Aim/Purpose Generative artificial intelligence (GAI) tools based on Large Language Models (LLMs) are increasingly used to enrich knowledge, support decision-making, and generate new content. However, as beneficial as these tools may be, they also store vast amounts of information, which might jeopardize users’ privacy and data protection. Thus, users who wish to maximize the benefits of GAI tools must relinquish some of their privacy, a phenomenon known in the literature as the “privacy paradox.” Background This research aims to determine whether users experience privacy concerns when using GAI tools by examining the relationships among privacy concerns, trust in AI operators, and actual usage patterns among students. It considers different theoretical models (Privacy Calculus, TAM, and UTAUT) to deter-mine if perceived benefits outweigh the risks of information disclosure. Methodology To examine this issue, a quantitative pilot study was conducted using closed-ended questionnaires distributed to 121 students in Israel. Data were analyzed using Pearson correlations and a stepwise multivariate linear regression to identify predictors of GAI tool usage. Contribution This study contributes to the body of knowledge by being the first to examine GAI privacy perceptions within Israeli academia. It challenges the universal applicability of the privacy paradox by demonstrating that, in this specific context, trust and proficiency are more decisive than privacy concerns. Findings The findings indicate that AI-related privacy concerns have no significant correlation with the amount of GenAI tool usage. However, higher levels of trust in the operators of GenAI tools were found to positively affect the amount of GenAI tool use and Internet proficiency. Recommendations for Practitioners Operators of GAI tools should prioritize transparency regarding data collection and usage to build user trust, which directly influences adoption rates. Educational institutions should integrate AI literacy programs to increase students’ proficiency, thereby fostering more confident and effective use of AI tools. Recommendation for Researchers Researchers should move beyond simple correlational studies to explore causal links between emotional engagement and data disclosure. It is also recommend-ed to use validated, multidimensional scales to measure privacy concerns rather than broad self-reports. Impact on Society The findings suggest that as users feel safe and knowledgeable about digital risks, their intent to utilize GAI increases, potentially accelerating the integration of AI into work and study processes. Future Research Future studies should employ larger, more diverse samples across multiple institutions to test the generalizability of these findings. Additionally, a mixed-methods approach, including qualitative interviews, could provide a deeper understanding of the privacy paradox.
This systematic literature review paper explores perspectives on the ideal metaverse from user experience, business, and national levels, considering both academic and industry viewpoints. The study examines the metaverse as a sociotechnical imaginary, enabled collectively by virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies. Through a systematic literature review, n=144 records were included and by employing grounded theory for analysis of data, we developed three research models, which can guide researchers in examining the metaverse as a sociotechnical future of information technology. Designers can apply the metaverse user experience maturity model to develop more user-friendly services, while business strategists can use the metaverse business readiness model to assess their firms' current state and prepare for transformation. Additionally, policymakers and policy analysts can utilize the metaverse national competitiveness model to track their countries' competitiveness during this paradigm shift. The synthesis of the results also led to the development of practical assessment tools derived from these models that can guide researchers
Aim/Purpose With the technology of artificial intelligence (AI) improving every day it is important to find ways to harness AI in the software development life cycle (SDLC). This research demonstrates how AI tools were incorporated into an upper division Computer Science course to assist with development of various memory games. Background Since ChatGPT’s release in 2022, other companies have released rival chatbots each competing for a piece of the new market. With the plethora of AI options now available, it is important for a developer to learn to use AI as an assistant within the development of a custom project. Methodology The research presented is a multi-case, cross-analysis of four student researchers in a required, senior level Computer Science course. All students were tasked with collecting mixed-methods data on two AI assistants, throughout design and development a unique memory app; then these four students pooled data and conducted a cross-comparative analysis. To prepare for cross analysis, standardized Likert rankings and thematic categories were developed and consistently used during data collections. AI assistants evaluated: Claude, Copilot, ChatGPT Free, and ChatGPT Paid. Throughout the development process, each student provided both of their AI assistants with the same initial queries, the results of which were given a Likert ranking and notes were kept regarding AI accuracy. Individual datasets were examined, then pooled and the combined dataset was used to finalize hypothesis findings. The four student-researchers presented their multi-case, mixed-methods analysis as a snap-shot in time regarding the value of AI as assistants in the development of their projects. Contribution This paper builds on prior research focusing both on student experience and instructional methods in capstone-like courses. This study examines using AIs as assistants as a current trend in Computer Science education. Findings During multi-case analysis, two hypotheses were analyzed against the data of the four student-researchers. The cross examination of data found no statistical significance between the helpfulness of paid vs. free AI as course project assistants; while non-IDE AI assistants performed significantly better than IDE assistants across 7 out of 8 usage type categories. Recommendations for Practitioners Technology instructors can use this research to incorporate AI assistants into advanced courses that focus on building custom software, with cautions that foundational coding skills and knowledge should be in place prior to attempting complex projects. Companies that are researching how AI can be integrated into the software development process can use this research to see preferred strengths of various AI’s, with cautions for use with proprietary data. Recommendations for Researchers Researchers can observe how different AI’s can assist with application development. Further research is encouraged as AI capabilities will continue to evolve. Impact on Society The researchers’ findings show AI in light of its current abilities and limitations in the software development life cycle. While AI assistants excelled in simple to medium complexity debugging tasks, there were many complex tasks where a human coder was preferred over the AI assistants; however, this is expected to change over time. Future Research As future technology strengthens AI some aspects of the study may become historical; however, the core of the research, that of using AI as assistants in development of software projects is expected to remain pertinent to education for some time.
