Decision-making involves choosing between one ore more alternatives, to achieve one or more goals. To support this process, there are decision support systems that employ different approaches, supporting groups or not. Generally, however, these systems do not have great flexibility; their users have to follow preestablished decision methods. This paper, after exposing some decision-making processes, describes a system, LaSca (from Large Scale), to support decisions in large-scale groups. This system, besides allowing effective achievement of the benefits of deciding in large groups through the proper structuring of the group, also allows its users to define themselves how this structuring will happen, based or not in the existing theories on the subject. So, in addition to facilitate the decision-making process, LaSca also allows its users to decide how to decide.

1. Hiking with a backpack is the workout of 2024. An exercise scientist says it’s worth the extra effort  The Globe and Mail
2. Military-Inspired Workout Has 'Huge Wins' for Women, Says Personal Trainer  MSN
3. How Rucking Can Turn Your Walks into a Full-Body Workout  Verywell Health
4. What Is Rucking and Is It Better Than Regular Walking? Here's What Personal Trainers Say  EatingWell
5. Rucking: Why It’s a Great Workout & How to Get Started  Athletech News

(Belga) Le conseil communal de la Ville de Charleroi a adopté lundi une motion faisant de Charleroi "une ville antifasciste" et consacrant l'existence d'une "coalition antifasciste" composée des partis politiques carolos, des syndicats, d'associations et de membres de la société civile.

Cette "coalition antifasciste" est le fruit de discussions entamées dans un contexte de montée générale des idées d'extrême droite et à la suite des incidents qui sont survenus le 25 janvier 2020 à Charleroi à l'occasion d'une mobilisation d'un front antifasciste contre la tenue dans la métropole d'une réunion d'un nouveau parti d'extrême droite. Ce jour-là, selon les manifestants antifascistes, la police avait fait usage contre eux de sprays, d'autopompes et de coups de matraques même pour les disperser. Ce qui avait provoqué un certain émoi, y compris au sein de la classe politique carolo. La motion donne à la coalition antifasciste quelques objectifs généraux, comme celui "d'empêcher par tous les moyens légaux la diffusion de propos incitant à la haine, au racisme, à l'antisémitisme, au sexisme, à la discrimination relative à l'orientation sexuelle, ouvertement fasciste et xénophobe, sur le territoire de Charleroi" ou celui de relayer l'information "lorsqu'elle concerne un événement susceptible d'inciter à la haine, au racisme, à l'antisémitisme, au sexisme, ouvertement fasciste et xénophobe". (Belga)

The utilisation of big data presents significant opportunities for businesses to create value and gain a competitive edge. This capability enables firms to anticipate and uncover information quickly and intelligently. The author introduces a human resource scheduling optimisation strategy using a parallel network fusion structure model. The author's approach involves designing a set of network structures based on parallel networks and streaming media, enabling the macro implementation of the enterprise parallel network fusion structure. Furthermore, the author proposes a human resource scheduling optimisation method based on a parallel deep learning network fusion structure. It combines convolutional neural networks and transformer networks to fuse streaming media features, thereby achieving comprehensive identification of the effectiveness of the current human resource scheduling in enterprises. The result shows that the macro and deep learning methods achieve a recognition rate of 87.53%, making it feasible to assess the current state of human resource scheduling in enterprises.

Aim/Purpose: This paper provides a review of previously published work related to active learning in information systems (IS) courses. Background: There are a rising number of strategies in higher education that offer promise in regards to getting students’ attention and helping them learn, such as flipped classrooms and offering courses online. These learning strategies are part of the pedagogical technique known as active learning. Active learning is a strategy that became popular in the early 1990s and has proven itself as a valid tool for helping students to be engaged with learning. Methodology: This work follows a systematic method for identifying and coding previous research based on an aspect of interest. The authors identified and assessed research through a search of ABI/Inform scholarly journal abstracts and keywords, as well as additional research databases, using the search terms “active learning” and “information systems” from 2000 through June 2016. Contribution: This synthesis of active learning exercises provides guidance for information technology faculty looking to implement active learning strategies in their classroom by demonstrating how IS faculty might begin to introduce more active learning techniques in their teaching as well as by presenting a sample teaching agenda for a class that uses a mix of active and passive learning techniques to engage student learning. Findings: Twenty successful types of active learning exercises in IS courses are presented. Recommendations for Practitioners : This paper offers a “how to” resource of successful active learning strategies for IS faculty interested in implementing active learning in the classroom. Recommendation for Researchers: This work provides an example of a systematic literature review as a means to assess successful implementations of active learning in IS. Impact on Society: An updated definition of active learning is presented as well as a meaningful list of exercises that encourage active learning both inside and outside of the IS classroom. Future Research: In relation to future research, this study highlights a number of opportunities for IS faculty in regards to new active learning activities or trends to study further.

