The Impact of Competitive Threats from the Product Market on Data Breaches

In this digital era, the concern about data breaches is rapidly increasing among consumers and firms. When the firm is facing competitive threats from the product market, would it perform well in data protection? Does the presence of a CIO/CTO help prevent a data breach? Would the impact of competitive threats on data breach vary by the severity of the data breach? In this study, by examining a sample of data breaches from 2005 to 2018, we find that competitive threats from the product market are positively associated with the likelihood of data breaches. And the presence of a CIO/CTO will also increase the likelihood of a data breach. It’s somewhat different from prior studies. We plan to further explore the competitive threats’ impact on different types of data breaches as well as their latent mechanism. Our study contributes a lot to IS cybersecurity and management literature

ENHANCING WORK PERFORMANCE IN STABLE POST-ADOPTIVE STAGE: A SYSTEM USE-RELATED BEHAVIORS PERSPECTIVE

The success of enterprise systems (ES) hinges on the work performance of system users in the stable post-adoptive stage. With a high failure rate of ES implementation, it is crucial to explore factors that could enhance users’ work performance. Drawing on literature on IS post-adoption and system use-related behaviors, this study proposes a theoretical model to understand how different types of ES use-related behaviors (i.e., technology interaction behaviors, task-technology adaptation behaviors and individual adaptation behaviors) can induce better performance in the stable phase of post-adoption. A field survey involving 250 physicians was conducted to test the proposed research model. The results showed different effects of ES use-related behaviors on improving users’ work performance. Individual adaptation behaviors enhanced the user performance, while technology interaction behaviors and task-technology adaptation behaviors did not show significant effect on performance. Interestingly, individual adaptation and task-technology adaptation behaviors could moderate the relationship between system use and performance, yet in an opposite manner. This study offers important contributions to ES researchers and practitioners

A Bayesian Network-Based Framework for Personalization in Mobile Commerce Applications

Providing personalized services for mobile commerce (m-commerce) can improve user satisfaction and merchant profits, which are important to the success of m-commerce. This paper proposes a Bayesian network (BN)-based framework for personalization in m-commerce applications. The framework helps to identify the target mobile users and to deliver relevant information to them at the right time and in the right way. Under the framework, a personalization model is generated using a new method and the model is implemented in an m-commerce application for the food industry. The new method is based on function dependencies of a relational database and rough set operations. The framework can be applied to other industries such as movies, CDs, books, hotel booking, flight booking, and all manner of shopping settings

Mining Sequential Relations from Multidimensional Data Sequence for Prediction

By analyzing historical data sequences and identifying relations between the occurring of data items and certain types of business events we have opportunities to gain insights into future status and thereby take action proactively. This paper proposes a new approach to cope with the problem of prediction on data sequence characterized by multiple dimensions. The proposed relation mining approach improves the existing sequential pattern mining algorithm by considering multidimensional data sequences and incorporating time constraints. We demonstrate that multidimensional relations extracted by our approach are an enhancement of single dimensional relations by showing significantly stronger prediction capability, despite of the substantial work done in the latter area. In addition, matching algorithm based on the obtained relations is proposed to make prediction. The effectiveness of the proposed methods is validated by experiments conducted on a mobile user context dataset

The Effect of Online Review Portal Design: The Moderating Role of Explanations for Review Filtering

The flood of non-constructive and fake online consumer reviews erects a considerable barrier to consumers making efficient decisions. Various review filtering algorithms have been developed to address this challenge, but the design of post-development review portals continues to lack a consensus. In review portals, disclosing more transparent reviews is efficient for enhancing users’ trust. However, it will cause users’ diminished focus on recommended reviews, leading to sub-optimal decisions. A research model is then developed to investigate users’ cognitive processes in their responses to three review exhibition designs (i.e., informed silent display design, filtered review display design, and composite display design) regarding trust in the review portal and perceived decision quality. We also suggest that explanations for review filtering play a moderating role in users’ perceptions, which appears to be a viable resolution to this dilemma. This paper provides significant theoretical and practical insights for the review portal design and implementation

Exploring the Role of AI Explanations in Delivering Rejection Messages: A Comparative Analysis of Organizational Justice Perceptions between HR and AI

The increasing use of AI decision systems in recruitment processes has created challenges, including potential resistance from job applicants. To address this issue, drawing on organizational justice theory, we identify dimensions of AI explanations in the employment context and examine their impact on job applicants\u27 perceptions of organizational justice. We conducted an experiment to understand applicants\u27 reactions to AI versus HR managers without explanations and examined the impact of AI explanations on organizational justice perceptions and acceptance intention. Our findings show that without explanation, AI is perceived as lower organizational just and acceptance intention compared to HR managers. Organizational justice mediates the effects between outcome/process explanations of AI on acceptance intention. However, outcome explanations have a stronger impact compared to process explanations. Our study contributes to understanding explanation structures for AI-based recruitment and offers practical implications for developing explanations that improve the perceived justice of AI recruitment systems

Contagion in a Financial System

Financial contagion is often observed in recent financial crisis, which illustrates a critical need for new and fundamental understanding of its dynamics. So in this paper we mainly focus on modeling and analysing the financial contagion in a system where a large number of financial institutions are randomly connected by the direct balance sheets linkages own to the lending or borrowing relationships. We propose a simple contagion algorithm to study the effect of several determinants, such as the topology of financial network, exposure ratio, leverage ratio, and the liquidation ratio. One of our finding is that the financial contagion is weaker as the growth of connectivity of network, so a financial system with a higher connectivity is more stability or robustness; we also find that the exposure ratio increases the risk of financial contagion, but both the leverage ratio and liquidation ratio has a negative relationship on financial contagion

Deep Learning on Abnormal Chromosome Segments: An Intelligent Copy Number Variants Detection System Design

Gene testing emerged as a business in the last two decades, and the testing cost has been reduced from 100 million to 1000 dollars for the development of technologies. Preimplantation genetic screening (PGS) is a popular genetic profiling of embryos prior to implantation in gene testing. Copy number variants (CNVs) detection is a key task in PGS which still needs the manual operation and evaluation. At the same time, deep learning technology earns a booming development and wide application in recent years for its strong computing and learning capability. This research redesigns the PGS workflow with the intelligent CNVs detection system, and proposes the corresponding system framework. Deep learning is selected as the proper technology in the system design for CNVs detection, which also fit the task of denoising. The evaluation is conducted on simulation dataset with high accuracy and low time cost, which may achieve the requirements of clinical application and reduce the workload of bioinformatics experts. Moreover, the redesigned process and proposed framework may enlighten the intelligent system design for gene testing in following work, and provide a guidance of deep learning application in AI healthcar

FOCUSING ON CENTRALITY MEASURE IN EMERGENCY MEDICAL SERVICES

Emergency Medical Services (EMS) attracted many researchers because the demand of EMS was increasing over time. One of the major concerns of EMS is the response time and ambulance despatching is one of the vital factors which affects the response time. This paper focuses on the problem of ambulance despatching when many emergency calls emerge in a short time, which exists under the condition of catastrophic natural or manmade disasters. We modify a new method for ambulance despatching by centrality measure, this method constructs a nearest-neighbor coupled emergency call network and then prioritize those calls by the score of fitness, where the score of fitness considers two factors: centralized measure a call by the emergency call network and the closest policy which means despatching to the closest call site. This method is testified by a series of simulation experiments on the real topology road network of Hong Kong Island which contains 8 hospitals. These analyses demonstrate the real situation and proof the potential of centrality measure in reducing response time of EMS