Aim/Purpose To explore the potential of Federated Machine Learning (FML) in developing predictive models while ensuring data privacy and security. Background The rise of data-driven technologies has led to an increased focus on privacy concerns associated with centralized data storage. FML offers a decentralized approach, allowing organizations to collaboratively train models without sharing sensitive data (McMahan et al., 2017). Methodology This study employs a FML framework, utilizing local model training on decentralized datasets, followed by aggregation of model updates to create a global model. Privacy-preserving techniques, such as differential privacy, are also implemented (Dwork & Roth, 2014). Contribution This research contributes to the field of machine learning by demonstrating the efficacy of FML in predictive modeling, highlighting its potential for secure and privacy-conscious applications. Findings The study indicates that FML can effectively enhance model performance while maintaining the privacy of individual data sources. Recommendations for Practitioners Practitioners are encouraged to adopt FML techniques in applications requiring high data security, particularly in sectors such as healthcare and finance. Recommendation for Researchers Future research should explore advanced aggregation methods and evaluate the scalability of FML in diverse settings. Impact on Society The findings of this research have implications for the broader application of machine learning in sensitive areas, promoting data privacy while harnessing the power of collaborative intelligence. Future Research Further investigations should focus on the robustness of FML against adversarial attacks and its applicability in real-world scenarios.
Aim/Purpose This paper explores how large incumbent organizations adopt the newly pro-posed data management approach “Data Mesh”. Particularly, this paper explores to which extent data ownership and data governance are shifting from a centralized to a decentralized approach and whether companies take different paths in this transition. Background Large incumbent organizations suffer from high complexity and often centralized infrastructure of data management that lead not only to high operating costs but also slow down innovation projects and make them more expensive. As a managerial complement of decentralized data management technology such as Data Fabric, Data Mesh was introduced as a management approach to address the organizational challenges of centralization and complexity. Methodology We studied how ten large Swiss incumbent organizations adapted Data Mesh. We interviewed positions such as chief information officers, chief data officers, head of information management, head of group IT. We developed a conceptual framework to position their data management approaches and how much they decentralized data governance and data ownership. Findings and Contribution All large incumbent companies adopt Data Mesh principles, but in different ways and to a different extent. While data governance continues to be increasingly centralized, the degree of data ownership centralization mostly remains unchanged, according to the degree of regulation and other contextual factors. Recommendations for Practitioners Data Mesh is regarded as beneficial by many organizations and thus a relevant option for corporate data management. But there is no “silver bullet” adoption process – instead the degree and way of adoption depends on a certain number of contextual factors, and several reference adoption models exist. Recommendations for Researchers and Future Research The study proposes several hypotheses on Data Mesh adoption that need to be validated in other geographies, other types of organizations (non-incumbents, government, small companies) to better understand adoption paths, adoption barriers, and adoption economies.
Aim/Purpose This study examines strategies to support the need to enhance computational thinking and professional performance across disciplines through the integration of writing across the curriculum (WAC) in graduate and doctoral programs. Background This study explores how combining rhetorical theory and computational thinking can improve students’ communication skills, critical thinking, and problem-solving abilities, preparing them for real world application and the complexities of modern global challenges. Methodology Qualitative research was used to gather the lived experiences of higher education instructors Contribution This research contributes to a deeper understanding of how WAC can be effectively integrated into graduate and doctoral programs, enhancing scholarly communication and professional performance. Findings Discipline-specific writing skills, critical thinking, information literacy, and proficiency in writing assist with student success in various industry sectors. Recommendations for Practitioners Integrate WAC strategies across disciplines in graduate and doctoral programs. Focus on developing discipline-specific writing skills tailored to real world application. Recommendations for Researchers Conduct further studies on the specific application of WAC within graduate and doctoral programs in additional fields. Explore innovative methods for assessing and evaluating writing skills across disciplines. Impact on Society By improving the quality of scholarly communication and professional performance, this approach can lead to more effective transdisciplinary collaborations, better prepared graduates, and ultimately contribute to addressing complex global challenges. Future Research Longitudinal studies tracking the impact of WAC integration on career outcomes. Comparative analyses of WAC implementation across different cultural and educational contexts. Exploring the role of emerging technologies in enhancing WAC strategies and computational thinking skills.
Aim/Purpose Given the complex and ill-structured nature of modelling problems, database education can benefit from learning approaches such as inquiry-based learning (IBL), where students are encouraged to work collaboratively on modelling, design, and querying tasks. IBL can be embedded into teaching approaches such as pair programming, which is known for its many benefits, for example, improved student collaboration, enhanced student involvement, and deep learning. Background Data modelling, design, and SQL are crucial parts of a database course in computing, information systems (IS), IT, and software engineering curricula. Students must analyse and model activities at high levels of abstraction before moving to the design, implementation, and data manipulation phases. This research examines the impact of using IBL via a paired assessment in combination with existing teaching methods as an effective learning technique in the practical database project assessment. Methodology This study implements IBL via a paired assessment strategy in a large undergraduate Database System Design course. Students were assessed in pairs to see if they could effectively complete the assessment in collaboration with their partners. Qualitative data was collected and analysed to determine if the paired approach improved learning effectiveness and performance. Furthermore, student feedback and perceptions are analysed. Contribution This research enhances the literature on database education and IBL by presenting a paired assessment approach for academics interested in implementing this methodology in their database courses. It demonstrates how paired assessments can facilitate collaboration in database education. The study outlines four key lessons learned and provides guidelines for effectively assigning pairs, monitoring the balanced contributions of student pairs, implementing peer evaluation systems, and enforcing a strict policy on student attendance and engagement. Findings Findings related to student learning show that the paired assessment was an effective learning technique that improved student engagement and learning. Students strongly supported the paired assessment approach, and their overall perceptions were positive. The findings led to the identification of four lessons learned and guidelines for future implementation. Recommendations for Practitioners Educational implications emphasize the challenges of inquiry-based learning through paired learning for assessment, such as guidelines for assigning pairs and monitoring students’ balanced contributions. They also highlight the benefits, including the development of soft skills. Recommendations for Researchers This study offers guidelines and recommendations for implementing IBL using paired assessment in database education. Researchers can explore the application of pair-based assessment in online and hybrid database education. Impact on Society Academic faculty that aims to enhance student learning in the complex and ill-structured nature of data modelling and database design, as well as in teamwork and collaboration, will benefit their students, the workforce, and society. Future Research Future research could adapt the proposed methodology in various contexts. Additionally, its impact on the online environment warrants further investigation.
Aim/Purpose This paper investigates the challenges Hispanic graduate students face in online education, focusing on disparities in satisfaction and academic outcomes. It aims to identify and implement culturally responsive teaching strategies and community-building practices that promote equity and success. Background The study addresses the problem by exploring how tailored, culturally grounded instructional strategies and virtual community practices can mitigate feelings of isolation, improve engagement, and foster academic achievement among Hispanic students. Methodology Using a qualitative, autoethnographic approach, the researcher analyzed data from 122 Hispanic graduate students enrolled in online courses. Data sources included Student Ratings of Instruction (SRIs), reflective journals, and informal feedback, complemented by descriptive statistics. Contribution The study contributes to the literature by identifying effective, scalable, culturally responsive teaching practices that support Hispanic students in online learning environments. It offers practical insights grounded in student voices and integrated theoretical frameworks. Findings The findings of this paper are the following. • Personalized feedback increases emotional connection and motivation • Culturally relevant curricula enhance student satisfaction and engagement • Peer support networks reduce isolation and build community • Students reported growth in confidence, academic goals, and creativity Recommendations for Practitioners Implement culturally affirming feedback, include cultural content in the curriculum, create virtual community spaces, address language barriers through multimodal tools, and establish intentional peer mentorship programs. Recommendation for Researchers Expand research to other cultural and linguistic groups using mixed-methods and comparative designs. Examine how strategies perform across different modalities and educational levels. Impact on Society These practices help close equity gaps in higher education, enhancing institutional inclusivity and promoting diverse student success. They also support international and underserved populations facing similar challenges. Future Research Investigate the adaptability of these strategies across varied cultural groups, educational settings, and course formats. Further studies should explore undergraduate, doctoral, and international student populations.
