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Massively multiplayer online role-playing games (MMORPGs) create quasi-real social systems in which players can interact with one another, and quasi-real economic systems where players can consume and trade in-game items with virtual currency. The in-game currency price, an important indicator of a virtual economy, is highly contingent on players’ behavioral interaction in MMORPGs. The purpose of this paper is to adopt a network perspective to examine how topological characteristics of social networks in an MMORPG, namely, network externalities, density, and closure, would exert impacts on the in-game currency price.

Players’ behavioral data were collected from a popular MMORPG in China on a weekly basis for 52 weeks. With a time series analytical approach, the empirical model for the price function of in-game currency was estimated with vector autoregression.

The results show that the number of core avatars and network density are positively associated with in-game currency price, while network closure has a negative effect on in-game currency price. However, in-game currency price is found to have no significant relationship with the trade volume of the currency.

This study fills in an important research gap by investigating factors influencing the in-game currency price of MMORPGs from a network perspective, which contributes to the existing literature of network effects and advances our understanding about how players’ interaction will influence the dynamics of a virtual economy. The findings could offer useful insights for online game companies to better understand their players’ social interaction and consumption behavior.

The study of the history of Chinese science has attracted further attention over the past 50 years, with an increasing number of scholars, influenced by Joseph Needham and his project “Science and Civilization in China”, developing ideas in this field. There are a range of Web sites which have been produced and are available to the scholar of the history of Chinese science, and some of these are collected together and evaluated in this article.

Institutional actors are critical allies for grassroots movements, but few studies have examined their effects and variations within the non-democratic context. This chapter argues that while institutional allies are heavily constrained and unlikely to give open endorsement to grassroot activists, some institutional activists indirectly facilitate movement mobilization and favorable outcomes in the process of advancing their own political agendas. Drawing upon in-depth interviews conducted in 2008 and 2012, I illustrate this argument by examining the Anti-PX Movement – a landmark grassroots environmental movement against a chemical plant – in Xiamen, China. I find that the environmental institutional actors were constrained and divided, yet some still fostered opportunities for movement mobilization and in turn exploited the opportunity created by the protesters to pursue their policy interests, thus facilitating positive movement outcomes. As long as the claims are not politically subversive to the authoritarian rule, this type of tacit and tactical interaction between institutional activists within the state and grassroot activists on the street is conducive to promoting progressive policy changes.

The purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.

Vibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.

The obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.

The proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.

In this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

Boredom is believed to be the common cause of workers' absenteeism, accidents, job dissatisfaction, and performance variations in manufacturing environments with repetitive jobs. Effectively measuring and possibly predicting job boredom is the key to the design and implementation of appropriate strategies to deal with such undesirable emotional state. The purpose of this paper is to present new methodologies to measure and predict human boredom at work.

Two series of mathematical formulations, linear and nonlinear, to describe the variation of human boredom at work are first presented. Given the complexity of human emotions, the authors also present a probabilistic framework based on state‐of‐the‐art Bayesian networks to model employees' boredom at work.

The proposed methods centre on the prediction and measurement of human boredom at work. They enable managers to take proactive actions to deal with human boredom at work. Examples of such actions are task rotation and job redesign.

The proposed methods are verified using a number of cases describing a set of phenomena that may occur in the real world. However, further research is required to demonstrate the validity of the models using real world data.

According to accessible literature, human boredom is being measured by self reporting scales thus far. This study describes and demonstrates analytical approaches to model human boredom at work.

As one of its major strategies, China has made a new plan to further expand High Standard Farmland (HSF) to all permanent basic farmland (80% of total farmland) for grain security over the next decade. Yet, what will be the impact of farmland infrastructure investment on agrifood systems? The paper aims to systematically evaluate the multiple effects (food security, economy, nutrition and environment) of expanding HSF construction under the context of the “Big Food vision” using an interdisciplinary model.

An interdisciplinary model – AgriFood Systems Model, which links the China CGE model to diet and carbon emission modules, is applied to assess the multiple effects of HSF construction on agrifood systems, such as food security and economic development, residents’ diet quality and carbon emissions. Several policy scenarios are designed to capture these effects of the past HSF investment based on counterfactual analysis and compare the effects of HSF future investment at the national level under the conditions of different land use policies – restricting to grain crops or allowing diversification (like vegetables, and fruit).