Aim/Purpose: This paper is part of a multi-case study that aims to test whether generative AI makes an effective coding assistant. Particularly, this work evaluates the ability of two AI chatbots (ChatGPT and Bing Chat) to generate concise computer code, considers ethical issues related to generative AI, and offers suggestions for how to improve the technology. Background: Since the release of ChatGPT in 2022, generative artificial intelligence has steadily gained wide use in software development. However, there is conflicting information on the extent to which AI helps developers be more productive in the long term. Also, whether using generated code violates copyright restrictions is a matter of debate. Methodology: ChatGPT and Bing Chat were asked the same question, their responses were recorded, and the percentage of each chatbot’s code that was extraneous was calculated. Also examined were qualitative factors, such as how often the generated code required modifications before it would run. Contribution: This paper adds to the limited body of research on how effective generative AI is at aiding software developers and how to practically address its shortcomings. Findings: Results of AI testing observed that 0.7% of lines and 1.4% of characters in ChatGPT’s responses were extraneous, while 0.7% of lines and 1.1% of characters in Bing Chat’s responses were extraneous. This was well below the 2% threshold, meaning both chatbots can generate concise code. However, code from both chatbots frequently had to be modified before it would work; ChatGPT’s code needed major modifications 30% of the time and minor ones 50% of the time, while Bing Chat’s code needed major modifications 10% of the time and minor ones 70% of the time. Recommendations for Practitioners: Companies building generative AI solutions are encouraged to use this study’s findings to improve their models, specifically by decreasing error rates, adding more training data for programming languages with less public documentation, and implementing a mechanism that checks code for syntactical errors. Developers can use the findings to increase their productivity, learning how to reap generative AI’s full potential while being aware of its limitations. Recommendation for Researchers: Researchers are encouraged to continue where this paper left off, exploring more programming languages and prompting styles than the scope of this study allowed. Impact on Society: As artificial intelligence touches more areas of society than ever, it is crucial to make AI models as accurate and dependable as possible. If practitioners and researchers use the findings of this paper to improve coders’ experience with generative AI, it will make millions of developers more productive, saving their companies money and time. Future Research: The results of this study can be strengthened (or refuted) by a future study with a large, diverse dataset that more fully represents the programming languages and prompting styles developers tend to use. Moreover, further research can examine the reasons generative AI fails to deliver working code, which will yield valuable insights into improving these models.

Human behaviour, which is led by the human, emotional and occasionally fallible brain, is highly influenced by the environment in which choices are presented. This research paper explores the synergistic potential of the Internet of Behaviours (IoB) and digital nudges in the financial sector as new avenues for intervention while shedding light on the IoB benefits and the digital nudges' added value in these financial settings. Afterward, it proposes an IoB-Nudges conceptual model to explain how these two concepts would be incorporated and investigates their complementary relationship and benefits for this sector. Finally, the paper also discusses key challenges to be addressed by the IoB framework.

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 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.

In order to overcome the problems of poor evaluation accuracy and long evaluation time in traditional customer relationship evaluation methods, this study proposes a new customer relationship evaluation method for e-commerce platform based on decision tree algorithm. Firstly, analyse the connotation and characteristics of customer relationship; secondly, the importance of customer relationship in e-commerce platform is determined by using decision tree algorithm by selecting and dividing attributes according to the information gain results. Finally, the decision tree algorithm is used to design the classifier, the weighted sampling method is used to obtain the training samples of the base classifier, and the multi-period excess income method is used to construct the customer relationship evaluation function to achieve customer relationship evaluation. The experimental results show that the accuracy of the customer relationship evaluation results of this method is 99.8%, and the evaluation time is only 51 minutes.