Aim/Purpose This study explores how generative AI is being integrated into unified communications (UC) platforms, focusing specifically on Microsoft Copilot as implemented in Microsoft Teams. It explores how generative AI enhances UC functionalities, identifies key adoption challenges, and provides insights into implementation strategies. Unlike traditional technologies that followed a gradual adoption curve, Copilot’s integration into Teams has the potential to accelerate its adoption, necessitating organizations to be proactive in their planning for its use. Background UC platforms have transformed enterprise communication by integrating multiple tools into a single interface. The integration of generative AI into UC introduces automation of complex routine and time-intensive tasks, enhanced decision support, and workflow optimization. However, adoption dynamics, user experiences, and long-term organizational impacts remain underexplored. Methodology This study employs a meta-analytic approach, synthesizing findings from peer-reviewed articles, conference proceedings, and industry reports. The analysis categorizes user perceptions of AI usefulness, key adoption barriers, and best practices for integration. Contribution This study evaluates the emerging literature on generative AI in UC platforms, focusing on initial user impressions and adoption challenges. Given the technology’s early stage, the findings provide preliminary insights to help organizations plan for effective AI integration in UC environments. Findings The findings indicate that generative AI in UC platforms enhances productivity, streamlines workflows, and improves decision support through features such as meeting summarization, transcription, and AI-driven content generation. However, adoption challenges, including resistance to change, data privacy concerns, and integration complexities, remain key barriers. Recommendations for Practitioners Preliminary findings indicate that users recognize the value of UC platforms integrated with generative AI and anticipate increasing benefits over time. However, successful adoption requires strategic planning to address implementation challenges and ensure effective deployment. Recommendations for Researchers As AI technologies evolve, further research is needed to assess the long-term impact of generative AI in UC platforms on workplace efficiency, productivity gains, user adaptation, and organizational transformation. Comparative research across industries can provide domain-specific best practices, while investigations into human-AI collaboration should examine the balance between automation and human oversight to optimize AI’s role in workplace communication. Impact on Society The integration of generative AI in UC platforms has far-reaching implications for enterprise communication, workforce collaboration, and digital transformation. AI-driven automation is poised to enhance workplace efficiency, but responsible governance and deployment are crucial for ensuring fair and transparent adoption. Future Research Future research is needed to explore the evolving role of agentic AI and its impact on enterprise workflows and strategic decision-making. Studies should assess its role in reducing cognitive load and enhancing team coordination while also addressing adoption challenges such as ethics, automation reliability, and user trust in autonomous AI systems.
Aim/Purpose Globally, small businesses are experiencing alarmingly high failure rates. The Business Failure Modes and Effects Analysis (BFMEA) framework was developed as a proactive solution to address this challenge, specifically targeting small technology enterprises representing approximately one-third of the small enterprises sector. This research illustrates how targeted practitioner interventions and iterative refinements, guided by the elaborated Action Design Research (eADR) methodology, significantly enhanced the effectiveness of the BFMEA worksheet – evolving it into a structured, practical tool for improving business resilience. Background The BFMEA artifact, presented as a structured worksheet, enables business owners to systematically identify potential failure modes while emphasizing proactive risk identification and prioritization within small businesses or industries. Following its preliminary implementation in select small technology enterprises, the BFMEA framework is positioned for broader adoption across various industry sectors. Methodology The author applied the eADR methodology to evolve the BFMEA theory and the artifact (worksheet). Through the four stages of diagnosis, design, implementation, and evolution, eADR facilitated the iterative refinement of the BFMEA worksheet. Practitioner interventions throughout this process enhanced its effectiveness and expanded its scalability. Contribution Enhancing the resilience of small industries is vital to ensuring regional and global economic stability. This study presents the development and refinement of the BFMEA framework – a scalable, structured tool designed to reduce failure rates among small technology businesses – emphasizing the value of academic-practitioner collaboration in driving sustainable business solutions. Findings This study underscores the value of the eADR methodology in developing innovative, sustainable solutions for small industries through iterative, practitioner-driven artifact design. It also highlights the importance of qualitative analytics and academic-practitioner collaboration in refining the BFMEA framework and enhancing the effectiveness of its worksheet. Recommendations for Practitioners This study highlights the essential role of practitioner involvement in applied research and developing practical business tools. The BFMEA work-sheet empowers small industries to proactively identify and prioritize potential failure modes using the Risk Priority Index (RPI), enabling targeted mitigations that enhance operational resilience and support long-term sustainability. Recommendations for Researchers For applied research to effectively benefit the community, selecting the appropriate research methodology and engaging in relevant businesses, industries, or organizations is essential. The academics-practitioner collaborations ensure the collection of valuable insights that contribute to the refinement and evolution of the solutions. Impact on Society Small industries gain significantly by strengthening their growth, sustainability, and resilience while minimizing failure rates. This study supports global economic stability by offering practical insights and solutions that contribute to the long-term success of small businesses. Future Research Future research will focus on broadening the implementation of BFMEA across a wider spectrum of companies, investigating its potential to enhance small industry funding criteria through engagement with venture capitalists and business angels, and developing an AI-driven BFMEA application that can be universally adopted by businesses.
