings: Task and technology characteristics positively affected academics’ intentions to continue AI chatbot usage. Among network factors, density showed the strongest effect on TTF and perceived usefulness, while homophily and connectedness had partial effects. The study provides insights into designing appropriate AI tools for the academic context. Recommendations for Practitioners: AI chatbot designers should focus on aligning features to academics’ task needs and preferences. Compatibility with academic work culture is critical. Given peer network influences, training and demonstrations to user groups can enhance adoption. Platforms should have capabilities for collaborative use. Targeted messaging customized to disciplines can resonate better with academic subgroups. Multidisciplinary influencers should be engaged. Concerns like plagiarism risks, privacy, and job impacts should be transparently addressed. Recommendation for Researchers: More studies are needed across academic subfields to understand nuanced requirements and barriers. Further studies are recommended to investigate differences across disciplines and demographics, relative effects of specific network factors like size, proximity, and frequency of interaction, the role of academic leadership and institutional policies in enabling chatbot adoption, and how AI training biases impact usefulness perceptions and ethical issues. Impact on Society: Increased productivity in academia through the appropriate and ethical use of AI can enhance quality, access, and equity in education. AI can assist in mundane tasks, freeing academics’ time for higher-order objectives like critical thinking development. Responsible AI design and policies considering socio-cultural aspects will benefit sustainable growth. With careful implementation, it can make positive impacts on student engagement, learning support, and research efficiency. Future Research: Conduct longitudinal studies to examine the long-term impacts of AI chatbot usage in academia. Track usage behaviors over time as familiarity develops. Investigate differences across academic disciplines and roles. Requirements may vary for humanities versus STEM faculty or undergraduate versus graduate students. Assess user trust in AI and how it evolves with repeated usage, and examine trust-building strategies. Develop frameworks to assess pedagogical effectiveness and ethical risks of conversational agents in academic contexts.

This research aims to establish a valid and accurate measurement scale and identify consumer-driven characteristics for minimalism. The study has employed a hybrid approach to produce items for minimalism. Expert interviews were conducted to identify the items for minimalism in the first phase followed by consumer survey to obtain their response in second phase. A five-point Likert scale was used to collect the data. Further, data was subjected to reliability and validity check. Structural equation modelling was used to test the model. The findings demonstrated that there are five dimensions by which consumers perceive minimalism: decluttering, mindful consumption, aesthetic choices, financial freedom, and sustainable lifestyle. The outcome also revealed a high correlation between simplicity and well-being. This study is the first to provide a reliable and valid instrument for minimalism. The results will have several theoretical and practical ramifications for society and policymakers. It will support policymakers in gauging and encouraging minimalistic practices, which enhance environmental performance and lower carbon footprint.

Monetary policy is essential for sustainable growth where effective monetary policies can improve investment, employment, and consumption by fostering a balanced and resilient economy. However, sustainable development is vital for harmonising economic growth, social equity, and environmental preservation. A number of factors have been discussed in the literature that impact sustainable development. However, this study explicitly tries to investigate the nexus among the monetary policy (MP) toward sustainable development (SD) with the mediation of e-banking services (e-BS) and moderation of financial risk management (FRM) from China drawing on stakeholder theory. It discovered a significant connection between monetary policy and sustainable development along with sub-dimensions of SD. Likewise, this study confirmed a positive mediating influence of e-BS between monetary policy and sustainable development. Finally, the study additionally ensured a positive moderation of financial risk between monetary policy and sustainable development, respectively. These outcomes bestow several interesting insights into monetary policy, e-banking services, financial risk management, and sustainable development.

Numerous scandals due to unethical decisions occur despite the growing use of decision support systems (DSS). Several scholars recommend incorporating ethical attributes along with business requirements in DSS design. However, little guidance exists to indicate which ethical attributes to include and the importance ethical attributes should be given in comparison to business requirements. This study addresses this deficiency by identifying ethical requirements to integrate in DSS design drawn from the business ethics literature. This study conducted a large-scale empirical survey with information technology decision-makers to examine the relative importance of DSS fit with ethical and business requirements as well as the appropriate balance of those requirements on perceived DSS performance. The results show that decision makers perceive better DSS performance when the ethical and business requirements align with its organisation's beliefs than from ethical or business requirements alone.