In this article, we have attempted to study the ways in which the COVID-19 pandemic has gradually increased and impacted the world. The authors integrate the knowledge from cognitive psychology literature to illustrate how the limitations of the human mind might have a critical role in the decisions taken during the COVID-19 pandemic. The authors show the correlation between different biases in various contexts involved in the COVID-19 pandemic and highlight the ways in which we can nudge ourselves and various stakeholders involved in the decision-making process. This study uses a typology of biases to examine how different patterns of biases affect the decision-making behaviour of people during the pandemic. The presented model investigates the potential interrelations among environmental transformations, cognitive biases, and strategic decisions. By referring to cognitive biases, our model also helps to understand why the same performance improvement practices might incite different opinions among decision-makers.

Numerous Internet of Things (IoT) applications require brain-empowered intelligence. This necessity has caused the emergence of a new area called cognitive IoT (CIoT). Reasoning, planning, and selection are typically involved in decision-making within the network bandwidth limit. Consequently, data minimisation is needed. Therefore, this research proposes a novel technique to extract conscious data from a massive dataset. First, it groups the data using k-means clustering, and the entropy is computed for each cluster. The most prominent cluster is then determined by selecting the cluster with the highest entropy. Subsequently, it transforms each cluster element into an informative element. The most informative data is chosen from the most prominent cluster that represents the whole massive data, which is further used for intelligent decision-making. The experimental evaluation is conducted on the 21.25 years of environmental dataset, revealing that the proposed method is efficient over competing approaches.

This paper describes the employment of a Personal Response System (PRS) during a Decision Analysis course for Management Information Systems (MIS) students. The description shows how the carefully designed PRS-based questions, the delivery, and the follow-up discussions; provided a context for eliciting and exercising central concepts of the course topics as well as central skills required for MIS majors. A sample of PRS-based questions is presented along with a description for each question of its purpose, the way it was delivered, the response rate, the responses and their frequencies, and the respective in-class discussion. Lessons from these findings are discussed.

Aim/Purpose: This paper describes and information system for the maintenance and management of municipal lighting systems that also serves as a decision support tool for reducing power consumption on urban lighting. Background: Many municipalities are financially constrained and unable to invest in improving their lighting infrastructure. We propose a very efficient and inexpensive way to set up the database and provide city leaders with tools to improve their system efficiently. Methodology: An information database for the data management and an Integer Programming model for deriving the optimal investment plan. Contribution: This paper contributes to the fields of urban economics and sustainability. Findings: Informing management and workers about the status of the system and how to optimize it will reward the city with considerable savings and improve the service quality. Recommendations for Practitioners: The application of this model, even in a small scale such as a neighborhood can improve citizen’s quality of life without a heavy burden on the city budget. Recommendation for Researchers: There is a growing need for cost-effective means to improve urban management. Innovative ideas that meet these goals should be researched and developed. Impact on Society: First, it allows reduction in carbon emissions and light pollution by reducing power consumption and over-luminous lighting levels. Second, financially constrained municipalities can manage their systems at a very low cost. Future Research: A full scale application is needed in order to evaluate the city-wide benefits of the system.

Aim/Purpose: Our research goal was to examine the factors that motivate women to enroll in Computer Science (CS) courses in order to better understand the small number of women in the field of CS. Background: This work is in line with the growing interest in better understanding the problem of the underrepresentation of women in the field of CS. Methodology: We focused on a college that differs in its high numbers of female CS students. The student population there consists mostly of religious Jews; some of them are Haredi, who, because of their unique lifestyle, are expected to be the breadwinners in their family. Following group interviews with 18 students, a questionnaire was administered to all the female students and 449 of them responded. We analyzed it statistically. We compared the responses of the Haredi and non-Haredi students. Contribution: The main contribution of this work lies in the idea that studying the factors underlying women’s presence in a CS program in unique communities and cultures, where women are equally represented in the field, might shed light on the nature of this phenomenon, especially whether it is universal or confined to the surrounding culture. Findings: There were significant differences between the Haredi and non-Haredi women regarding the importance they attributed to different factors. Haredi women resemble, regarding some social and economic variables, women in developing countries, but differ in others. The non-Haredi women are more akin to Western women, yet they did not completely overlap. Both groups value their family and career as the most important factors in their lives. These factors unify women in the West and in developing countries, though with different outcomes. In the West, it deters women from studying CS, whereas in Israel and in Malaysia, other factors can overcome this barrier. Both groups attributed low importance to the masculine image of CS, found important in the West. Hence, our findings support the hypothesis that women’s participation in the field of CS is culturally dependent. Recommendations for Practitioners: It is important to learn about the culture within which women operate in order to attract more women to CS. Recommendations for Researchers: Future work is required to examine other loci where women are underrepre-sented in CS, as well as how the insights obtained in this study can be utilized to decrease women’s underrepresentation in other loci. Impact on Society: Women's underrepresentation in CS is an important topic for both economic and social justice reasons. It raises questions regarding fairness and equality. In the CS field the gender pay gaps are smaller than in other professional areas. Thus, resolving the underrepresentation of women in CS will serve as a means to decrease the social gender gap in other areas.