Aim/Purpose This study investigates how technology-driven nudging can enhance decision-making quality and consistency in the U.S. criminal justice system, addressing declining public trust. Background In today’s digital age, Information Technology (IT) plays a crucial role in improving decision-making in the criminal justice system, especially in the United States. Despite the technological adoption and advancements in the domain, trust in decision-making within the system is decreasing. Methodology The study employs qualitative research through focus groups and grounded theory analysis, as it is an exploratory study. Participants were chosen using a mix of convenience and purposive sampling in multidisciplinary domains such as criminal justice, human-computer interaction, artificial intelligence, law, and behavioral psychology. The participants were experts with more than ten years of experience in their respective domains. Further, grounded theory approach was utilized for data analysis. Contribution The study presented a conceptual framework derived from the insights of the focus group data that addresses fundamental factors which influence the implementation, adoption, and evaluation of the efficacy of technological interventions in the U.S. criminal justice system, by focusing on intervention, outcomes, and contextual factors in a practical and more realistic approach. Findings The study identified benefits of using technology driven nudges to support decision-making in the criminal justice system while identifying considerations for implementing technology-driven nudging interventions. In addition, the study found six contextual factors which affect interventions and outcomes, including voter influence, public perception, environment and resources, decision-making scenarios, and decision-makers’ attitudes. Further, the study proposed a conceptual(theoretical) framework that can be utilized by practitioners and researchers to design, implement and evaluate the technological interventions in criminal justice decision making. Recommendations for Practitioners Practitioners can use the study’s conceptual framework as a guide to design, implement, and evaluate nudging interventions tailored to the criminal justice context. Recommendations for Researchers Researchers can build on the proposed theoretical framework, to initiate research experiments and refining it through empirical testing and expanding it to accommodate a wider range of scenarios within criminal justice and beyond. Impact on Society The findings support the development of guidelines or regulations for the ethical and effective application of digital tools in decision-making within the criminal justice system. Additionally, this study can contribute to rebuilding trust in the criminal justice system by employing technology-driven nudging methods that enhance transparency and accountability. Future Research Future research should empirically validate the nudging framework by assessing long-term societal impacts and explore applications in other domains.
Aim/Purpose To address the gap in students’ effective use of generative AI tools, this paper presents a framework to introduce university students to the principles and practices of prompt engineering – the art and science of crafting precise and purposeful inputs to guide LLMs in generating accurate and useful outputs. This paper aims to equip students with strategies to interact meaningfully with AI chatbots for academic success. Background Generative AI tools, like ChatGPT, are widely adopted in educational settings, yet many students lack the skills to harness their full potential. This paper introduces prompt engineering as a critical competency for students to develop both technical proficiency and critical thinking. Methodology The paper provides a structured framework for teaching prompt engineering in university courses. It draws on existing literature, practical applications, and pedagogical strategies to guide educators in integrating generative AI effectively into their university courses. Contribution This paper contributes to the body of knowledge by presenting a comprehensive framework for teaching prompt engineering. It highlights prompt engineering’s role in enhancing AI literacy and preparing students for technology-driven academic and professional environments. Findings Prompt engineering enhances students’ ability to generate precise and relevant outputs from AI tools by supporting student development of communication strategies tailored to large language models. This guide introduces essential concepts and skills that facilitate effective interaction with AI chatbots. Structured instruction in prompt engineering helps to foster critical thinking, problem-solving, and reflective interaction – key competencies for navigating an AI-driven environment. Additionally, integrating prompt engineering into education improves AI literacy, enabling students to tackle complex tasks and apply AI tools effectively across various disciplines. Recommendations for Practitioners Educators should integrate structured, prompt engineering instruction into their courses, emphasizing its interdisciplinary applications. Scaffolded learning will help students develop competency in applying prompt engineering techniques and strategies. Recommendations for Researchers Future studies should explore the long-term impact of prompt engineering instruction on academic performance and professional readiness. Additionally, research should examine its effectiveness across diverse disciplines. Impact on Society Teaching prompt engineering equips students with essential AI literacy skills, fostering responsible and innovative use of AI in academic, professional, and societal contexts. This contributes to a workforce better prepared for the challenges of the AI era. Future Research Further research should examine the integration of multimodal AI tools alongside prompt engineering to assess how combined approaches can enhance learning outcomes. In addition, studies should investigate the effective-ness of various instructional designs to identify best practices for promoting student engagement and skill development. Exploring discipline-specific and pedagogically meaningful student use cases will also be essential to guiding the thoughtful integration of AI tools across diverse educational contexts.
Aim/Purpose. This study aims to identify the influential factors in the application of Applied Data Science in healthcare. Background. The research examines the historical adoption and use of Applied Data Science in the healthcare industry, addressing the specific question: What are the factors that influence the use of Applied Data Science in healthcare throughout the history of information technology? Methodology. The study was conducted using a systematic literature review methodology following Kitchenham and Charters’ (2007) protocol, which includes planning, conducting, and reporting the review. Contribution. The study contributes to understanding key factors that shape the adoption and application of data science technologies in healthcare. Findings. The study used thematic analysis and identified six factors that influenced the application of Applied Data Science in healthcare: deep learning, data organization, medical or healthcare applications, techniques used, data and computing infrastructure, and language systems. Recommendation for Practitioners. The study recommends that the application of data science should adapt in response to advancements in technologies and computing infrastructure. It further suggests that recent developments in these areas may drive a significant transformation in the field. Recommendation for Researchers. The study recommends further examination of the evolution of applied data science in healthcare, especially from 1975 to 1990, when limited primary studies were available. Impact on Society. The results highlight the importance of technological infrastructure, advanced learning models, organized datasets, and ethical considerations for effective data science integration in healthcare. Future Research. Future studies should explore the timeline more, particularly in the time frame of 1975 to 1990, when a gap in the literature was found. Studies could also investigate regional differences, especially focusing on how differences in incomes and GDP affect healthcare data. Additionally, further research should expand beyond the ACM Digital Library to create a more comprehensive view of the field.
Aim/Purpose: The research examines the main factors that motivate users to provide falsified details upon website registration and identifies the types of personal details that are most prone to falsification. In addition, the tendency for identity falsification is predicted by examining various factors, such as, sense of online anonymity, privacy concern, and socio-demographic factors. To provide a contemporaneous dimension to the research, those issues are investigated in relation to the COVID-19 pandemic and examine its influence on privacy concerns and the willingness to expose personal details. Background: Many users choose to deliberately falsify their details during online activities. Methodology: To assess this claim, a user study was carried out among 245 students of the Israeli academia, comprising 52.2% men and 47.8% women, with ages ranging from 18 to 60 years. The research applied a quantitative method using online closed-ended questionnaires. The results were analyzed using conventional statistical methods, such as t-tests and ANOVA. To predict the tendency of identity falsification upon website registration, a logistic regression analysis was performed, taking into account various independent variables: sense of anonymity on websites; sense of exposure to other users online; privacy concern; Internet proficiency; various demographic factors: gender, age, and education. Findings: The research findings suggest that privacy-related issues are the most prevalent for identity falsification. In addition, logistic regression showed that the higher the privacy concerns rates, the higher the chance for identity falsification.
Aim/Purpose This paper seeks to unearth the benefits of deep fake technology and its potential for application to pursue unethical intentions on social media, thereby negatively impacting individuals and society’s well-being. Background The research paper addresses the problem by exploring the ethical implications of deep fake technology and fake news on social media. Through the analysis of the impact on trust, privacy, and democracy, regulatory and ac-countability recommendations are made. Methodology Through a systematic literature review and thematic data analysis, this paper presents interesting ethical issues around deep fake technology and fake news on social media. Contribution This study contributes to the debate around artificial intelligence, social media, and the associated regulatory environment by offering insights into deep fake technology’s social, political, and psychological consequences. Findings The study finds an urgent need to design and implement a strong regulatory framework for both content creators and social media platforms to curb the spread of harmful content and protect individuals’ rights. Recommendations for Practitioners The study recommends robust content moderation, stronger regulatory frameworks, media literacy, and awareness campaigns to citizens to improve their ability to assess social media content’s authenticity. Recommendations for Researchers Researchers are encouraged to take an interdisciplinary approach that includes law, ethics, information systems, psychology, and media studies to address the challenges brought by deep fake technology. Impact on Society The paper impacts society by advancing the comprehension of the potential impacts of digital manipulation. Future Research Scholars may conduct longitudinal studies to determine the long-term psychological and social effects of individuals’ exposure to deep fakes on social media.
