How transformation expectation leads consumers to immediate gratification - A PLS-SEM approach

This study explores the mechanism which triggers consumer's immediate gratification behavior. It is proposed that consumer's expectation of meaningful life transformation by acquisition of a product causes her perception of product hedonic and utilitarian value, which can further predict immediate gratification. The positive impact of perception of hedonic value on immediate gratification can be mediated by price sensitivity and moderated by materialism level. The structural model is established for further empirical analysis with PLSSEM approach. The model suggests different domain of transformation expectation may have conflicting impact on immediate gratificatio

UNDERSTANDING POST ADOPTION SWITCHING BEHAVIOR FOR MOBILE INSTANT MESSAGING APPLICATION IN CHINA: BASED ON MIGRATION THEORY

Post adoptive IT use is a hot research stream in information systems field, including continuance behaviours and switching behaviours. While there are a great number of studies on users’ intentions or behaviors for diversified information systems, previous post adoptive IT studies pay relatively less attention on users’ switching behaviors. Hence, we know little about this phenomenon and triggers on users’ switching behaviors. This research identifies the features of users IT switching behaviors and examines what trigger their switching intentions and actual behaviors in the context of mobile instant messaging (MIM) application in China. A model of MIM switching behaviors is developed based on Curran and Saguy’s (2001) research on how networks of obligation, trust and relative deprivation affect human’s migration decision and process. Besides these three triggers, we also introduce dissatisfaction and curiosity into our model according to prior IS studies on switching behaviors. A survey research method will be adopted to test this model. Overall, our study may theoretically contribute to further understand users’ IT switching behaviors and yield some practical implications for designers and managers in MIM providers and their products propaganda

The market value of sustainable practices in the luxury industry:An identity mismatch and institutional theoretical perspective

Shareholders of luxury firms uphold a view of identity mismatch between being luxury and sustainable. We examine the associated market value with sustainable practice adoption of luxury firms from an institutional theoretical lens and an identity mismatch perspective. Based on 289 announcements made by public luxury firms, results from event study show that the stock market reacts negatively to the announcements of sustainable practices. Nevertheless, the negative effect attenuates in more recent year announcements, and more profitable and smaller luxury firms. Our results alert managers to better align their sustainability goals with luxury firms' identity and the ever-changing environment

Routing in Socially Selfish Delay Tolerant Networks

Abstract—Existing routing algorithms for Delay Tolerant Networks (DTNs) assume that nodes are willing to forward packets for others. In the real world, however, most people are socially selfish; i.e., they are willing to forward packets for nodes with whom they have social ties but not others, and such willingness varies with the strength of the social tie. Following the philosophy of design for user, we propose a Social Selfishness Aware Routing (SSAR) algorithm to allow user selfishness and provide better routing performance in an efficient way. To select a forwarding node, SSAR considers both users ’ willingness to forward and their contact opportunity, resulting in a better forwarding strategy than purely contact-based approaches. Moreover, SSAR formulates the data forwarding process as a Multiple Knapsack Problem with Assignment Restrictions (MKPAR) to satisfy user demands for selfishness and performance. Trace-driven simulations show that SSAR allows users to maintain selfishness and achieves better routing performance with low transmission cost. I

Utilizing Multiple Inputs Autoregressive Models for Bearing Remaining Useful Life Prediction

Accurate prediction of the Remaining Useful Life (RUL) of rolling bearings is crucial in industrial production, yet existing models often struggle with limited generalization capabilities due to their inability to fully process all vibration signal patterns. We introduce a novel multi-input autoregressive model to address this challenge in RUL prediction for bearings. Our approach uniquely integrates vibration signals with previously predicted Health Indicator (HI) values, employing feature fusion to output current window HI values. Through autoregressive iterations, the model attains a global receptive field, effectively overcoming the limitations in generalization. Furthermore, we innovatively incorporate a segmentation method and multiple training iterations to mitigate error accumulation in autoregressive models. Empirical evaluation on the PMH2012 dataset demonstrates that our model, compared to other backbone networks using similar autoregressive approaches, achieves significantly lower Root Mean Square Error (RMSE) and Score. Notably, it outperforms traditional autoregressive models that use label values as inputs and non-autoregressive networks, showing superior generalization abilities with a marked lead in RMSE and Score metrics

The impact of evidence type and message framing on promoting HPV vaccination in online health communities

Message features and type are crucial in health-related communication, especially due to the potential impact these messages can have on an individual's health. This study uses a 2 ' 2 experimental design (evidence type: statistical evidence vs. narrative evidence; message framing: gain-framed message vs. loss-framed message), to investigate how evidence type and message framing affect the attitudes, health beliefs, and intentions of college students in online health communities, regarding getting the HPV vaccination. Preliminary results (N=300) indicated that; (1) evidence type and message framing both influence attitudes and intentions significantly; Statistical evidence will lead to more favorable views than narrative evidence, and loss-framed messages will lead to more favorable views than gain-framed messages. (2) Concerning the interactions, we used construal level theory and found that, for gain-framed message, narrative evidence will lead to more favorable attitudes, free intentions, perceived benefits and barriers of HPV vaccination than statistical evidence; for loss-framed message, statistical evidence will lead to more favorable attitudes, intentions, perceived seriousness, benefits and barriers of HPV vaccination than narrative evidence

Utilizing VQ-VAE for End-to-End Health Indicator Generation in Predicting Rolling Bearing RUL

The prediction of the remaining useful life (RUL) of rolling bearings is a pivotal issue in industrial production. A crucial approach to tackling this issue involves transforming vibration signals into health indicators (HI) to aid model training. This paper presents an end-to-end HI construction method, vector quantised variational autoencoder (VQ-VAE), which addresses the need for dimensionality reduction of latent variables in traditional unsupervised learning methods such as autoencoder. Moreover, concerning the inadequacy of traditional statistical metrics in reflecting curve fluctuations accurately, two novel statistical metrics, mean absolute distance (MAD) and mean variance (MV), are introduced. These metrics accurately depict the fluctuation patterns in the curves, thereby indicating the model's accuracy in discerning similar features. On the PMH2012 dataset, methods employing VQ-VAE for label construction achieved lower values for MAD and MV. Furthermore, the ASTCN prediction model trained with VQ-VAE labels demonstrated commendable performance, attaining the lowest values for MAD and MV.Comment: 17 figure

Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems

Although AI has significant potential to transform society, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and guidelines for responsible AI have been issued recently. However, these principles are high-level and difficult to put into practice. In the meantime much effort has been put into responsible AI from the algorithm perspective, but they are limited to a small subset of ethical principles amenable to mathematical analysis. Responsible AI issues go beyond data and algorithms and are often at the system-level crosscutting many system components and the entire software engineering lifecycle. Based on the result of a systematic literature review, this paper identifies one missing element as the system-level guidance - how to design the architecture of responsible AI systems. We present a summary of design patterns that can be embedded into the AI systems as product features to contribute to responsible-AI-by-design