This work investigated the suitability of a new tool for decision support in training programs of high performance athletes. The aim of this study was to find a reliable and robust measure of the fitness of an athlete for use as a tool for adjusting training schedules. We examined the use of heart rate recovery percentage (HRr%) for this purpose, using a two-phased approach. Phase 1 consisted of testing the suitability of HRr% as a measure of aerobic fitness, using a modified running test specifically designed for high-performance team running sports such as football. Phase 2 was conducted over a 12-week training program with two different training loads. HRr% measured aerobic fitness and a running time-trial measured performance. Consecutive measures of HRr% during phase 1 indicated a Pearson’s r of 0.92, suggesting a robust measure of aerobic fitness. During phase 2, HRr% reflected the training load and significantly increased when the training load was reduced between weeks 4 to 5. This work shows that HRr% is a robust indicator of aerobic fitness and provides an on-the-spot index that is useful for training load adjustment of elite-performance athletes.

Aim/Purpose: This journal paper seeks to understand historical aspects of data management, leading to the current data issues faced by organizational executives in relation to big data and how best to present the information to circumvent big data challenges for executive strategic decision making. Background: This journal paper seeks to understand what executives value in data visualization, based on the literature published from prior data studies. Methodology: The qualitative methodology was used to understand the sentiments of executives and data analysts using semi-structured interview techniques. Contribution: The preliminary findings can provide practical knowledge for data visualization designers, but can also provide academics with knowledge to reflect on and use, specifically in relation to information systems (IS) that integrate human experience with technology in more valuable and productive ways. Findings: Preliminary results from interviews with executives and data analysts point to the relevance of understanding and effectively presenting the data source and the data journey, using the right data visualization technology to fit the nature of the data, creating an intuitive platform which enables collaboration and newness, the data presenter’s ability to convey the data message and the alignment of the visualization to core the objectives as key criteria to be applied for successful data visualizations Recommendations for Practitioners: Practitioners, specifically data analysts, should consider the results highlighted in the findings and adopt such recommendations when presenting data visualizations. These include data and premise understanding, ensuring alignment to the executive’s objective, possessing the ability to convey messages succinctly and clearly to the audience, having knowledge of the domain to answer questions effectively, and using the right technology to convey the message. Recommendation for Researchers: The importance of human cognitive and sensory processes and its impact in IS development is paramount. More focus can be placed on the psychological factors of technology acceptance. The current TAM model, used to describe use, identifies perceived usefulness and perceived ease-of-use as the primary considerations in technology adoption. However, factors that have been identified that impact on use do not express the importance of cognitive processes in technology adoption. Future Research: Future research requires further focus on intangible and psychological factors that could affect technology adoption and use, as well as understanding data visualization effectiveness in corporate environments, not only predominantly within the Health sector. Lessons from Health sector studies in data visualization should be used as a platform.

