Exploring the ACIS Community through the Analysis of Co-authorship across Institutions

Population-focused care is gaining importance due to an increase in the number of people with long-term conditions. A population-focused care addresses the care needs of a group of patients who share a common trait. Primary health care (PHC) being the first point of contact with a health system, our work aims to predict and analyse this population-level workload at a PHC centre. We followed a design science research (DSR) to develop a workload prediction model. As a part of this work, we identified that current patient information models lack the ability to support population-level analysis. In this paper, we discuss an extended ontology of patient information models to support population-level workload analysis. We describe the three cycles of DSR applied to develop our ontology. Then, we discuss the existing health data models. Thus, this paper makes a domain-specific application of DSR to develop a patient information model that supports population-level analysis

Contextual Difference and Intention to Perform Information Security Behaviours Against Malware in a BYOD Environment: a Protection Motivation Theory Approach

The research domain of end-user’s information security behaviours has been gaining much attention over the recent years. While the nature of intention to perform information security behaviours are being revealed, there are still gaps in this area. In particular, few studies have addressed whether such intention remains across contexts, especially from home to public places. Secondly, the amount of the cyber-threats swells with the increase of personal devices with the rapid adoption of the BYOD trend. This research employed MSEM methods to develop a conceptual model based on Protection Motivation Theory by using data collected from 252 higher education students in a BYOD Australian university. Our findings confirmed and explored in details how intention to perform information security behaviours varied due to the change of context. Academics and practitioners could mitigate the security gap by focusing on the intention’s differences discussed in our findings

Digital Kaizen: An Approach for Conducting Large-Scale Digital Transformation Projects

While digital transformation creates opportunities across all industries, many businesses still do not know how to embark on this journey and hesitate to commit resources to such an unclear initiative. By using the interpretive case study method, this paper investigates how Digital Kaizen–a philosophy that focuses on making continuous digital improvements–could guide large-scale digital transformation activities in incremental steps. Our findings show that the adoption of Digital Kaizen has urged the investigated organization to continuously address cross-functional issues that are aligned with their strategic business goals, through implementing incremental digital changes that improve business processes and people engagement. Subsequently, these activities lead to sustainable and scalable digital transformation success that re-defines the organization’s value creation processes and identity. This research suggests a new strategy for conducting large-scale digital transformation, by integrating Kaizen philosophy into digital transformation practices

Explainable Information Security: Development of a Construct and Instrument

Despite the increasing efforts to encourage information security (InfoSec) compliance, employees’ refusal to follow and adopt InfoSec remains a challenge for organisations. Advancements in the behavioural InfoSec field have recently highlighted the importance of developing usable and employeecentric InfoSec that can motivate InfoSec compliance more effectively. In this research, we conceptualise the theoretical structure for a new concept called explainable InfoSec and develop a research instrument for collecting data about this concept. Data was then collected from 724 office workers via an online survey. Exploratory and confirmatory factor analyses were performed to validate the theoretical structure of the explainable InfoSec construct, and we performed structural equation modelling to examine the construct’s impact on intention to comply with organisational InfoSec. The validated theoretical structure of explainable InfoSec consists of two dimensions, fairness and transparency, and the construct was found to positively influence compliance intention

Exploring Value Co-Destruction Process in Customer Interactions with AI-Powered Mobile Applications

Background: Mobile applications have emerged as important touchpoints for addressing service requests and optimizing human resources. Within the service industry, the integration of artificial intelligence (AI) into these applications has enabled the inference of product demand, provision of personalized service offers, and enhancement of overall firm value. Customers now engage with these apps to stay informed, seek guidance, and make purchases. It is important to recognize that the interactive and human-like qualities of AI can either foster the co-creation of value with customers or potentially lead to the co-destruction of customer value. Although prior research has examined the process of value co-creation, the present study aims to investigate the underlying factors contributing to the value co-destruction process, specifically within AI-powered mobile applications. Method: Our research employs topic modelling and content analysis to examine the value co-destruction process that occurs when customers engage with AI apps. We analyze 7,608 negative reviews obtained from eleven AI apps available on Google Play and App Store AI apps. Results: Our findings reveal six distinct types of value - utilitarian, hedonic, symbolic, social, epistemic, and economic value - that can be co-destroyed during the process. System failure, self-threat and privacy violation are some contributing factors to this value co-destruction process. These values change over time and vary depending on the type of app. Conclusion: Theoretically, our findings extend the concept of value co-destruction in the context of AI apps. We also offer practical recommendations for designing an AI app in a more service-friendly way

