Southeast Missouri State University

Delivery reliability remains a critical determinant of competitiveness in modern supply chains. Despite technological advancements, many organizations continue to experience delays caused by operational and logistics-related inefficiencies. This study investigates the key operational and transportation factors influencing delivery delays using business intelligence and data-driven analytical techniques applied to secondary supply chain data. The research integrates regression modeling, clustering analysis, and mode-based performance evaluation to examine how lead time components and transportation modes jointly affect delivery reliability. The findings demonstrate that delivery delays emerge from systemic interactions among processing, shipment, and coordination phases rather than from isolated bottlenecks. Furthermore, distinct delivery- performance profiles highlight the importance of risk- based order segmentation and mode-specific logistics strategies. The study contributes to the supply chain literature by proposing an integrated analytical framework that combines business intelligence tools with operational modeling to enhance delivery performance management. The results provide actionable insights for managers seeking to improve reliability, optimize lead-time structures, and implement data-driven decision support systems in global supply chain environments.

Purpose Guided by social identity and social exchange theory, this study offers a conceptual model explaining how employees' perceptions of organizational justice—namely distributive, procedural, and interactional justice—shape their evaluations of firm performance when accounting for the mediating role of organizational identification. The model also introduces service climate as a contextual moderator of the justice–identification relationship. Design/methodology/approach Survey data from 246 full-time employees across diverse industries were analyzed using structural equation modeling to test the proposed hypotheses empirically. Findings Organizational identification partially mediates the link between justice perceptions and perceived firm performance. Service climate positively moderates the effect of procedural justice on identification, whereas its interaction with interactional justice yields a negative effect, contrary to expectations, indicating a substitution dynamic. Additionally, the three justice dimensions exert asymmetric influences on identification, underscoring the need to distinguish their distinct roles in organizational contexts. Originality/value This research advances performance management theory by demonstrating that employees' assessments of firm effectiveness are jointly shaped by their justice perceptions and organizational identification. This integrated framework offers a holistic and context-sensitive lens for understanding performance in contemporary organizations. For practitioners, it underscores the need for managerial strategies that are attuned to employees’ priorities across different justice dimensions and diverse organizational settings.

The 1992 cold case homicide of “Ina Jane Doe” illustrates how an interdisciplinary team worked to identify the decedent using a combined approach of skeletal re-analysis, updated forensic art informed by anthropologists’ input, archival research, and forensic investigative genetic genealogy. The original forensic art for “Ina Jane Doe” showed an over-pathologization of skeletal features and an inaccurate hairstyle; however, the case gained notoriety on internet true crime forums leading to speculation about the decedent’s intellectual capacity and physical appearance. The “Ina Jane Doe” case demonstrates the importance of advocating for skeletal re-analysis as more robust methods and technologies emerge in forensic science, as well as the impact of sustained public interest in cold cases. In this case, continuous public interest and online speculation led to anthropologists constructing a team of experts to correct and revise narratives about the decedent. Forensic anthropologists’ role in cold cases may include offering skeletal re-analysis, recognizing and correcting errors in the original estimations of the biological profile, searching for missing person matches, and/or working collaboratively with subject matter experts in forensic art, odontology and forensic investigative genetic genealogy.

Amid the audience turn in digital journalism studies, one question has been left largely unanswered: What makes for a good news experience? In this paper, we first define news experience as a dynamic and temporal interaction with content as well as a sensemaking activity, one that is situated within the personal, social, and cultural contexts of the user, and influenced by the available technological resources and affordances. We then argue that, despite the increasing application of user- and design-centered approaches in the field, there remains a need for theoretical frameworks that explain what it means to experience news. To stimulate these conversations, we introduce and elaborate on Morville’s Honeycomb model from UX studies. This model, applied to journalism, illustrates how an optimal news experience should be useful, usable, desirable, findable, accessible, credible, and valuable. Ultimately, this research aims to guide both scholars and practitioners toward user-centric approaches in journalism that may contribute to a more sustainable future for news.

The clinical trial continuum requires the use of Clinical Data Management (CDM) to facilitate data quality, consistency and adherence to regulatory standards and requirements. Traditional manual data conflict detection and correction may be inefficient and time consuming, which can easily damage the quality, integrity, and timely locking of the database. This paper presents an automated Electronic Data Capture (EDC) discrepancy resolution and compliance verification platform that can be used to enhance data management workflow efficiency and regulatory compliance. The proposed three-stage workflow consists of a Query Detection Layer to identify discrepancies in data, an Automated Classification Engine that applies rules for triaged and automated management of discrepancies and disputes, and a Compliance Verification Module that checks/evaluates the audit trail to ensure that discrepancies resolved meet 21 CFR Part 11 and ICH E6(R3) guidelines. Results from the analysis of datasets, from multi-center clinical trials, indicated that the automated discrepancy resolution process was able to resolve 81.5% of discrepancies identified, reduce the time researchers would need to review discrepancies and differences by 35%-40% per query, and achieve 95% compliance with acceptable standards of accuracy for the verification outputs. Overall, the results of this exploratory investigation indicate that the automated processes embedded within EDC-focused CDM processes could significantly improve data quality, compliance and efficiency, providing an important platform for sustainable, technological enabled data governance throughout the life-cycle of a clinical study.

The quality, and more importantly, the integrity of academic research heavily rely on the peer review process. However, the increasing sophistication and accessibility of Large Language Models (LLMs) pose a potential threat, as they can be utilized to generate generic and ineffective peer reviews, thereby undermining the rigor of scholarly publications. "Pal-reviews," providing generic comments with no critical feedback, are another pressing subject in scientific publications. This research investigates the feasibility of automatically auditing and categorizing peer reviews using fine-tuned transformer-based language models. We explored five prominent architectures, i.e., BERT, XLNet, DistilBERT, RoBERTa and OpenAI GPT-4o-nano, while training them on a very specifically curated dataset of authentic, AI-generated, and generic reviews. We generated the authentic reviews from journals that publish open reviews with articles. ChatGPT was used to generate AI-generated reviews, while sentence concatenation was used to generate generic reviews. Our methodology involved combining paper titles, abstracts, and review text with special tokens, employing robust text encoding techniques, and utilizing a rigorous training routine incorporating cross-validation, focal loss for class imbalance, and early stopping. The models were evaluated using accuracy, F1-score, precision, and recall. Our findings indicate the promising potential of this approach, with GPT-4o-nano substantially surpassing all other models, achieving a held-out test accuracy of 80.7% and macro-F1 of 80.6%. RoBERTa, DistilBERT and XLNet followed with moderate performance, while BERT lagged behind. To encourage reproducibility, the fine-tuned the BERT based models have been published on the Hugging Face Model Hub with open access.

The increasing use of mobile devices in higher education has revolutionized the field. The use of mobile devices in the e-learning process has become imperative. This research aims to identify the mediating and moderating variables affecting learning outcomes. The research model is tested using AMOS 27, a statistical software package used for structural equation modeling (SEM). There are three notable findings: First, mobile technology strengthens motivation and enhances self-regulated learning. Second, this research reexamined the effects of extrinsic motivation on SRL, SID, and learning outcomes. Finally, this research concludes that mobile technology use in distance learning does not moderate the relationship between self-regulated learning activities and student learning outcomes.

Universities are experiencing challenges with students completing their degrees due to an increase in mental health issues. Effects of social media, the aftermath of the COVID-19 pandemic, a lack of community, and ineffective self-help and coping skills appear to be positively associated factors. The consequences of unaddressed mental health issues on academic efficacy are well established in research. In an effort to assist struggling students, universities have invested in multiple on-campus student success programs; however, the students in greatest need of these services are often the least likely to take advantage of them. Personalized targeted case management (TCM) is an active approach that physically connects students with services. Social work students are uniquely prepared for these positions due to the discipline's theoretical underpinnings, skills-based approach, and accreditation standards for practicum-based education. Specifics of the TCM job description, lessons learned from a pilot semester, and suggestions moving forward will be discussed.