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

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

Building Comparative Product Relation Maps by Mining Consumer Opinions on the Web

With the Web 2.0 paradigm, users play the active roles in producing Web contents at online forums, wiki, blogs, social networks, etc. Among these users contributed contents, many of them are opinions about products, services, or political issues. Accordingly, extracting the comparative relations about products or services by means of opinion mining techniques could generate significant business values. From the producers’ perspective, they could better understand the relative strength or weakness of their products, and hence developing better products to meet the consumers’ requirements. From the consumers’ perspective, they could exercise more informed purchasing decisions by comparing the various features of certain kind of products. The main contribution of this paper is the development of a novel Support Vector Machine (SVM) based comparative relation map generation method for automatic product features analysis based on the sheer volume of consumer opinions posted on the Web. The proposed method has been empirically evaluated based on the consumer opinions crawled from the Web recently. Our initial experimental results show that the performance of the proposed method is promising, and the precision can achieve 73.15%

Predict Market Share with Users’ Online Activities Data: An Initial Study on Market Share and Search Index of Mobile Phone

Acquiring accurate and timely market share information is very important for producers to arrange producing plan and design marketing strategy. However the high cost and long period of collecting survey data in survey-based method make it much difficult to easily get latest market shares data. Recently, the emerging online web systems provide users with new and convenient ways of searching, learning, experiencing and buying products. The users’ activities data captured by these web systems can reflect users’ buying intentions and behaviours very well, and contain very valuable information for predicting market shares. In this study, the correlation between Google search index and market shares of mobile phones is analyzed with time series analysis technology. The experiment result shows the statistically significant relationships exist between search index and market shares. This indicates the easily got search index data with low cost has the power of timely forecasting market shares. This study opens a door to apply users’ online activities data to accurately and timely predict market shares, which will bring many benefits to producers and customers

Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off

Over-parameterization of deep neural networks (DNNs) has shown high prediction accuracy for many applications. Although effective, the large number of parameters hinders its popularity on resource-limited devices and has an outsize environmental impact. Sparse training (using a fixed number of nonzero weights in each iteration) could significantly mitigate the training costs by reducing the model size. However, existing sparse training methods mainly use either random-based or greedy-based drop-and-grow strategies, resulting in local minimal and low accuracy. In this work, we consider the dynamic sparse training as a sparse connectivity search problem and design an exploitation and exploration acquisition function to escape from local optima and saddle points. We further design an acquisition function and provide the theoretical guarantees for the proposed method and clarify its convergence property. Experimental results show that sparse models (up to 98\% sparsity) obtained by our proposed method outperform the SOTA sparse training methods on a wide variety of deep learning tasks. On VGG-19 / CIFAR-100, ResNet-50 / CIFAR-10, ResNet-50 / CIFAR-100, our method has even higher accuracy than dense models. On ResNet-50 / ImageNet, the proposed method has up to 8.2\% accuracy improvement compared to SOTA sparse training methods