Aim/Purpose: This explanatory study aimed to determine the mediating role of ERP in the relation between the effect of a socio-technical approach and decision-making environment, and firms’ sustainable performance. Background: Although earlier studies have discussed the critical success factors of the failure or success of an ERP system and the extent to which it achieves its desired objectives, the current study focused on the significant impact of socio-technical elements and decision-making environment on the success of the ERP system (i.e., sustainable performance). In addition, the lack of research on ERP as a mediator in the above relationship motivated this study to bridge the literature gap. Methodology: The data was collected using questionnaires distributed to 233 randomly selected employees of three multinational companies (BP, LUKOIL, and Eni) operating in Iraq. The structural equation modeling was employed to test the hypothesized relationships. Contribution: The study contributes to the literature by examining the mediating role of the ERP system in the relationship between socio-technical elements and the decision-making environment, as well as, the moderating role of organizational culture in the relationship between socio-technical elements and ERP systems. Findings: The results showed that ERP is a significant mediator between the linkage of socio-technical elements and the decision-making environment while organizational culture has an insignificant moderating role in the relationship between socio-technical elements and ERP systems. Recommendations for Practitioners: In a developing country like Iraq, there is a need to implement ERP to achieve better sustainable performance through change management and organizational development that ultimately work towards enhancing individual capabilities, knowledge, and training. Recommendation for Researchers: The researchers are recommended to conduct an in-depth study of the phenomenon based on theoretical and empirical grounds, particularly in light of the relationship of socio-technical elements and decision-making environments. Impact on Society: This study provides a reference for organizations with similar cultural backgrounds in using ERP systems to minimize pollution in Iraqi context. Future Research: A more in-depth study can be performed using a bigger sample, which not only includes the oil industry but also the other industries.

Aim/Purpose: The first goal is to develop a decision support system for pricing and production amounts for a firm facing high levels of product returns. The second goal is to improve the management of the product returns process. Background: This study was conducted at a food importer and manufacturer in Israel facing a very high rate of product returns, much of which is eventually discarded. The firm’s products are commonly considered to be a low-cost generic alternative and are therefore popular among low-income families. Methodology: A decision support module was added to the plant’s business information system. The module is based on a supply chain pricing model and uses the sales data to infer future demand’s distribution. Ergonomic models were used to improve the design of the returns warehouse and the handling of the returns. Contribution: The decision support system allows to improve the plant’s pricing and quantity planning. Consequently, it reduced the size of product returns. The new design of the returns process is expected to improve worker’s productivity, reduces losses and results in safer outcomes. This study also demonstrates a successful integration and of a theoretical economical model into an information system. Findings: The results show the promise of incorporating pricing supply chain models into informing systems to achieve a practical business task. We were able to construct actual demand distributions from the data and offer actual pricing recommendations that reduce the number of returns while increasing potential profits. We were able to identify key deficiencies in the returns operations and added a module to the decisions support system that improves the returns management and links it with the sales and pricing modules. Finally, we produced a better warehouse design that supports efficient and ergonomic product returns handling. Recommendations for Practitioners: This work can be replicated for different suppliers, manufacturers and retailers that suffer from product returns. They will benefit from the reduction in returns, as well as the decrease in the losses associated with these returns. Recommendation for Researchers: It is worthwhile to research whether decision support systems can be applied to other aspects of the organizations’ operations. Impact on Society: Product returns is a lose-lose situation for producers, retailers and customers. Moreover, mismanagement of these returns is harmful for the environment and may result in the case of foods, in health hazards. Reducing returns and improving the handling improves sustainability and is beneficial for society. Future Research: The decision support system’s underlying pricing model assumes a specific business setting. This can be extended using other pricing models and applying them in a similar fashion to the current application.