This research investigates the correlation between risk management and supply chain performance (SCP) along with moderation of AI-embedded technologies such as big data analytics, Internet of Things (IoT), virtual reality, and blockchain technologies. To calculate the results, this study utilised 644 questionnaires through the structural equation modelling (SEM) method. It is revealed using SmartPls that financial risk management (FRM) is positively linked with SCP. Second, it was observed that AI significantly moderates the connection between FRM and SCP. In addition, the study presents certain insights into supply chain and AI-enabled technologies and how these capabilities can beneficially advance SCP. Besides, certain implications, both managerial and theoretical are described for the supply chain managers along with limitations for future scholars of the world.

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.

Conventional intelligent traffic congestion discrimination methods mainly use GPS terminals to collect traffic congestion data, which is vulnerable to the influence of vehicle time distribution, resulting in poor final discrimination effect. Necessary to design a new intelligent traffic congestion discrimination method based on wireless sensor network front-end data collection. That is to use the front-end data acquisition technology of wireless sensor network to generate a front-end data acquisition platform to obtain intelligent traffic congestion data, and then design an intelligent traffic congestion discrimination algorithm based on traffic congestion rules so as to achieve intelligent traffic congestion discrimination. The experimental results show that the intelligent traffic congestion discrimination method designed based on the front-end data collection of wireless sensor network has good discrimination effect, the obtained discrimination data is more accurate, effective and has certain application value, which has made certain contributions to reducing the frequency of urban traffic accidents.

Bright Lights has a history of success, but is at a pivotal point, facing the pains of strategic change. One salesperson has found a way to maintain sales and increase profit margin, but it requires operating between the lines of ethical boundaries. Ethics provides a choice between right and right as opposed to moral temptation of right and wrong (Kidder, 1996). As the case unfolds, Jim receives a mandate of which customers he can call on, reducing sales, profit margin, and customer satisfaction. A top performer, Jim finds a solution within company policy and the law, but although not hidden, is not entirely transparent. This creates two ethical decisions: 1) Should he be reprimanded or praised? 2) Should the company update policies to ban his actions, or promote his actions among other salespeople? This case clearly strikes the dilemma found in navigating the boundaries of a questionable business strategy.

In the perspective of institutional theory, business education is an institutional field, in which two major institutional forces are accreditations and rankings. In this context, French business schools (BS) have adopted an isomorphic response by starting to engage in research and publishing in academic journals. Studies have discussed their research as a new institutional trajectory. However, what remains unknown is how they differ from each other in such research dynamics. To bring new insights to the discussion, this quantitative study examines, over the period of 2008-2018, the evolution of research of French BS by systematically comparing the 'best' schools with other schools in all analyses. The results indicate a strong isomorphism in terms of publication quantity and productivity, scale of research collaboration and the internationalisation of research. However, these schools are heterogeneous in terms research quality and scale of international research collaboration, reflecting the diversity in their research strategy.

Students in teaching-focused business-schools need a level of assistance and advisement broader and more profound than what is needed in R1&R2 schools. We investigate the informal interdependencies among marketing, finance, operation, and management core courses in these schools. By conducting hypothesis tests on a large dataset, we identify a flexible network showing the preferred sequencing of these courses to improve students' performance as measured by the course grade. Better performances in this context may also lead to higher retention-rates and lower time-to-degree. We recommend taking Finance or Finance and Management as the first course(s). Marketing should be the next course before or concurrent with Operations Management. Regarding the lower division courses, it is recommended to take Statistics before Economics and Accounting courses and Accounting before or concurrent with Economics. We also consider the significant role of a milestone course that links the lower division and core courses.

