Information Systems Frontiers最新文献

This paper reports on a study that investigated how the business models of Australian accounting firms are impacted by audit clients using blockchain technology. Semi-structured interviews with a range of stakeholders including audit partners from big-4 accounting firms reveal that firms are gradually adapting their business models by offering value propositions that involve efficiency, complementarities and novelty, despite a formative blockchain ecosystem in Australia. This ecosystem is characterized by clients’ reluctance to use blockchain platforms for financial systems and a lack of clear direction on applicable accounting standards and consensus on blockchain standards and governance.

During a crisis, small and family-owned businesses tend to experience more severe economic consequences than their larger counterparts and often lack financial resources needed to weather the challenges brought about by the crisis. To comprehend the distinct challenges and concerns of small and family-owned businesses during a major crisis, this research study focuses on the recent COVID-19 pandemic, which had a catastrophic effect on businesses and societies alike. To that effect, we address two research questions: First, what topics pertaining to small and family-owned businesses do social media users discuss during the COVID-19 pandemic? To achieve this goal, we employ the BERTopic model, a state-of-the-art technique for topic modeling, to identify and categorize prevalent themes arising from the discourse. Second, what is the impact of major government announcements on these discussions? Specifically, we study how sentiments change around a major government announcement aimed at supporting small businesses in the face of the pandemic. Our findings suggest that government announcements do not change the negative sentiments for most of the topics. This highlights the ineffectiveness of government announcements in alleviating people’s concern related to small and family-owned business and underscores the importance of a better consultation process and communication strategy by policymakers. The implications of our study transcend recent COVID-19 effects, as World Health Organization (WHO) cautions that there could be even worse health and socio-economic crises in the future, and we need to be better prepared to handle subsequent devastating effects.

Early detection of Alzheimer’s disease (AD) is crucial for timely intervention and management of this debilitating neurodegenerative disorder. However, it demands further serious attention. State-of-the-art vision transformers for multi-class AD detection techniques cannot handle the uncertainty issue arising between various stages of AD. Moreover, AD identification based on magnetic resonance imaging (MRI) scans is likewise computationally expensive. Further, vision transformers used in AD detection often suffer from a lack of interpretability of results. To address these issues, a new vision transformer, namely Fuzzy Granule-based Interpretable Cognitive Vision Transformer (FGI-CogViT) is developed. It has three parts, namely feature extraction, fuzzy logic-based granulation, and I-CogViT-based classification. Various vision and statistical features are computed over the MRI scan image(s). The statistical features are used to obtain the disease-prone regions in terms of fuzzy granules. In these regions, uncertainty may arise among the different stages of AD. Fuzzy logic-based rules are defined to obtain the crisp granules. Instead of considering the entire image, statistical features corresponding to the crisp granules are added with vision features for classification tasks through the I-CogViT that consists of three modules, namely residual network, traditional vision transformer, and classification network. These characteristics improve the speed and accuracy of FGI-CogViT. It synergizes the robust feature extraction capabilities of vision transformers with cognitive computing principles, aiming to augment the model’s interpretability. The efficacy of the FGI-CogViT has been demonstrated over 6,460 MRI scan images. Results reveal that FGI-CogViT outperforms some state-of-the-art. Furthermore, robustness checking and statistical significance testing support the findings.

Smartphones, while ubiquitous and beneficial, can lead to problematic use. This study investigates the intricate interplay between cultural dimensions, smartphone addiction, and employee performance. Through the lens of distraction theory, attachment Theory, coping theory combined with Hofstede's cultural dimensions, and self-regulation theory and quality of life, we examine how collectivism, individualism, uncertainty avoidance, and masculinity cultural dimensions influence smartphone addiction and its subsequent effect on employee performance. The findings, based on data collected from 233 employees at a major medical center in the Midwest region of the USA and employing structural equation modeling, reveal a significant cultural influence on smartphone addiction, ultimately leading to a decline in performance. However, quality of life emerges as a crucial moderator, mitigating the negative impact of smartphone addiction. This research offers valuable insights for information systems scholars, highlighting the importance of cultural context in understanding smartphone addiction. Furthermore, the study equips managers with practical knowledge to address smartphone addiction within a culturally diverse workforce. By implementing strategies that enhance employee quality of life, organizations can foster a more productive and engaged work environment.

Big data analytics (BDA) has a pivotal role in improving business performance, especially in small and medium enterprises (SMEs). The objective of this study is to examine the determinants and consequences of BDA adoption for SMEs. The theoretical foundation of the study is derived from Technology-Organization-Environment (TOE) framework and Resource-Based View (RBV) theory. Using a survey of 233 SMEs in Saudi Arabia, the results reveal that technical aspects (i.e., complexity and compatibility), environmental aspects (i.e., uncertainty), and organizational aspects (i.e., top management support, organization readiness, and data-driven culture) are perceived as factors that encourage firms to adopt BDA. The study shows a strong relationship between BDA and SMEs’ performance (financial, market, and business process). The empirical work presented in this paper adds to the understanding of the motivators of BDA adoption for SMEs, and consequently the effects of BDA adoption on SME performance. Theoretical and practical implications of the results are discussed further.