Aim/Purpose: This research aims to design a website-based group decision support system (DSS) user interface to support an integrated and sustainable waste management plan in Jagatera. The main focus of this research is to design a group DSS to help Jagatera prioritize several waste alternatives to be managed so that Jagatera can make the right decisions to serve the community. Background: The Indonesian government and various stakeholders are trying to solve the waste problem. Jagatera, as a waste recycling company, plays a role as a stakeholder in managing waste. In 2024, Jagatera plans to accept all waste types, which impacts the possibility of increasing waste management costs. If Jagatera does not have a waste management plan, this will impact reducing waste management services in the community. To solve this problem, the group DSS assists Jagatera in prioritizing waste based on aspects of waste management cost. Methodology: Jagatera, an Indonesian waste recycling company, is implementing a group DSS using the soft system methodology (SSM) method. The SSM process involves seven stages, including problem identification, problem explanation using rich pictures, system design, conceptual model design, real-life comparison, changes, and improvement steps. The final result is a prototype user interface design addressing the relationship between actors and the group DSS. The analytical hierarchy process (AHP) method prioritized waste based on management costs. This research obtained primary data from interviews with Jagatera management, a literature review regarding the group DSS, and questionnaires to determine the type of waste and evaluate user interface design. Contribution: This research focuses on determining waste handling priorities based on their management. It contributes the DSS, which uses a decision-making approach based on management groups developed using the SSM and AHP methods focused on waste management decisions. It also contributes to the availability of a user interface design from the DSS group that explains the interactions between actors. The implications of the availability of DSS groups in waste recycling companies can help management understand waste prioritization problems in a structured manner, increase decision-making efficiency, and impact better-quality waste management. Combining qualitative approaches from SSM to comprehend issues from different actor perspectives and AHP to assist quantitative methods in prioritizing decisions can yield theoretical implications when using the SSM and AHP methods together. Findings: This research produces a website-based group DSS user interface design that can facilitate decision-making using AHP techniques. The user interface design from the DSS group was developed using the SSM approach to identify complex problems at waste recycling companies in Indonesia. This study also evaluated the group DSS user interface design, which resulted in a score of 91.67%. This value means that the user interface design has met user expectations, which include functional, appearance, and comfort needs. These results also show that group DSS can enhance waste recycling companies’ decision-making process. The results of the AHP technique using all waste process information show that furniture waste, according to the CEO, is given more priority, and textile waste, according to the Managing Director. Group DSS developed using the AHP method allows user actors to provide decisions based on their perspectives and authority. Recommendations for Practitioners: This research shows that the availability of a group DSS is one of the digital transformation efforts that waste recycling companies can carry out to support the determination of a sustainable waste management plan. Managers benefit from DSS groups by providing a digital decision-making process to determine which types of waste should be prioritized based on management costs. Timely and complete information in the group DSS is helpful in the decision-making process and increases organizational knowledge based on the chosen strategy. Recommendation for Researchers: Developing a group DSS for waste recycling companies can encourage strategic decision-making processes. This research integrates SSM and AHP to support a comprehensive group DSS because SSM encourages a deeper and more detailed understanding of waste recycling companies with complex problems. At the same time, AHP provides a structured approach for recycling companies to make decisions. The group DSS that will be developed can be used to identify other more relevant criteria, such as environmental impact, waste management regulations, and technological capabilities. Apart from more varied criteria, the group DSS can be encouraged to provide various alternatives such as waste paper, metal, or glass. In addition to evaluating the group DSS’s user interface design, waste recycling companies need to consider training or support for users to increase system adoption. Impact on Society: The waste problem requires the role of various stakeholders, one of which is a waste recycling company. The availability of a group DSS design can guide waste recycling companies in providing efficient and effective services so that they can respond more quickly to the waste management needs of the community. The community also gets transparent information regarding their waste management. The impact of good group DSS is reducing the amount of waste in society. Future Research: Future research could identify various other types of waste used as alternatives in the decision-making process to illustrate the complexity of the prioritization process. Future research could also identify other criteria, such as environmental impact, social aspects of community involvement, or policy compliance. Future research could involve decision-makers from other parties, such as the government, who play an essential role in the waste industry.

