CO-CREATING SUSTAINABLE AND DYNAMIC CURRICULUM: A DECISION SUPPORT SYSTEM FOR ASSURING GRADUATE CAPABILITY

2023 | European Conference On Information Systems | Citations: 0

Authors: Sridharan, Bhavani; Mustard, Jamie

Abstract: An important challenge facing higher education institutions is authentically as ... Expand

Abstract: An important challenge facing higher education institutions is authentically assuring graduate capabilities, including the assessment of 'softer skills'. Many institutions have developed procedures through curriculum reform and renewal processes, but too often these processes involve isolated approaches such as 'ticking the box', use of summary statistics and gap analysis which are both imprecise and piecemeal. Despite growing interest in assuring employable graduate capabilities, there has been little research into using a structured holistic approach engaging with multiple key stakeholders. This research proposes a decision support system adopting a multi-criteria decision approach (MCDA) to appraise whole of degree level outcomes and the robustness of assessment policy decisions. The objective is to authentically assure graduate capability by considering multiple key criteria to reduce arbitrariness of current approaches to assessment design. Multiple scenarios are presented to explain the application of MCDA in evaluating assessment benchmarks using simple to complex criteria. Collapse

Topics: decision support system soft skill

Methods: deep learning data imputation trade-off analysis method multiple criteria decision making cross sectional research

A Human-in-the-Loop Segmented Mixed-Effects Modeling Method for Analyzing Wearables Data

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Srinivasan, Karthik; Currim, Faiz; Ram, Sudha

Abstract: Wearables are an important source of big data, as they provide real-time high-re ... Expand

Abstract: Wearables are an important source of big data, as they provide real-time high-resolution data logs of health indicators of individuals. Higher-order associations between pairs of variables is common in wearables data. Representing higher-order association curves as piecewise linear segments in a regression model makes them more interpretable. However, existing methods for identifying the change points for segmented modeling either overfit or have low external validity for wearables data containing repeated measures. Therefore, we propose a human-in-the-loop method for segmented modeling of higher-order pairwise associations between variables in wearables data. Our method uses the smooth function estimated by a generalized additive mixed model to allow the analyst to annotate change point estimates for a segmented mixed-effects model, and thereafter employs Brent's constrained optimization procedure to fine-tune the manually provided estimates. We validate our method using three real-world wearables datasets. Our method not only outperforms state-of-the-art modeling methods in terms of prediction performance but also provides more interpretable results. Our study contributes to health data science in terms of developing a new method for interpretable modeling of wearables data. Our analysis uncovers interesting insights on higher-order associations for health researchers. Collapse

Topics: smart wearable device mobile system analytical information system human computer interaction human in the loop

Methods: design science wearables based research conceptual modelling hierarchical linear modeling computational algorithm

Examining Disease Multimorbidity in U.S. Hospital Visits Before and During COVID-19 Pandemic: A Graph Analytics Approach

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Srinivasan, Karthik; Jiang, Jinhang

Abstract: Enduring effects of the COVID-19 pandemic on healthcare systems can be preempted ... Expand

Abstract: Enduring effects of the COVID-19 pandemic on healthcare systems can be preempted by identifying patterns in diseases recorded in hospital visits over time. Disease multimorbidity or simultaneous occurrence of multiple diseases is a growing global public health challenge as populations age and long-term conditions become more prevalent. We propose a graph analytics framework for analyzing disease multimorbidity in hospital visits. Within the framework, we propose a graph model to explain multimorbidity as a function of prevalence, category, and chronic nature of the underlying disease. We apply our model to examine and compare multimorbidity patterns in public hospitals in Arizona, U.S., during five six-month time periods before and during the pandemic. We observe that while multimorbidity increased by 34.26% and 41.04% during peak pandemic for mental disorders and respiratory disorders respectively, the gradients for endocrine diseases and circulatory disorders were not significant. Multimorbidity for acute conditions is observed to be decreasing during the pandemic while multimorbidity for chronic conditions remains unchanged. Our graph analytics framework provides guidelines for empirical analysis of disease multimorbidity using electronic health records. The patterns identified using our proposed graph model informs future research and healthcare policy makers for pre-emptive decision making. Collapse

Topics: pandemic electronic health record decision making programming language database system

Methods: longitudinal survey longitudinal research deep learning machine learning ablation analysis

Are Sustainability Reports Informative About Firm Value and Performance? A Text Mining Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: Srinivasan, Karthik; Rao, Sunita; juma, Norma

Abstract: ... Expand

Abstract: Collapse

Topics: organizational value organizational productivity logistics management personal computer programming language

Methods: sentiment analysis latent dirichlet allocation deep learning qualitative content analysis machine learning method

Theories: institutional theory

Online Sex Trafficking Policy Research: A Systematic Review

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Srivastava, Smriti; Dhillon, Gurpreet

Abstract: Online sex trafficking cases have been increasing every day. The field of Infor ... Expand

Abstract: Online sex trafficking cases have been increasing every day. The field of Information Systems (IS) has not significantly explored research on online sex trafficking specifically to evaluate existing literature and provide future research directions. In this paper, we use Latent Semantic Indexing (LSI) to review the literature on online sex trafficking. Our focus is on policies related to online sex trafficking. We then summarize our findings and suggest a future research agenda. Collapse

Topics: database system

Methods: latent semantic indexing literature sample principal component analysis descriptive statistic data transformation

Achieving Competitive Advantages Through Situation-Aware Big Data Engineering – When (Not) to Use Test Driven Development

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Staegemann, Daniel; Volk, Matthias; Pohl, Matthias; Haertel, Christian; Jamous, Naoum; Turowski, Klaus

Abstract: Data and their analysis are one of the driving forces of today's society. There ... Expand

