Digital Transformations of Business Operations
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Item Utilizing AI and Social Media Analytics to Discover Unreported Adverse Side Effects of GLP-1 Receptor Agonists Used for Obesity Treatment(2025-01-07) Bartal, Alon; Jagodnik, Kathleen; Pliskin, Nava; Seidmann, AbrahamAdverse side effects (ASEs) of drugs, revealed after FDA approval, pose a threat to patient safety. To promptly detect overlooked ASEs, we developed a digital health methodology capable of analyzing massive public data from social media, published clinical research, manufacturers' reports, and ChatGPT. We uncovered ASEs associated with the glucagon-like peptide 1 receptor agonist (GLP-1 RA) medications used to treat diabetes and obesity, a market expected to grow exponentially to $133.5 billion USD by 2030. Using a named entity recognition model, our method successfully detected 15 potential ASEs of GLP-1 RAs, overlooked upon FDA approval. Our data-analytic approach revolutionizes the detection of unreported ASEs associated with newly deployed medications, leveraging cutting-edge AI-driven social media analytics. This ongoing research can increase the safety of new medications in the marketplace by unlocking the power of social media to support regulators and manufacturers in the rapid discovery of hidden ASE risks.Item Towards a Benchmark for Large Language Models for Business Process Management Tasks(2025-01-07) Busch, Kiran; Leopold, HenrikAn increasing number of organizations are deploying Large Language Models (LLMs) for a wide range of tasks. Despite their general utility, LLMs are prone to errors, ranging from inaccuracies to hallucinations. To objectively assess the capabilities of existing LLMs, performance benchmarks are conducted. However, these benchmarks often do not translate to more specific real-world tasks. This paper addresses the gap in benchmarking LLM performance in the Business Process Management (BPM) domain. Currently, no BPM-specific benchmarks exist, creating uncertainty about the suitability of different LLMs for BPM tasks. This paper systematically compares LLM performance on four BPM tasks focusing on small open-source models. The analysis aims to identify task-specific performance variations, compare the effectiveness of open-source versus commercial models, and assess the impact of model size on BPM task performance. This paper provides insights into the practical applications of LLMs in BPM, guiding organizations in selecting appropriate models for their specific needs.Item The Impact of Customer Service Accounts on Social Media Consumer Engagement: A Natural Experiment(2025-01-07) Kim, Ahreum; Al Balawi, Ramah; Hu, Yuheng; Qiu, LiangfeiThis study explores the effect of creating dedicated customer service (CS) accounts on consumer engagement with the Main accounts for brands on social media. Given the importance of customer-brand interactions, creating dedicated CS accounts can allow brands to deliver faster and more efficient responses to consumer needs. However, dedicated CS accounts could diminish consumer engagement with brands’ Main accounts on social media. This research aims to address this gap by examining whether segregating customer service interactions into dedicated accounts affects consumer engagement with Main accounts on social media. Using a large Twitter dataset, we observe an overall increase in consumer engagement on Main accounts following the creation of CS accounts. We extend this study to examine the mediating role of consumer tweet traffic to uncover the underlying mechanism. The findings of this study provide important insights for brands to facilitate consumer engagement on social media.Item Applying BPMN and Ontology to Measure Digital Maturity in Construction 4.0 - A Case Study(2025-01-07) Heidenwolf, Orsolya; Antal, Kristóf; Szabó, IldikóIn recent years, Construction 4.0 (C4) has emerged as a pivotal concept representing the digital transformation of the construction industry by integrating advanced technologies such as Building Information Modeling (BIM), the Internet of Things (IoT), Artificial Intelligence (AI), robotics, and big data analytics. Despite the potential benefits, construction organizations often encounter difficulties in implementing these technologies systematically due to a lack of digital maturity. This paper presents how to use the ontology developed from our Construction 4.0 Maturity Model (C4MM) for investigating the digital maturity level of a construction organization based on their digital transformation journey described in Business Process Model and Notation (BPMN) 2.0 models. A case study of a Hungarian construction company illustrates the practical application of our developing methodology, highlighting the key phases of digital transformation and the role of BPMN and ontology in measuring digital maturity. The findings emphasize the importance of aligning business and IT strategies, continuous improvement, and strategic innovation management in achieving successful digital transformation.Item Introduction to the Minitrack on Digital Transformations of Business Operations(2025-01-07) Seidmann, Abraham; Jiang, Yabing; Zhang, Jie