Aiming at the problems of long evaluation time and poor evaluation accuracy of existing evaluation methods, an improved decision tree-based evaluation method for the effectiveness of college online course teaching reform is proposed. Firstly, the teaching mode of college online course is analysed, and an evaluation system is constructed to ensure the applicability of the evaluation method. Secondly, AHP entropy weight method is used to calculate the weights of evaluation indicators to ensure the accuracy and authority of evaluation results. Finally, the evaluation model based on decision tree algorithm is constructed and improved by fuzzy neural network to further optimise the evaluation results. The parameters of fuzzy neural network are adjusted and gradient descent method is used to optimise the evaluation results, so as to effectively evaluate the effect of college online course teaching reform. Through experiments, the evaluation time of the method is less than 5 ms, and the evaluation accuracy is more than 92.5%, which shows that the method is efficient and accurate, and provides an effective evaluation means for the teaching reform of online courses in colleges and universities.

In view of the problems of low accuracy, long time consuming and low efficiency of the existing engineering investment risk assessment method; this paper puts forward the investment risk assessment method of power grid construction project based on grey correlation analysis. Firstly, classify the risks of power grid construction project; secondly, determine the primary index and secondary index of investment risk assessment of power grid construction project; then construct the correlation coefficient matrix of power grid project investment risk to calculate the correlation degree and weight of investment risk index; finally, adopt the grey correlation analysis method to construct investment risk assessment function to realise investment risk assessment. The experimental results show that the average accuracy of evaluating the investment risk of power grid construction projects using the method is 95.08%, and the maximum time consuming is 49 s, which proves that the method has high accuracy, short time consuming and high evaluation efficiency.

In order to solve the problems of large generalisation error, low recall rate and low retrieval accuracy of customer evaluation information in traditional trust evaluation methods, an evaluation method of customer trust in e-commerce market based on entropy weight analytic hierarchy process was designed. Firstly, build an evaluation index system of customer trust in e-commerce market. Secondly, the customer trust matrix is established, and the index weight is calculated by using the analytic hierarchy process and entropy weight method. Finally, five-scale Likert method is used to analyse the indicator factors and establish a comment set, and the trust evaluation value is obtained by combining the indicator membership. The experiment shows that the maximum generalisation error of this method is only 0.029, the recall rate is 97.5%, and the retrieval accuracy of customer evaluation information is closer to 1.

The major technological developments have changed the traditional way of doing business. These developments have facilitated whistle-blowing. Access to data is easier and faster and communicating with the public can be done in seconds. Another development is the artificial intelligence (AI) which enters the business workplace in different forms challenging the traditional working relations. The combination of these concepts gives the idea of artificial whistle-blowing or robot whistle-blowing. The concept is that a machine should conceive and report relevant wrongdoing avoiding the traditional model of whistle-blowing where the employee is the person who should report. This concept, yet unexplored, presents interesting positive and negative aspects. The purpose of this contribution is to present the idea of artificial whistle-blowing and its advantages and disadvantages for the business sector. As a conclusion, this paper suggests that the concept of artificial whistle-blowing needs still to be researched and an optimal solution, for the time being, is to permit artificial whistle-blowing as a helping tool for the employees to detect wrongdoings but report them themselves.

The present paper offers a critique of the General Data Protection Regulation in the realm of access to information. Even though the GDPR supports the constitutionally obvious position that the right to data protection does not outweigh other equally important rights, the enhanced protection of the right to the protection of personal data leads to the potential neglect of other constitutional rights, such as that of access to information. Data protection and access to information authorities should be established both on an EU, as well as at national level as a single authority. Scientific research must be facilitated through access to a multitude of information. The present article explores the question of data ownership and aims to propose a new system that will enhance access to information. A key tool of our research will be the comparative overview of existing legislative systems and a review of the different approaches in the case-law of independent authorities.

This paper describes an environment for the “sheer curation” of the experimental data of a group of researchers in the fields of biophysics and structural biology. The approach involves embedding data capture and interpretation within researchers' working practices, so that it is automatic and invisible to the researcher. The environment does not capture just the individual datasets generated by an experiment, but the entire workflow that represent the “story” of the experiment, including intermediate files and provenance metadata, so as to support the verification and reproduction of published results. As the curation environment is decoupled from the researchers’ processing environment, the provenance is inferred from a variety of domain-specific contextual information, using software that implements the knowledge and expertise of the researchers. We also present an approach to publishing the data files and their provenance according to linked data principles by using OAI-ORE (Open Archives Initiative Object Reuse and Exchange) and OPMV.