Augmented Reality (AR) is an increasingly prominent technology with diverse applications across various surgical disciplines. This study aims to develop and assess the feasibility of a novel AR application intended to aid surgeons in the clinical assessment of temporomandibular joint (TMJ) alterations necessitating surgical intervention. The application employs a multi-modality tracking approach, combining both marker-less and marker-based tracking techniques to concurrently track the fixed portion of the joint and the movable mandible involved in TMJ. For the marker-based tracking both a planar marker with a binary QR-code pattern and a cuboid marker that contains a unique QR-code pattern on each face were tested and compared. The AR application was implemented for the HoloLens 2 head-mounted display and validated on a healthy volunteer performing the TMJ task, i.e. the opening and closing of the mouth. During the task, video recordings from the HoloLens cameras captured the horizontal and vertical excursions of the jaw movements (TMJ movements) using virtual markers anchored to the AR-displayed virtual anatomies. For validation, the video-recorded TMJ movements during AR viewing were compared with standard kinesiographic acquisitions. The findings demonstrated the consistency between the AR-derived trajectories and the kinesiography curves, especially when using the cubic Multi Target tracker to follow the moving mandible. Finally, the AR application was experienced on a patient and it was extremely useful for the surgeon to diagnose alterations in the normal kinematics of the TMJ. Future efforts should be addressed to minimize the bulkiness of the tracker and provide additional visual cues for surgeons.

The sustainability of agri-food supply chains (AFSCs) is severely threatened by regional and global events (e.g., conflicts, natural and human-made disasters, climate crises). In response, the AFSC industry is seeking digital solutions using Industry 4.0 (I4.0) technologies to enhance resilience and efficiency. However, why I4.0 adoption remains stubbornly low in the agri-food industry remains poorly understood. To address this gap, this study draws on middle-range theory (MRT) and uses thematic analysis, the fuzzy analytic hierarchy process, total interpretive structural modelling, and fuzzy cross-impact matrix multiplication applied to classification to produce insights from nine case studies in China that have invested in I4.0 technologies to improve their AFSC sustainability. New drivers of I4.0 unique to the agri-food industry are identified, showing how I4.0 can contribute to the environmental, economic, and social dimensions of AFSC sustainability. The results have implications for AFSC researchers and practitioners with an interest in supply chain sustainability.

The surge in online media has inundated the public with information, prompting the use of sensational and provocative language to capture attention, worsening the prevalence of online malicious behavior. This study delves into machine learning (ML) and deep learning (DL) techniques to identify and recognize harmful news and toxic comments, aiming to counteract the detrimental impact on public perception. Effective methods for detecting and categorizing malicious content are proposed and discussed, highlighting the differences between ML and DL approaches in combating malicious behavior. The study employs feature selection methods to scrutinize the distinctive feature set and keywords linked to harmful news and toxic comments. The proposed approach yields promising outcomes, achieving a 94% accuracy rate in recognizing toxic comments, a 68% recognition accuracy for harmful news, and an 81% accuracy in classifying malicious behavior content (combining harmful news and toxic comments). By harnessing the capabilities of ML and DL, this research enriches our comprehension of and ability to mitigate malicious behavior in online media. It provides valuable insights into the practical identification and categorization of harmful news and toxic comments, highlighting the unique facets of these advanced computational strategies as they address the pressing challenges of our digital society.

The ability to use mobile technologies anywhere and anytime has driven an important dark side known in this article as Mobile Technology Addiction (MTA). Here, we extend insights on this phenomenon by building on S–O-R theory and focusing on stimuli (flow and telepresence), organisms (mobile technology addiction), and responses (risky behaviours). This study conceptualised the moderating role of use-regulation between MTA and risky behaviours. Based on a study in the unique context of a developing country, this study adopted a stratified random sampling technique. The questionnaire was deployed through online and offline survey methods to select 528 participants from a developing country in which most internet interactions are done via mobile devices. It was found that MTA drives risky behaviours, but IS use-regulation minimises this effect. The findings provide important implications for theory and practice.

As artificial intelligence (AI) and machine learning (ML) advance, concerns about their sustainability impact grow. The emerging field "Sustainability of AI" addresses this issue, with papers exploring distinct aspects of ML’s sustainability. However, it lacks a comprehensive approach that considers all ML development phases, treats sustainability holistically, and incorporates practitioner feedback. In response, we developed the sustainable ML design pattern matrix (SML-DPM) consisting of 35 design patterns grounded in justificatory knowledge from research, refined with naturalistic insights from expert interviews and validated in three real-world case studies using a web-based instantiation. The design patterns are structured along a four-phased ML development process, the sustainability dimensions of environmental, social, and governance (ESG), and allocated to five ML stakeholder groups. It represents the first artifact to enhance each ML development phase along each ESG dimension. The SML-DPM fuels advancement by aggregating distinct research, laying the groundwork for future investigations, and providing a roadmap for sustainable ML development.