Abstract: Data and their analysis are one of the driving forces of today's society. Therefore, the concept of big data (BD) is also heavily discussed by researchers and practitioners alike. Besides many other aspects, this also includes the corresponding quality assurance. The publication at hand contributes to this by further examining the rather recent proposition of applying the test driven development (TDD) approach to the BD domain. More specifically, the paper aims to shed light on the question, in which BD scenarios TDD's application is likely to result in competitive advantages and when it should be refrained from. For doing so, it firstly outlines the advantages and drawbacks of TDD in a BD context. Afterwards, these aspects are brought together in a discussion to answer the research question. Thereby, this paper contributes to the scientific body of knowledge while also providing additional insights for practitioners and decision makers in the domain. Collapse

Topics: competitive advantage

Methods: machine learning

EMPOWERING COMMUNITY HEALTH WORKERS WITH MOBILE HEALTH: LEARNINGS FROM TWO PROJECTS ON NON-COMMUNICABLE DISEASE CARE

2023 | European Conference On Information Systems | Citations: 0

Authors: Staehelin, Dario; Greve, Maike; Schwabe, Gerhard

Abstract: Community-based healthcare is a promising approach to tackling workforce shorta ... Expand

Abstract: Community-based healthcare is a promising approach to tackling workforce shortage in healthcare, especially in the global south. Community health workers (CHWs) are lay cadres that bridge healthcare disparities by living in the community where they should provide basic health services, mainly through education. However, high attrition rates and underperformance of these health workers limit the scope of such programs. In addition, mobile health is not the hoped-for silver bullet to solve the two challenges. This paper examines two pilot projects using mobile health for non-communicable disease care from an empowerment perspective. We propose design knowledge of mobile health for the structural empowerment of CHWs. Furthermore, we evaluate their psychological empowerment by analyzing mobile health's intended and unintended consequences. Finally, our study demonstrates how empowering CHWs could help overcome the persisting challenges and lead to a sustainable and resilient health system. Collapse

Topics: mobile health unintended consequence mobile system health information system healthcare data

Methods: design requirement qualitative interview longitudinal research group interview experiment

From Solution Trap to Solution Patchwork: Tensions in Digital Health in the Global Context

2023 | International Conference on Information Systems | Citations: 0

Authors: Staehelin, Dario; Miscione, Gianluca; Dolata, Mateusz

Abstract: This paper problematizes underlying assumptions in Design Science Research -and ... Expand

Abstract: This paper problematizes underlying assumptions in Design Science Research -and InformationSystems Research more broadly by conceptualizing the "solution trap". The solution trap is caused by the incompatibility of co-existing solutions in complex socio-technical contexts. Information systems bring diverse cultures and theories together, causing tensions in the different institutional logics. We emphasize the need for a nuanced understanding of context unevenness and propose solution patchwork as a coordination approach to evade the solution trap. Substantiating the preliminary insights and propositions with a literature review and further empirical grounding will transition this research-in-progress to a full paper. Collapse

Topics: health information system problem solving organizational context innovation management information system use

Methods: design science

Theories: socio technical theory

What Do Customers Say About My Products? Benchmarking Machine Learning Models for Need Identification

2023 | HICSS | Citations: 0

Authors: Stahlmann, S.; Ettrich, O.; Kurka, M.; Schoder, D.

Abstract: Needmining is the process of extracting customer needs from user-generated cont ... Expand

Abstract: Needmining is the process of extracting customer needs from user-generated content by classifying it as either informative or uninformative regarding need content. Contemporary studies achieve this by utilizing machine learning. However, models found in the literature cannot be compared to each other because they use private data for training and testing. This study benchmarks all previously suggested needmining models including CNN, SVM, RNN, and RoBERTa. To ensure an unbiased comparison, this study samples and annotates a dataset of customer reviews for products from 4 different categories from amazon. Henceforth, the dataset is publicly available and serves as a gold-set for future needmining benchmarks. RoBERTa outperformed other classifiers and seems to be best suited for needmining. The relevance of this study is reinforced by the fact that this benchmark creates a different hierarchy between models than otherwise suggested by comparing the results of previous studies. Collapse

Topics: user-generated content Wikipedia Python online review Twitter

Methods: support vector machine RoBERTa naïve bayes machine learning XLNet

User-Centered Visual Design of Alarms in Manufacturing Dashboards: Insights on Comprehensibility and Preferences

2023 | International Conference on Information Systems | Citations: 0

Authors: Stahmann, Philip; Gravemeier, Laura; Dittmer, Anke; Janiesch, Christian; Thomas, Oliver

Abstract: The digitization of the manufacturing domain results in an enormous amount of a ... Expand

Abstract: The digitization of the manufacturing domain results in an enormous amount of available data decision-makers are continuously exposed to. Consequently, critical alarm information must be easily comprehensible and suit decision-makers' preferences to facilitate timely reactions and thus prevent harm to production processes and employees. However, despite its importance, academia and practice show little consensus regarding alarm design in manufacturing dashboards. Against this background, the purpose of our study is twofold. First, we identified 5 factors for visual alarm design with overall 12 design options in a structured literature review. Second, we investigated the effect these design options have on users' comprehension and preferences in a conjoint study and a best-worst scaling approach with 98 participants with experience working in manufacturing. Our results show that alarm description and their visual integration are the most important factors for alarm design. In this regard, a cluster analysis reveals more nuanced and more stable preferences in more experienced users. Further, we find that color-coding-based content classification entails best performance in recognizing alarms. We contribute to academia and practice by providing actionable insights that may support improving alarm design in manufacturing. Collapse

Topics: digital dashboard usability user interface virtual reality tablet

Methods: cluster analysis machine learning structured literature research descriptive statistic factorial experiment