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The relationship between medicine and video games is growing tremendously. In the field of medicine, realistic simulation and games have risen in popularity, and in turn, gamification has transformed the game elements into a non-gaming world for human engagement like motivation and performance. It is not surprising that game-based learning has branched out in the realm of the medical world. The person’s psychological state determines the effectiveness of education during training. This study aims to examine how the usage of immersive technology impacts users’ tendency to access immersive resources for learning during an emergency like the COVID-19 pandemic. Augmented reality (AR) apps have grown to be a popular tool in education nowadays. The purpose of using AR applications is to impart knowledge during the COVID-19 pandemic. An investigation was conducted to test the effectiveness of immersive technology in learning by developing a game-based experimental model and testing it on 100 non-randomly selected users of various ages. This study shows that users are open to new teaching approaches, including AR applications, in response to the challenges presented by the pandemic. AR applications provide a potential solution to the difficulties associated with education by providing an immersive and interesting experience that enhances learning-based results. This demonstrates that while using AR apps, an individual’s viewpoints and sense of control over their learning are more essential in influencing their conduct. By integrating AR apps into learning systems, immersive education may enhance users’ engagement, motivation and overall learning experiences.

Convenience sampling was chosen as the method for data analysis. One hundred users from a leading private university in the northwest part of India participated in this study. This gave a minimum sample size of 79 participants. To analyse the user experience (UX), a UX questionnaire was adopted. In this research paper, the researcher explores the importance of immersive games that emphasise awareness and experience through a series of questionnaires to assess the effective awareness of COVID through immersive technology, because the immersive element plays a major role in the quality and success of awareness through COVID-19 fighter video games as an interactive learning platform.

Results showed that most people do not know how to deal with an infected person in a critical situation; either they feel scared or deal without taking precautions. COVID-19 fighters are empowered with a virtual patient, which players can interact with. Once the user finishes the FPS game, he must find out the source of viruses that will be an AR-based virtual patient. The first step of the instruction will ask the user to give the mask to the infected person; in the second step, it will ask the user to sanitise his body. In the third step, it will ask to hospitalise.

The research offers empirical evidence on the effectiveness of augmented reality-based game approaches to increase reality in basic education to boost the awareness of individuals. The report also gives an example of good cross-cutting education materials that provide the player with a very valuable tool for understanding knowledge of covid awareness by playing the COVID-19 fighter game.

Smart card-based E-payment systems are receiving increasing attention as the number of implementations is witnessed on the rise globally. Understanding of user adoption behavior of E-payment systems that employ smart card technology becomes a research area that is of particular value and interest to both IS researchers and professionals. However, research interest focuses mostly on why a smart card-based E-payment system results in a failure or how the system could have grown into a success. This signals the fact that researchers have not had much opportunity to critically review a smart card-based E-payment system that has gained wide support and overcome the hurdle of critical mass adoption. The Octopus in Hong Kong has provided a rare opportunity for investigating smart card-based E-payment system because of its unprecedented success. This research seeks to thoroughly analyze the Octopus from technology adoption behavior perspectives.

Cultural impacts on adoption behavior are one of the key areas that this research posits to investigate. Since the present research is conducted in Hong Kong where a majority of population is Chinese ethnicity and yet is westernized in a number of aspects, assuming that users in Hong Kong are characterized by eastern or western culture is less useful. Explicit cultural characteristics at individual level are tapped into here instead of applying generalization of cultural beliefs to users to more accurately reflect cultural bias. In this vein, the technology acceptance model (TAM) is adapted, extended, and tested for its applicability cross-culturally in Hong Kong on the Octopus. Four cultural dimensions developed by Hofstede are included in this study, namely uncertainty avoidance, masculinity, individualism, and Confucian Dynamism (long-term orientation), to explore their influence on usage behavior through the mediation of perceived usefulness.

TAM is also integrated with the innovation diffusion theory (IDT) to borrow two constructs in relation to innovative characteristics, namely relative advantage and compatibility, in order to enhance the explanatory power of the proposed research model. Besides, the normative accountability of the research model is strengthened by embracing two social influences, namely subjective norm and image. As the last antecedent to perceived usefulness, prior experience serves to bring in the time variation factor to allow level of prior experience to exert both direct and moderating effects on perceived usefulness.

The resulting research model is analyzed by partial least squares (PLS)-based Structural Equation Modeling (SEM) approach. The research findings reveal that all cultural dimensions demonstrate direct effect on perceived usefulness though the influence of uncertainty avoidance is found marginally significant. Other constructs on innovative characteristics and social influences are validated to be significant as hypothesized. Prior experience does indeed significantly moderate the two influences that perceived usefulness receives from relative advantage and compatibility, respectively. The research model has demonstrated convincing explanatory power and so may be employed for further studies in other contexts. In particular, cultural effects play a key role in contributing to the uniqueness of the model, enabling it to be an effective tool to help critically understand increasingly internationalized IS system development and implementation efforts. This research also suggests several practical implications in view of the findings that could better inform managerial decisions for designing, implementing, or promoting smart card-based E-payment system.

Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias in AI-enabled ERP software customization. Although algorithmic bias in machine learning models has uneven, unfair and unjust impacts, research on it is mostly anecdotal and scattered.

As guided by the previous research (Akter et al., 2022), this study presents the possible design bias (model, data and method) one may experience with enterprise resource planning (ERP) software customization algorithm. This study then presents the artificial intelligence (AI) version of ERP customization algorithm using k-nearest neighbours algorithm.

This study illustrates the possible bias when the prioritized requirements customization estimation (PRCE) algorithm available in the ERP literature is executed without any AI. Then, the authors present their newly developed AI version of the PRCE algorithm that uses ML techniques. The authors then discuss its adjoining algorithmic bias with an illustration. Further, the authors also draw a roadmap for managing algorithmic bias during ERP customization in practice.

To the best of the authors’ knowledge, no prior research has attempted to understand the algorithmic bias that occurs during the execution of the ERP customization algorithm (with or without AI).

The power system is complex multi‐component dynamic system with many operational levels made up of a wide range of energy sources with many interaction points. Low frequency oscillations are observed when large power systems are interconnected by relatively weak tie lines. These oscillations may sustain and grow to cause system separation if no adequate damping is available. The present paper aims to propose an on‐line self‐learning PID (OLSL‐PID) controller in order to damp the low frequency power system oscillations in a single‐machine system.

The proposed OLSL‐PID is used as a controller in order to damp the low frequency power system oscillations. It has a local nature because of its powerful adaption process based on back‐propagation (BP) algorithm that is implemented through an adaptive self‐recurrent wavelet neural network identifier (ASRWNNI). In fact PID controller parameters are updated in on‐line mode, using BP algorithm based on the information provided by the ASRWNNI which is a powerful fast‐acting identifier because of its local nature, self‐recurrent structure and stable training algorithm with ALRs based on discrete lyapunov stability theorem.

The proposed control scheme is applied to a single machine infinite bus power system under different operating conditions and disturbances. The nonlinear time‐domain simulation results are promising and show the effectiveness and robustness of the proposed controller and also reveal that: because of the high adaptability, the local behavior and high flexibility of the OLSL‐PID controller, it can be damp the low frequency oscillations in the best possible manner and significantly improves the stability performance of the system.

The proposed controller adaption process is done in each sampling period using a powerful adaption law based on BP algorithm. Also during the process the system sensitivity is provided by a powerful fast‐acting identifier. As an alternative to multi‐layer perceptron neural network, self‐recurrent wavelet neural networks (SRWNNs) which combine the properties such as attractor dynamics of recurrent neural network and the fast convergence of the wavelet neural network were proposed to identify synchronous generator. Also to help the OLSL‐PID stability first, PID parameters tuning problem under a wide range of operating conditions is converted to an optimization problem which solved by a chaotic optimization algorithm (COA), and afterwards PID controller is hooked up in the system and on‐line tuning is done in each sampling period.

This chapter investigates the behavior of Reddit’s news subreddit users and the relationship between their sentiment on exchange rates. Using graphical models and natural language processing, hidden online communities among Reddit users are discovered. The data set used in this project is a mixture of text and categorical data from Reddit’s news subreddit. These data include the titles of the news pages, as well as a few user characteristics, in addition to users’ comments. This data set is an excellent resource to study user reaction to news since their comments are directly linked to the webpage contents. The model considered in this chapter is a hierarchical mixture model which is a generative model that detects overlapping networks using the sentiment from the user generated content. The advantage of this model is that the communities (or groups) are assumed to follow a Chinese restaurant process, and therefore it can automatically detect and cluster the communities. The hidden variables and the hyperparameters for this model are obtained using Gibbs sampling.

This chapter contributes to the ongoing debate about how digitalisation affects the internationalisation of small- and medium-sized firms (SMEs). By applying the Uppsala Internationalisation Process model, this chapter examines the impact of e-commerce on the internationalisation of SMEs. The study uses a unique dataset, which includes 14,513 SMEs across several sectors in 34 countries. The results show that firms using the Internet as a means to provide information about the firm exhibit a higher degree of internationalisation, while using the Internet to facilitate transactions was found to have a positive impact on the ratio of foreign sales to the total sales; however, these foreign sales are likely to be concentrated in less regions/markets. Furthermore, perceived export barriers were found to be a significant moderator of the effects of e-commerce usage on international intensity and international diversification. This suggests that e-commerce does not automatically facilitate the internationalisation of SMEs.