Aim/Purpose Institutions with a commitment to diversity, equity, and inclusion (DEI) must evaluate their praxes for equity, recognizing that campuses must be inclusive communities that celebrate diversity. Further, teaching and learning experiences should provide mirrors, windows, and doors, have cultural validity, afford multiple mechanisms for student success, be centered around the assets of students, build knowledge, extend perspectives, and foster empathy. Background A state university system, located in the mid-Atlantic region of the United States, has a strategic plan that prioritizes diversity, equity, and inclusion with goals that include the conduct of research on DEI, promoting best practices to enhance inclusion and endorse equity, and nurturing DEI education that encourages students to be informed and engaged citizens and social change agents in our democracy. A minority-serving institution located in this system has also prioritized justice, equity, diversity, and inclusion (JEDI) with activities that include evaluating and assessing current programming and services; introducing a JEDI institutional learning goal and supporting general education requirement; using surveys to measure faculty, student, and staff perceptions; and exploring culturally responsive practices throughout teaching and learning. Accordingly, in 2024, a quality improvement project was proposed that involves the development, delivery, and reporting of a comprehensive JEDI needs assessment of the community using a mixed methods approach. Methodology This paper outlines the process by which a mid-Atlantic HBCU has engaged in a comprehensive JEDI needs assessment. More specifically, thirteen specific steps are discussed: 1. Identification of goals 2. Establishment of research questions 3. Review of literature and existing tools 4. Consultation with experts 5. Identification of Methodology 6. Preparation of draft instruments 7. Check of readability and face validity 8. Expert panel review 9. Institutional Review Board review and approval 10. Pilot study 11. Distribution and data collection 12. Analysis and reporting of findings 13. Internal validity testing and use of results Contribution The instruments prepared as a result of this endeavor are shared in this paper and are available for adoption and customization with approval and attribution. Findings The surveys were closed in December of 2024. Internal validity testing was conducted via the application of Cronbach’s alpha. The Cronbach’s alpha analysis results indicated high internal consistency and reliability. Recommendations for Researchers It is hoped that by sharing processes and instrumentation, more institutions will decide to engage in similar comprehensive climate studies. Impact on Society It is the goal of the authors that this paper contributes to the body of literature on DEI in higher education. Future Research The survey results will be reported and included in a future paper, and the information gathered will be used to inform qualitative assessment measures.
Aim/Purpose This study investigates the reliability of peer assessments for information systems (IS) case study presentations and examines differences between American and Chinese graduate students in evaluating such presentations. Background Peer assessments provide diverse perspectives in evaluating student work, but cultural differences may influence assessment patterns. This study explores how American and Chinese students differ in their assessment of IS case study presentations using real-world cases. Methodology Data were collected from 89 graduate students across four course sections - three comprising 54 American students and one with 35 Chinese students. Peer assessments were analyzed based on three constructs: organization, content, and communication. Statistical methods, including reliability tests and F-tests, were used to assess the constructs and compare the two groups. Contribution This study contributes to the understanding of peer assessment reliability and highlights cultural differences in assessment practices, particularly in IS education. It expands the empirical literature on using real-world cases and peer assessments in IS courses. Findings Peer assessments were found to be a moderately reliable method for evaluating IS case study presentations. While no significant differences emerged between American and Chinese students in the organization and content constructs, a notable cultural difference was observed in the communication construct. American students excelled in communication and displayed a tendency toward greater leniency in their assessments compared to their Chinese counterparts. These findings highlight the importance of considering cultural dynamics when designing and interpreting peer assessments in educational contexts. Recommendations for Practitioners Educators should design culturally inclusive peer assessment systems and offer training for students to provide constructive and unbiased feedback. Greater emphasis should be placed on enhancing students’ communication skills. Recommendations for Researchers Future studies should investigate how cultural contexts shape students’ assessment criteria and explore methods to mitigate biases in peer assessments across diverse student populations. Impact on Society The findings encourage equitable educational practices, fostering better intercultural understanding and collaboration in globalized learning environments. This approach improves the reliability of student assessments and enhances the quality of education. Future Research Further research should explore how peer assessments evolve over time with training and assess their application in interdisciplinary or multinational courses. Studies could also investigate other cultural dimensions influencing peer assessment practices. Keywords peer assessment, information systems (IS) education, cultural differences, real-world cases