The investments in HSF offer a promising solution for addressing the challenges of food and nutrition security, economic development and environmental sustainability. Without HSF construction, grain production and self-sufficiency would decline significantly, while the agricultural and agrifood systems’ GDP would decrease. The future investment in the HSF construction will further increase both grain production and GDP, improve dietary quality and reduce carbon emissions. Compared with the policy of limiting HSF to planting grains, diversified planting can provide a more profitable economic return, improve dietary quality and reduce carbon emissions.

This study contributes to better informing the impact of land infrastructure expanding investment on the agrifood systems from multiple dimensions based on an interdisciplinary model. We suggest that the government consider applying diversified planting in the future HSF investment to meet nutritional and health demands, increase household income and reduce carbon emissions.

Intellectual experiences focus on users’ information processing and critical thinking toward stimuli. The deployment of humanoid service robots as novel stimuli in tourism and hospitality has influenced users’ perceptions and may affect their intellectual engagement. This paper aims to connect four contemporary theoretical concepts: the service robot acceptance model, technological fear, the uncanny valley theory and the stereotype content model, to investigate users’ perceptions and intellectual experiences toward humanoid service robots.

Scale development procedures were conducted: literature review, checking face and content validity, factorizing items and dimensions, achieving construct and criterion validity and testing predictive validity.

Through literature review and free-response tasks, 43 measurement items were generated. Next, 1,006 samples from two cross-cultural groups refined the scale. Finally, a reliable and valid scale with four dimensions measuring users’ perceptions of humanoid service robots was determined.

Humanoid service robots should be designed to enhance functionality and innovativeness while minimizing stiffness, inflexibility, unsafety and danger to improve users’ intellectual engagement.

This study provides a novel examination of users’ intellectual experiences toward humanoid service robots by connecting four contemporary theories of users’ perceptions. This study enriches human–robot experience through an integrated perspective and presents a rigorous examination of the scale’s psychometric properties. A reliable and valid scale for measuring users’ perceptions toward humanoid service robots fills the gaps and serves as an effective predictor of intellectual experience in human–robot literature.

Customer perceptions toward hybrid human–robot teams remain largely unexplored. We focus on the impact of frontline robots’ (FLRs) automated social presence (ASP) on customers’ perceived teamwork quality, and ultimately frontline employees’ (FLEs) competence and warmth. We explore the role of interrogation as a relevant contingency. We complement the customer view with insights into the FLEs’ viewpoint.

We manipulate FLR’s ASP cues (speech and identity) in a hybrid team in four business-to-consumer (B2C) video-based experiments and collect data from online participants. We combine these with one business-to-business (B2B) field survey which collected data from FLEs working in hybrid teams.

When FLR’s ASP increases, customers more positively evaluate teamwork quality, ultimately affecting FLEs’ competence and warmth. FLEs who correct (interrogate) robotic mistakes strengthen the positive effect of FLRs’ ASP on teamwork quality. When FLRs correct FLEs, ASP’s effect on teamwork quality is also strengthened, while FLEs are not “punished” for erring. In contrast, FLEs themselves do perceive corrections as detrimental to teamwork quality. We term this the hybrid team evaluation paradox.

We recommend that firms deploy hybrid teams equipped with high-ASP FLRs (name and speech suffice). FLEs should be trained, and FLRs programmed, to appropriately use interrogation. Managers should pay attention to the paradox, given the conflicting perceptions toward interrogative behaviors.

We advance the hybrid teams literature by drawing on ASP, social cognition and collective mindfulness theories and behaviors that ameliorate customer perceptions. Our results support using FLRs to enhance FLEs’ capabilities.

The purpose of this paper is to investigate the effect of inter-platform competition on the adoption of different broadband technologies (i.e. among xDSL, fibre-optic technologies, and hybrid fibre coaxial (HFC)), examine the direction of the effect, and identify potential technology convergence and the speed of technology innovation.

It uses Lotka-Volterra equation to determine the dynamic competition pattern for xDSL, fibre-optic technologies, and HFC.

The influence of inter-platform competition on the adoption rate may vary depending on the market conditions, the phase of the adoption period, and the types of competing technology. Even though new technology has competitive advantage, it still requires time to acquire market share. Even though fibre-optic is leading in the market, alternative technologies have also garnered significant market share in the early stage. Specifically, HFC has gained its own market position, making it a valuable alternative in the short term. Nonetheless, the market will eventually converge to fibre-optics.

The findings show that inter-platform competition does not always exert positive influence on broadband adoption as indicated in previous literature. Instead, the influence may vary from negative to neural. This information is an important knowledge addition to the literature. Overall, the study has important implications to governmental effort in managing market competitions and in planning national broadband infrastructure policies. It also provides valuable implications on how ISPs should strategize their investment in new broadband technologies.