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1. Large Language Models for Conducting Advanced Text Analytics Information Systems Research

   https://doi.org/10.1145/3682069  

   Ampel, Benjamin; Yang, Chi-Heng; Hu, James; Chen, Hsinchun (March 2025, ACM Transactions on Management Information Systems)

   The exponential growth of digital content has generated massive textual datasets, necessitating the use of advanced analytical approaches. Large Language Models (LLMs) have emerged as tools that are capable of processing and extracting insights from massive unstructured textual datasets. However, how to leverage LLMs for text analytics Information Systems (IS) research is currently unclear. To assist the IS community in understanding how to operationalize LLMs, we propose a Text Analytics for Information Systems Research (TAISR) framework. Our proposed framework provides detailed recommendations grounded in IS and LLM literature on how to conduct meaningful text analytics IS research for design science, behavioral, and econometric streams. We conducted three business intelligence case studies using our TAISR framework to demonstrate its application in several IS research contexts. We also outline the potential challenges and limitations of adopting LLMs for IS. By offering a systematic approach and evidence of its utility, our TAISR framework contributes to future IS research streams looking to incorporate powerful LLMs for text analytics. 

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2. Building Research Collaboration Networks: Leveraging Sponsored Research Data

   https://doi.org/10.13023/50DS-PZ07  

   Pilgrim, Robert (May 2024, University of Kentucky Libraries)

   Discover how to transform standard research award/proposal reports into dynamic visualizations of your research collaborations using Microsoft Power BI and custom visuals. This session guides you through creating interactive networks to visualize your research community. While we'll touch on network analysis, detailed statistical discussions are beyond the scope of this session. Participants are encouraged to bring a laptop and their data, although dummy data will be available. We'll cover data loading and transformation in Microsoft Power BI, employing custom visuals for network generation, and we will explore practical applications of research networks. Please download Microsoft Power BI Desktop beforehand if you plan to follow along, data can be provided or use your own! Presented at the 2024 Research Analytics Summit in Albuquerque, NM 

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3. An Exploration of the Effects of Enterprise Social Media Use for Classroom Teams.

   Cherchiglia, L.; Van Osch, W.; Liang, Y.; Averkiadi, E. (December 2020, SIGHCI 2020 Proceedings)

   null (Ed.)

   Cherchiglia et al. Effects of ESM Use for Classroom Teams Proceedings of the Nineteenth Annual Pre-ICIS Workshop on HCI Research in MIS, Virtual Conference, December 12, 2020 1 An Exploration of the Effects of Enterprise Social Media Use for Classroom Teams Leticia Cherchiglia Michigan State University leticia@msu.edu Wietske Van Osch HEC Montreal & Michigan State University wietske.van-osch@hec.ca Yuyang Liang Michigan State University liangyuy@msu.edu Elisavet Averkiadi Michigan State University averkiad@msu.edu ABSTRACT This paper explores the adoption of Microsoft Teams, a group-based Enterprise Social Media (ESM) tool, in the context of a hybrid Information Technology Management undergraduate course from a large midwestern university. With the primary goal of providing insights into the use and design of tools for group-based educational settings, we constructed a model to reflect our expectations that core ESM affordances would enhance students’ perceptions of Microsoft Teams’ functionality and efficiency, which in turn would increase both students’ perceptions of group productivity and students’ actual usage of Microsoft Teams for communication purposes. In our model we used three core ESM affordances from Treem and Leonardi (2013), namely editability (i.e., information can be created and/or edited after creation, usually in a collaborative fashion), persistence (i.e., information is stored permanently), and visibility (i.e., information is visible to other users). Analysis of quantitative (surveys, server-side; N=62) and qualitative (interviews; N=7) data led to intriguing results. It seems that although students considered that editability, persistency, and visibility affordances within Microsoft Teams were convenient functions of this ESM, problems when working collaboratively (such as connectivity, formatting, and searching glitches) might have prevented considerations of this ESM as fast and user-friendly (i.e., efficient). Moreover, although perceived functionality and efficiency were positively connected to group productivity, hidden/non-intuitive communication features within this ESM might help explain the surprising negative connection between efficiency and usage of this ESM for the purpose of group communication. Another explanation is that, given the plethora of competing tools specifically designed to afford seamless/optimal team communication, students preferred to use more familiar tools or tools perceived as more efficient for group communication than Microsoft Teams, a finding consistent with findings in organizational settings (Van Osch, Steinfield, and Balogh, 2015). Beyond theoretical contributions related to the impact that ESM affordances have on users’ interaction perceptions, and the impact of users’ interaction perceptions on team and system outcomes, from a strategic and practical point of view, our findings revealed several challenges for the use of Microsoft Teams (and perhaps ESM at large) in educational settings: 1) As the demand for online education grows, collaborative tools such as Microsoft Teams should strive to provide seamless experiences for multiple-user access to files and messages; 2) Microsoft Teams should improve its visual design in order to increase ease of use, user familiarity, and intuitiveness; 3) Microsoft Teams appears to have a high-learning curve, partially related to the fact that some features are hidden or take extra steps/clicks to be accessed, thus undermining their use; 4) Team communication is a complex topic which should be further studied because, given the choice, students will fall upon familiar tools therefore undermining the full potential for team collaboration through the ESM. We expect that this paper can provide insights for educators faced with the choice for an ESM tool best-suited for group-based classroom settings, as well as designers interested in adapting ESMs to educational contexts, which is a promising avenue for market expansion. 

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4. Towards strengthening links between learning analytics and assessment: Challenges and potentials of a promising new bond

   https://doi.org/10.1016/j.chb.2022.107304  

   Gašević, D.; Greiff, S.; Shaffer, D. W. (January 2022, Computers in human behavior)

   Learning analytics uses large amounts of data about learner interactions in digital learning environments to understand and enhance learning. Although measurement is a central dimension of learning analytics, there has thus far been little research that examines links between learning analytics and assessment. This special issue of Computers in Human Behavior highlights 11 studies that explore how links between learning analytics and assessment can be strengthened. The contributions of these studies can be broadly grouped into three categories: analytics for assessment (learning analytic approaches as forms of assessment); analytics of assessment (applications of learning analytics to answer questions about assessment practices); and validity of measurement (conceptualization of and practical approaches to assuring validity in measurement in learning analytics). The findings of these studies highlight pressing scientific and practical challenges and opportunities in the connections between learning analytics and assessment that will require interdisciplinary teams to address: task design, analysis of learning progressions, trustworthiness, and fairness – to unlock the full potential of the links between learning analytics and assessment. 

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5. A Data Driven Approach to Research Enterprise Growth and Infrastructure Design

   https://doi.org/10.13023/9MEF-ZY96  

   Zuskar, Marisa; Labayen, Gabby (April 2024, University of Kentucky Libraries)

   As proposal, subaward, award, and agreement volumes continue to grow at your institution, how can you strategize to support the size of your research infrastructure? How can you justify the expansion of your teams and talent? How do you adjust roles and workload to align with the growing portfolio? What technology considerations should you make to track this? This session will cover how to leverage data to make effective business decisions regarding resource needs and allocation methodology to meet growing demands, starting with your internal data and then looking externally. We will cover strategies for using data analytics to efficiently manage the size of your research enterprise and portfolios, as well as measure performance of pre-award and post-award functions across the grant lifecycle. Participants will understand how to analyze portfolios, beyond sheet volume, and analyze the various criteria that can be objectively evaluated to determine how to balance your sponsored programs portfolio. We will review common reporting and analytics tools and provide examples of critical data points to consider at both the central office and department level. Presented at the 2024 Research Analytics Summit in Albuquerque, NM 

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