Categorizing Young Facebook Users Based On Their Differential Preference of Social Media Heuristics: A Q-Methodology Approach

Background: Social media have become an integral part of our modern society by providing platforms for users to create and exchange news, ideas, and information. The increasing use of social media has raised concerns about the reliability of the shared information, particularly information that is generated from anonymous users. Though prior studies have confirmed the important roles of heuristics and cues in the users’ evaluation of trustworthy information, there has been no research–to our knowledge–that categorized Facebook users based on their approaches to evaluating information credibility. Method: We employed Q-methodology to extract insights from 55 young Vietnamese users and to categorize them into different groups based on the distinct sets of heuristics that they used to evaluate the trustworthiness of online information on Facebook. Results: We identified four distinct types of young Facebook user groups that emerged based on their evaluation of online information trustworthiness. When evaluating online information trustworthiness on Facebook, these user groups assigned priorities differently to the characteristics of the online content, its original source, and the sharers or aggregators. We named these groups: (1) the balanced analyst, (2) the critical analyst, (3) the source analyst, and (4) the social network analyst. Conclusion: The findings offer insights that contribute to information processing literature. Moreover, marketing practitioners who aim to disseminate information effectively on social networks should take these user groups’ perspectives into consideration

A Machine Learning-based Approach to Vietnamese Handwritten Medical Record Recognition

Handwritten text recognition has been an active research topic within computer vision division. Existing deep-learning solutions are practical; however, recognizing Vietnamese handwriting has shown to be a challenge with the presence of extra six distinctive tonal symbols and extra vowels. Vietnam is a developing country with a population of approximately 100 million, but has only focused on digitalization transforms in recent years, and so Vietnam has a significant number of physical documents, that need to be digitized. This digitalization transform is urgent when considering the public health sector, in which medical records are mostly still in hand-written form and still are growing rapidly in number. Digitization would not only help current public health management but also allow preparation and management in future public health emergencies. Enabling the digitalization of old physical records will allow efficient and precise care, especially in emergency units. We proposed a solution to Vietnamese text recognition that is combined into an end-to-end document-digitalization system. We do so by performing segmentation to word-level and then leveraging an artificial neural network consisting of both convolutional neural network (CNN) and a long short-term memory recurrent neural network (LSTM) to propagate the sequence information. From the experiment with the records written by 12 doctors, we have obtained encouraging results of 6.47% and 19.14% of CER and WER respectively

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573

Use of Kriging metamodels for seismic fragility analysis of structures

International audienceIn civil engineering, a seismic fragility curve is popularly used to predict failure probability of structures under different earthquakes, and hence propose essential rehabilitation strategies through risk assessment for future earthquakes. The curve shows the failure probability as a function of seismic intensity, e.g., spectral acceleration at fundamental frequencies of structures (Sa, T1), and can be obtained using one of three approaches: engineering judgment, empirical studies or numerical simulations. The paper focuses on constructing seismic fragility curves using numerical simulations, where robust approaches of seismic reliability analysis are based on direct Monte Carlo simulation technique. The MCS based method usually requires a relatively large number of simulations to obtain a sufficiently reliable estimate of the fragility. It therefore becomes computationally expensive and time consuming as generating the simulations using the actual model or called full model of the structure. In this regard, this paper suggests using Kriging metamodel as a viable alternative of the actual model to reduce computational costs in seismic fragility computation. The Kriging metamodel is constructed based on the training samples of input and corresponding output responses of the structure. The validation of this method is performed on two numerical examples

Bioactive-Guided Phytochemical Investigations, In Vitro and In Silico Alpha-Glucosidase Inhibition of Two Vietnamese Medicinal Plants <i>Dicranopteris linearis</i> and <i>Psychotria adenophylla</i>

Little is known about the chemical and biological profiles of Dicranopteris linearis and Psychotria adenophylla. No previous studies have investigated alpha-glucosidase inhibition using extracts from D. linearis and P. adenophylla. In this paper, bioactive-guided isolation procedures were applied to the plants D. linearis and P. adenophylla based on alpha-glucosidase inhibition. From the most active fractions, 20 compounds (DL1–DL13 and PA1–PA7) were isolated. The chemical structures were elucidated using spectroscopic data and compared with those available in the literature. These compounds were evaluated for alpha-glucosidase inhibition, while a molecular docking study was performed to elucidate the mechanisms involved. Consequently, D. linearis and P. adenophylla might serve as a good potential for developing new antidiabetic preparations