Special Issue in Honor of Prof. Ting-Peng Liang’s Lifetime Contribution to the Service Innovation Discipline

This special issue is dedicated to the reminiscences of TP for his significant contributions to the global IS discipline. This PAJAIS special issue solicits research submissions that are related to the Service Innovation discipline, one of TP’s key areas of research. Since service-oriented economy is evolving into experience economy, the research topics regarding how to design products, services, information systems, and mobile services to increase users’ experience value are becoming more and more important. From a service logic perspective, innovative service design focus on how they change customer thinking, participation, and capabilities to co-create value rather than new features in order to enhance user experience. Hence, this special issue focuses on issues related to service innovation, service quality & user experience (UX)

Artificial Intelligence and Visual Analytics: A Deep-Learning Approach to Analyze Hotel Reviews & Responses

With a growing number of online reviews, consumers often rely on these reviews to make purchase decisions. However, little is known about managerial responses to online hotel reviews. This paper reports on a framework to integrate visual analytics and machine learning techniques to investigate whether hotel managers respond to positive and negative reviews differently and how to use a deep-learning approach to prioritize responses. In this study, forty 4- and 5-star hotels in London with 91,051 reviews and 70,397 responses were collected and analyzed. Visual analyses and machine learning were conducted. The results indicate most hotels (72.5%) showing no preference to respond to positive and negative reviews. Our proposed deep-learning approach outperformed existing algorithms to prioritize responses

Dual task measures in older adults with and without cognitive impairment: Response to simultaneous cognitive-exercise training and minimal clinically important difference estimates

BACKGROUND: Responsiveness and minimal clinically important difference (MCID) are critical indices to understand whether observed improvement represents a meaningful improvement after intervention. Although simultaneous cognitive-exercise training (SCET; e.g., performing memory tasks while cycling) has been suggested to enhance the cognitive function of older adults, responsiveness and MCID have not been established. Hence, we aimed to estimate responsiveness and MCIDs of two dual task performance involving cognition and hand function in older adults with and without cognitive impairment and to compare the differences in responsiveness and MCIDs of the two dual task performance between older adults with and without cognitive impairment. METHODS: A total of 106 older adults completed the Montreal Cognitive Assessment and two dual tasks before and after SCET. One dual task was a combination of Serial Sevens Test and Box and Block Test (BBT), and the other included frequency discrimination and BBT. We used effect size and standardized response mean to indicate responsiveness and used anchor- and distribution-based approaches to estimating MCID ranges. When conducting data analysis, all participants were classified into two cognitive groups, cognitively healthy (Montreal Cognitive Assessment ≥ 26) and cognitively impaired (Montreal Cognitive Assessment \u3c 26) groups, based on the scores of the Montreal Cognitive Assessment before SCET. RESULTS: In the cognitively healthy group, Serial Seven Test performance when tasked with BBT and BBT performance when tasked with Serial Seven Test were responsive to SCET (effect size = 0.18-0.29; standardized response mean = 0.25-0.37). MCIDs of Serial Seven Test performance when tasked with BBT ranged 2.09-2.36, and MCIDs of BBT performance when tasked with Serial Seven Test ranged 3.77-5.85. In the cognitively impaired group, only frequency discrimination performance when tasked with BBT was responsive to SCET (effect size = 0.37; standardized response mean = 0.47). MCIDs of frequency discrimination performance when tasked with BBT ranged 1.47-2.18, and MCIDs of BBT performance when tasked with frequency discrimination ranged 1.13-7.62. CONCLUSIONS: Current findings suggest that a change in Serial Seven Test performance when tasked with BBT between 2.09 and 2.36 corrected number (correct responses - incorrect responses) should be considered a meaningful change for older adults who are cognitively healthy, and a change in frequency discrimination performance when tasked with BBT between 1.47 and 2.18 corrected number (correct responses - incorrect responses) should be considered a meaningful change for older adults who are cognitively impaired. Clinical practitioners may use these established MCIDs of dual tasks involving cognition and hand function to interpret changes following SCET for older adults with and without cognitive impairment. TRIAL REGISTRATION: NCT04689776, 30/12/2020

Location-Aware Visual Question Generation with Lightweight Models

This work introduces a novel task, location-aware visual question generation (LocaVQG), which aims to generate engaging questions from data relevant to a particular geographical location. Specifically, we represent such location-aware information with surrounding images and a GPS coordinate. To tackle this task, we present a dataset generation pipeline that leverages GPT-4 to produce diverse and sophisticated questions. Then, we aim to learn a lightweight model that can address the LocaVQG task and fit on an edge device, such as a mobile phone. To this end, we propose a method which can reliably generate engaging questions from location-aware information. Our proposed method outperforms baselines regarding human evaluation (e.g., engagement, grounding, coherence) and automatic evaluation metrics (e.g., BERTScore, ROUGE-2). Moreover, we conduct extensive ablation studies to justify our proposed techniques for both generating the dataset and solving the task.Comment: EMNLP 202

Evolution of carbapenem resistance in Acinetobacter baumannii: An 18-year longitudinal study from a medical center in northern Taiwan

BackgroundCarbapenem-resistant Acinetobacter baumannii has emerged as an important cause of nosocomial infections with high morbidity and mortality. The carbapenemases, especially class D carbapenem-hydrolyzing oxacillinases (CHDLs), play an important role, but the relationship between their prevalence trend and carbapenem resistance remains unclear.Materials and methodsBetween 1995 and 2012, we collected 667 isolates of A. baumannii from a single medical center in northern Taiwan. Pulsed-field gel electrophoresis (PFGE) was used to determine clonality. Antimicrobial susceptibility was determined. Carbapenemase genes and associated genetic structures were detected by polymerase chain reaction.ResultsIsolates were heterogeneous on PFGE. Susceptibility to carbapenem decreased steadily over the study period from 88.1% (2001–2003) to <25% (2010–2012), whereas the isolates remained susceptible to colistin (nearly 100%) and partially susceptible to tigecycline (80%). Starting in 2001, isolates carrying the ISAba1-blaOXA-51-like allele were consistently identified. Isolates containing the transposons Tn2006 or Tn2008 first appeared in 2007 with increasing carriage rates from 17.5% (2007–2009) to 50.0% (2010–2012). The IS1008-ΔISAba3-blaOXA-58-like, blaOXA-72 and metallo-β-lactamase genes were detected only sporadically. Isolates carrying CHDL genes were resistant to multiple drugs, including carbapenem, but remained susceptible to colistin (100.0%).ConclusionIncreased carbapenem resistance in A. baumannii may be caused by the increased prevalence of isolates containing the ISAba1-blaOXA-51-like allele and the transposons Tn2006 and Tn2008

A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images

Motivation: High-throughput image-based assay technologies can rapidly produce a large number of cell images for drug screening, but data analysis is still a major bottleneck that limits their utility. Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise