https://www.fox.temple.edu/sites/fox/files/Frontiers-Machines-versus-Humans-The-Impact-of-Artificial-Intelligence-Chatbot-Disclosure-on-Customer-Purchases.pdf
Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases Xueming Luo,aSiliang Tong,aZheng Fang,bZhe Quc
https://www.fox.temple.edu/sites/fox/files/documents/CVs/xueming-luo-cv.pdf
Bio: Xueming Luo is the Charles Gilliland Distinguished Chair Professor of Marketing, Professor of Strategy, and Professor of MIS, and Founder/ Director of the Global Institute for Artificial Intelligence & Business Analytics in the Fox School of Business at Temple University. He is an interdisciplinary thought-leader in leveraging AI/ML algorithms, text/audio/image/video big data ...
https://scholarshare.temple.edu/bitstreams/1c1f1a6d-0f34-4234-8b39-41d9eeb397f0/download
of machine learning (ML) versus a traditional statistical model in predicting dental caries in
https://cis.temple.edu/~jiewu/research/publications/Publication_files/IEEE%20TMC-2025%EF%BC%88Forward_Legal_Anonymous_Group_Pairing-Onion_Routing_for_Mobile_Opportunistic_Networks%EF%BC%89.pdf
a group can potentially act as a relay. To ensure message authenticity, we employ the efficient SM2 signing algorithm to generate signatures for the message source. Furthermore, by incorporating parameters such as the public key validity period and master key validity period into the group pairing-onion routing protocol, we achieve forward security in message delivery. We conduct a thorough ...
https://cis.temple.edu/~apal/nfmi_comnet.pdf
Near Field Magnetic Induction (NFMI) based communication is an emerging technology that promises several advantages over the traditional radio frequency (RF) communication including low energy use, ability to work reliably in a variety of difficult propagation media (e.g., water, non-ferromagnetic metals, underground, tissue media of fresh produce & meats, etc.), and low leakage possibility ...
https://www.fox.temple.edu/sites/fox/files/ISR-delayed-effects.pdf
Can location-based mobile promotion (LMP) trigger contemporaneous and delayed sales purchases? As mobile technologies can reach users anywhere and anytime, LMP becomes a promising new channel. We unravel the dynamic sales impact of LMP on the basis of a randomized field experiment with 22,000 mobile users sponsored by one of the largest mobile service providers in the world. Our identification ...
https://www.fox.temple.edu/sites/fox/files/Complementarity-and-Cannibalization-of-Offline-to-Online-Targeting-A-Field-Experiment-on-Omnichannel-Commerce.pdf
However, it is debatable whether such offline-to-online tar-geting is effective. On the one hand, advocates argue that inducing offline customers to buy online may complement a firm’s store channel. This is because as more channels are used to engage customers, the value of these customers increases (Gimpel et al. 2018), and multichannel shoppers are more loyal and spend more than single ...
https://www.fox.temple.edu/sites/fox/files/documents/Cummins%20Conference%202022/RILA_Moenig_JRI_final.pdf
Registered index-linked annuities (RILAs) are increasingly popular equity-based re-tirement savings products o ered by U.S. life insurance companies. They combine features of xed-index annuities and traditional variable annuities (TVAs), o ering in-vestors equity exposure with downside protection in a tax-deferred setting. This article introduces RILAs to the academic literature by describing ...
https://cis.temple.edu/~wu/research/publications/Publication_files/1571039973-LCN2024.pdf
Abstract—Local Area Networks (LANs), as interconnected networks, are susceptible to numerous security threats. Existing intrusion detection systems (IDS) heavily rely on large, fully-labeled datasets to have accurate detection, facing challenges when only a few malicious samples are available. In addition, previous studies have identified the deterioration of IDS’s per-formance when the ...
https://cis.temple.edu/~yu/research/D3Guard-Infocom19.pdf
D3-Guard achieves an average accuracy of 93:31% for detecting all types of drowsy driving actions. Furthermore, the accuracy of a specific action for all drivers are different, since various drivers have differences in doing the same action. Among the 5 drivers and the 3 actions, the lowest accuracy is 89:57% (Driver 2 and operating SW).