Aim/Purpose: This study investigates the relationship between Electronic Word-of-Mouth (EWOM), Information Adoption, and the stock investment of Vietnamese investors. Background: Misinformation spreads online, and a lack of strong information analysis skills can lead Vietnamese investors to make poor stock choices. By understanding how online conversations and information processing influence investment decisions, this research can help investors avoid these pitfalls. Methodology: This study applies Structural Equation Modelling (SEM) to investigate how non-professional investors react to online information and which information factors influence their investment decisions. The final sample includes 512 investors from 18 to 65 years old from various professional backgrounds (including finance, technology, education, etc.). We conducted a combined online and offline survey using a convenience sampling method from August to November 2023. Contribution: This study contributes to the growing literature on Electronic Word-of-Mouth (EWOM) and its impact on investment decisions. While prior research has explored EWOM in various contexts, we focus on Vietnamese investors, which can offer valuable insights into its role within a developing nation’s stock market. Investors, particularly those who are new or less experienced, are often susceptible to the influence of EWOM. By examining EWOM’s influence in Vietnam, this study sheds light on a crucial factor impacting investment behavior in this emerging market. Findings: The results show that EWOM has a moderate impact on the Information Adoption and investment decisions of Vietnamese stock investors. Information Quality (QL) is the factor that has the strongest impact on Information Adoption (IA), followed by Information Credibility (IC) and Attitude Towards Information (AT). Needs for Information (NI) only have a small impact on Information Adoption (IA). Finally, Information Adoption (IA) has a limited influence on investor decisions in stock investment. We also find that investors need to verify information through official sites before making investment decisions based on posts in social media groups. Recommendations for Practitioners: The findings suggest that state management and media agencies need to coordinate to improve the quality of EWOM information to protect investors and promote the healthy development of the stock market. Social media platform managers need to moderate content, remove false information, prioritize displaying authentic information, cooperate with experts, provide complete information, and personalize the experience to enhance investor trust and positive attitude. Securities companies need to provide complete, accurate, and updated information about the market and investment products. They can enhance investor trust and positive attitude by developing news channels, interacting with investors, and providing auxiliary services. Listed companies need to take the initiative to improve the quality of information disclosure and ensure clarity, comprehensibility, and regular updates. Use diverse communication channels and improve corporate governance capacity to increase investor trust and positive attitude. Investors need to seek information from reliable sources, compare information from multiple sources, and carefully check the source and author of the information. They should improve their investment knowledge and skills, consult experts, define investment goals, and build a suitable investment portfolio. Recommendation for Researchers: This study synthesized previous research on EWOM, but there is still a gap in the field of securities because each nation has its laws, regulations, and policies. The relationships between the factors in the model are not yet clear, and there is a need to develop a model with more interactive factors. The research results need to be further verified, and more research can be conducted on the influence of investor psychology, investment experience, etc. Impact on Society: This study finds that online word-of-mouth (EWOM) can influence Vietnamese investors’ stock decisions, but information quality is more important. Policymakers should regulate EWOM accuracy, fund managers should use social media to reach investors, and investors should diversify their information sources. Future Research: This study focuses solely on the stock market, while individual investors in Vietnam may engage in various other investment forms such as gold, real estate, or cryptocurrencies. Therefore, future research could expand the scope to include other investment types to gain a more comprehensive understanding of how individual investors in Vietnam utilize electronic word-of-mouth (EWOM) and adopt information in their investment decision-making process. Furthermore, while these findings may apply to other emerging markets with similar levels of financial literacy as Vietnam, they may not fully extend to countries with higher financial literacy rates. Hence, further studies could be conducted in developed countries to examine the generalizability of these findings. Finally, future research could see how EWOM’s impact changes over a longer period. Additionally, a more nuanced understanding of the information adoption process could be achieved by developing a research model with additional factors.

Aim/Purpose: This study explored teachers’ data-driven decision-making processes during routine and emergency remote teaching, as experienced during the COVID-19 pandemic. Background: Decision-making is essential in teaching, with informed decisions promoting student learning and teachers’ professional development most effectively. However, obstacles to the use of data have been identified in many studies. Methodology: Using a qualitative methodology (N=20), we studied how teachers make decisions, what data is available, and what data they would like to have to improve their decision-making. We used an inductive approach (bottom-up), utilizing teachers’ statements related to decision-making as the unit of analysis. Contribution: Our findings shed an important light on teachers’ Data-Driven Decision-Making (DDDM), highlighting the differences between routine and Emergency Remote Teaching (ERT). Findings: Overall, we found that teachers make teaching decisions in three main areas: pedagogy, discipline-related issues, and appearance and behavior. They shift between making decisions based on data and making decisions based on intuition. Academic-related decisions are the most prominent in routine teaching, and during ERT, they were almost the only area in which teachers’ decisions were made. Teachers reported collecting data about students’ academic achievements and emotional state and considered the organizational culture, consultation with colleagues, and parents’ involvement before decision-making. Recommendations for Practitioners: Promote a culture of data-driven decision-making across the education system; Make diverse and rich data of different types accessible to teachers; Increase professional and emotional support for teachers. Recommendation for Researchers: Researchers have the potential to expand the scope of this study by conducting research using other methodologies and in different countries. Impact on Society: This study highlights the importance of teachers’ data-driven decision-making in improving teaching practices and promoting students’ achievement. Future Research: Additional research is required to examine data-driven decision-making in diverse circumstances.