Aim/Purpose The proliferation of online banking has led to a significant increase in identity theft and cybercrimes, creating an urgent need to develop more effective predictive models to detect and prevent such fraudulent activities using advanced technological approaches. Background This study addresses the critical gap in the existing literature by exploring how historical identity theft victim data can be leveraged through supervised ma-chine learning to create a comprehensive prediction model for identifying and preventing future identity theft incidents. Methodology A design science quantitative-focused research approach was employed, analyzing public records of identity theft victims in the United States from 2016. The study utilized a nonexperimental research design to compare and group data sets by number of victims and types of identity theft, applying an onto-logical research philosophy to examine the potential of supervised machine learning in prediction. Contribution The research contributes to the body of knowledge by demonstrating a novel approach to identity theft prevention through data-driven predictive modeling, bridging the gap between historical victim data and machine learning technologies. Findings The study confirmed that supervised machine learning can be effectively used to create an identity theft prediction model using historical victim data, providing insights into potential detection and prevention strategies for cybercrime. Recommendations for Practitioners (1) Implement advanced machine learning models for early identity theft detection. (2) Develop comprehensive data collection strategies for historical victim information. (3) Integrate predictive analytics into existing fraud prevention systems. Recommendations for Researchers (1) Expand the dataset to include more recent and diverse identity theft incidents. (2) Develop more sophisticated machine learning algorithms. (3) Investigate cross-border identity theft patterns. (4) Explore the integration of multiple data sources for improved prediction accuracy. Impact on Society The research offers a potential breakthrough in combating identity theft, potentially reducing financial losses, protecting individual privacy, and enhancing overall cybersecurity for online banking and financial transactions. Future Research (1) Developing real-time prediction models. (2) Exploring artificial intelligence techniques for more dynamic fraud detection. (3) Investigating the psychological and social factors contributing to identity theft.
Aim/Purpose . The present study aimed to understand in depth the experience of identity formation of beginning teachers (BTs), members of Gen Y, in their first year at elementary school, teaching students of Gen Alpha, from the perspective of BTs and their teacher mentors (TMs). Background. The purpose of the study was to compare the aspects described by BTs and their mentor teachers of the initial experience of teaching and of shaping the professional identity of BTs, members of Gen Y, in elementary schools, from the perspective of BTs and teacher mentors (TMs). Methodology. This was a qualitative study. Two groups participated in the study: (a) 75 BTs, members of Gen Y, and (b) 40 mentors of beginning teachers. Contribution. The findings of this study indicate that the creation of an emotionally and professionally supportive community led to a fruitful discussion on issues related to the process of absorption and integration of BTs in the school. This process advanced their professional development, expanding knowledge, abilities, strategies, and innovative pedagogical practices for classroom management, and meaningful teaching and learning in the classroom. The supportive community provided an emotional, professional, social-organizational, and evaluative-reflective response to the needs of BTs, facilitating meaningful interactions between the BTs and their students. It created for students a space for emotional training, organizing and managing behavior, regulating emotions and behavior, reducing feelings of anger, and arousing a feeling of optimism. Findings. The findings show that there was a conflict between the BTs’ and TMs’ perceptions of school reality. The mentors expected the BTs to adapt to the existing system, whereas the BTs perceived the process as one of formation of their identity as teachers. It turned out that parameters that were important to Gen Y teachers, such as knowing the school organization and being an influential factor that brings about change, were less important to their mentors. The findings of the present study reinforce those of previous studies that investigated the employment characteristics of Gen Y. Recommendations for Practitioners. A supportive community at school is likely to increase the level of mental well-being of Gen Y teachers. To this end, support communities of teachers by form and by discipline of study should be created. In the community, emphasis should be placed on reflection and mental resilience in all situations and challenging events that happen to the BTs to help them cope with the accumulated stress. Recommendations for Researchers. Students need a sensitive environment that is appropriate for Gen Alpha children. This environment must allow for emotional training and regulation, behavior organization and management to arouse a feeling of optimism and reduce anger. To develop students’ emotional, social, and cognitive abilities, teachers must teach with love, sensitivity, affectivity, and empathy. Impact on Society. To retain BTs and prevent them from quitting their career, schools must ensure that members of Gen Y understand the school organization and are satisfied with the way the organization is managed. They must have a sense of being significant partners in the life of the school. Under optimal working conditions, Gen Y teachers may greatly contribute to the values of education and equal opportunity, maximizing the personal potential of each student and the classroom as a whole, and making the school relevant. Future Research. Future studies should examine the characteristics of students belonging to Gen Alpha. One of the difficulties mentioned by BTs was a misunderstanding of the characteristics of Gen Alpha, which created problems in the interactions within the teaching staff and between the teachers and the students, and pre-vented gaining authority with other teachers and with students.
