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Due to the rapidly increasing volume of users and products in virtual worlds, recommender systems are an important feature in virtual worlds; they can help solve information overload problems. Virtual world users are able to perform several actions that promote the enjoyment of their virtual life, including interacting with others, visiting virtual houses and shopping for virtual products. This study aims to concentrate on the following two important factors: the social neighbors’ influences and the virtual house bandwagon phenomenon, which affects users’ preferences during their virtual house visits and purchasing processes.

The authors determine social influence by considering the interactions between the target user and social circle neighbors. The degree of influence of the virtual house bandwagon effect is derived by analyzing the preferences of the virtual house hosts who have been visited by target users during their successive visits. A novel hybrid recommendation method is proposed herein to predict users’ preferences by combining the analyses of both factors.

The recommendation performance of the proposed method is evaluated by conducting experiments with a data set collected from a virtual world platform. The experimental results show that the proposed method outperforms the conventional recommendation methods, and they also exhibit the effectiveness of considering both the social influence and the virtual house bandwagon effect for making effective recommendations.

Existing studies on recommendation methods did not investigate the virtual house bandwagon effects that are unique to the virtual worlds. The novel idea of the virtual house bandwagon effect is proposed and analyzed for predicting users’ preferences. Moreover, a novel hybrid recommendation approach is proposed herein for generating virtual product recommendations. The proposed approach is able to improve the accuracy of preference predictions and enhance the innovative value of recommender systems for virtual worlds.

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.

The current chapter focuses on environmental and organizational factors that affect the performance appraisal context, performance evaluations, and rating accuracy. Drawing on the extant literature and focusing on current organizational practices, we propose a dynamic multi-level model of performance rating that takes these distal factors into consideration. In doing so, we also provide propositions explicating causal linkages between these distal factors, more proximal performance appraisal factors, and ultimately the accuracy of performance ratings. Furthermore, we identify current and emerging directions in performance appraisal research and practice. The implications of the current and emerging trends are then discussed in the context of our proposed model.

Online advertisement brings huge revenue to many websites. There are many types of online advertisement; this paper aims to focus on the online banner ads which are usually placed in a particular news website. The investigated news website adopts a pay-per-ad payment model, where the advertisers are charged when they rent a banner from the website during a particular period. In this payment model, the website needs to ensure that the ad pushed frequency of each ad on the banner is similar. Under such advertisement push rules, an ad-recommendation mechanism considering ad push fairness is required.

The authors proposed a novel ad recommendation method that considers both ad-push fairness and personal interests. The authors take every ad’s exposure time into consideration and investigate users’ three different usage experiences in the website to identify the main factors affecting the interests of users. Online ad recommendation is conducted on the investigated news website.

The results of the experiments show that the proposed approach performs better than the traditional approach. This method can not only enhance the average click rate of all ads in the website but also ensure reasonable fairness of exposure frequency of each ad. The online experiment results demonstrate the effectiveness of this approach.

Existing researches had not considered both the advertisement recommendation and ad-push fairness together. With the proposed novel ad recommendation model, the authors can improve the ad click-through rate of ads with reasonable push fairness. The website provider can thereby increase the commercial value of advertising and user satisfaction.

Emotional intelligence (EI) has been known to play an important role in teaching for long. Interactions, teaching environment, and emotional responses of students and instructors all have a demonstrable, complex interplay which spills over to behaviour. Particularly predictive and powerfully pattern-inducing in this regard, are emotional responses to events in the external environment. COVID-19 was a critical disruption in the teaching environment on account of its far-reaching effects over the modes and contents of instruction. Thus, there is a clear and present need to connect the emotional responses among students and instructors due to COVID-19 with the practice and interactions occurring during teaching. The authors present a narrative analysis based on qualitative inputs from instructors in a graduate course setting to find the effects of emotional responses to COVID-19 on teaching virtually. The authors bring in the concept of EI to explain the observations made from the analysis. The conclusions drawn are of direct and immediate importance for the future of teaching and learning in times of disruptions such as COVID-19. The study contributes by updating the knowledge base on emotion management in the classroom on the one hand, while adding to newer streams of research on virtual classroom settings and disruption-induced changes in teaching on the other hand. Some significant directions for praxis of business are also included.

The purpose of this paper is to investigate how consumers’ luxury purchase behavior has been affected by COVID-19. A theoretical framework is proposed to determine how isolation leads to intention to purchase luxury brands through bandwagon luxury consumption behavior. Additionally, the moderating effects of COVID-19 anxiety and social capital on the relationship between bandwagon luxury consumption behavior and subjective well-being and intention to purchase luxury brands are tested.

Survey responses from a national sample of 261 luxury consumers in the USA were collected. The data were analyzed using a covariance-based structural equation modeling technique.

The results confirm that the feeling of isolation leads to a higher intention to purchase luxury brands. Both COVID-19 anxiety and social capital moderate the relationship between bandwagon luxury consumption behavior and intention to purchase luxury brands/subjective well-being related to the luxury brand purchase.

Luxury marketers should focus on highlighting bandwagon elements of their brands, such as their popularity and how they enhance social connectedness when tailoring their brand communication to isolated consumers. The data is limited to luxury consumers in the USA; thus, the findings are specific to the US market.

Given the paucity of research on luxury consumption for isolated consumers, this study adds to the literature on luxury brands by examining how the feeling of isolation affects the intention to purchase luxury brands.

Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.