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Humanoid social robots (HSRs) are an innovative technology revitalizing various service sectors, such as the hospitality industry. However, limited research has explored how anthropomorphic features of HSRs influence user satisfaction with the services delivered by HSRs. To address this, a research model was proposed to evaluate how three distinct anthropomorphic features: appearance, voice and response, impact the perceived values (i.e. utilitarian, social and hedonic values) of HSRs, which, in turn, influence user satisfaction.

Data from an online survey of hotel customers was utilized to test the research model (N = 509).

The results indicated that appearance, voice, and response affect perceived utilitarian, hedonic and social values differently. The response feature of HSRs demonstrated the strongest impact on perceived utilitarian, social and hedonic values. In addition, voice affected all three perceived values, while appearance only affected perceived utilitarian and social values. Furthermore, perceived utilitarian, hedonic and social values showed positive impacts on user satisfaction, with hedonic value being the most influential factor.

This study contributes to the literature on HSRs and anthropomorphism by explaining how different anthropomorphic features affect users’ value perceptions and user satisfaction with HSR services by utilizing the stimulus-organism-response (SOR) framework.

The purpose of this study is to examine how the interplay of stressor (e.g. fear of missing out, FoMO) and strains (e.g. perceived social overload, communication overload, information overload and system feature overload) in social networking sites (SNS) use can contribute to users’ SNS fatigue from a configurational view.

Data were collected among 363 SNS users in China via an online survey, and fuzzy-set qualitative comparative analysis (fsQCA) was applied in this study to scrutinize the different combinations of FoMO and overload that contribute to the same outcome of SNS fatigue.

Six combinations of casual conditions were identified to underlie SNS fatigue. The results showed that FoMO, perceived information overload and system feature overload are the core conditions that contribute to SNS fatigue when combined with other types of overloads.

The current work supplements the research findings on SNS fatigue by identifying the configurations contributing to SNS fatigue from the joint effects of stressor (FoMO) and strain (perceived social overload, communication overload, information overload and system feature overload) and by providing explanations for SNS fatigue from the configurational perspective.

Although knowledge sharing in online communities has been studied for many years, little is known about the determinants for individuals' knowledge sharing in online health communities (OHCs) surrounding smoking cessation. Examining the determinants of knowledge sharing in such OHCs from the social capital perspective may prove particularly enlightening.

A questionnaire-based online user survey of two smoking cessation OHCs, one based in Finland and one based in China, was performed. Performing data analysis with partial least squares (SmartPLS 3.0), the authors developed a model conceptualizing the structural, cognitive and relational dimensions of social capital as drivers for knowledge sharing in smoking cessation OHCs, with users' stage in giving up smoking as a moderator.

The results show that structural capital (social ties) and relational capital (reciprocity) are important motivators behind knowledge sharing in smoking cessation OHCs, and the authors found a moderating effect of the stage in quitting on the antecedents' relationship with knowledge sharing in these OHCs.

The study enriches understanding of knowledge sharing in smoking cessation OHCs, contributing to theory and identifying practical implications for such groups' administration.

To cope with the COVID-19 pandemic, contact tracing mobile apps (CTMAs) have been developed to trace contact among infected individuals and alert people at risk of infection. To disrupt virus transmission until the majority of the population has been vaccinated, achieving the herd immunity threshold, CTMA continuance usage is essential in managing the COVID-19 pandemic. This study seeks to examine what motivates individuals to continue using CTMAs.

Following the coping theory, this study proposes a research model to examine CTMA continuance usage, conceptualizing opportunity appraisals (perceived usefulness and perceived distress relief), threat appraisals (privacy concerns) and secondary appraisals (perceived response efficacy) as the predictors of individuals' CTMA continuance usage during the pandemic. In the United States, an online survey was administered to 551 respondents.

The results revealed that perceived usefulness and response efficacy motivate CTMA continuance usage, while privacy concerns do not.

This study enriches the understanding of CTMA continuance usage during a public health crisis, and it offers practical recommendations for authorities.

An empirical study investigated the antecedents to perceived usefulness (PU) and its consequences in the context of smoking cessation online health communities (OHCs).

To validate a research model for perceived informational support, perceived emotional support and perceived esteem support, the authors conducted a partial-least-squares analysis of empirical data from an online survey (N = 173) of users of two smoking cessation OHCs. The proposed model articulates these as antecedents to PU from a social support perspective, and knowledge sharing and continuance intention are expressed as consequences of PU.

The empirical study identified that the PU of smoking cessation OHCs is influenced by perceived emotional support and perceived esteem support, and perceived informational support indirectly affects PU via these factors. In turn, PU exerts a positive influence on both knowledge sharing and continuance intention. Also, knowledge sharing positively affects continuance intention.

The study contributes to scholarship on users' postadoption behavior in the context of smoking cessation OHCs by disentangling the antecedents to PU from a social support perspective and pinpointing some important consequences of PU. The research also has practical implications for managing smoking cessation OHCs.

The paper aims to explore how perceived prosumer content quality (PPCQ) and perceived interaction quality (PIQ) improve users' co-creation experiences and subsequently influence their co-creation intentions in the future. In addition, the paper examines users' prosumer ability into consideration.

The research model based on stimulus-organism-response (S-O-R) paradigm is developed to observe users' participation in value co-creation activities. In total, 318 valid responses were collected from a survey. Structural equation modeling was used to examine the model and Statistical Package for the Social Sciences (SPSS) PROCESS macro (Model 58) by Hayes was applied to investigate the moderating effect of prosumer ability in mediation paths.

It is observed that co-creation intention is determined by user-learning value, social-integrative value and hedonic value, which are influenced by PPCQ and PIQ. Besides, uses' prosumer ability moderates the indirect effects of PPCQ and PIQ on co-creation intentions through co-creation experiences.

The paper provides a prosumption perspective to explain users' co-creation intentions in social commerce and proposes the importance of user-learning, social-integrative and hedonic values in determining co-creation intentions.

Social commerce platforms can encourage prosumption activities and cultivate multi-level prosumers to achieve a win–win situation.

Little prior research has explicitly examined how and why users participate in value co-creation activities in social commerce from prosumption perspective. The current paper seeks to fill this gap and open new avenues for other value co-creation researchers.

Four decades ago, the Swedish economist, Gunnar Myrdal (1970, p. 230) attributed the paucity of research on corruption in South Asia to the research taboo on this topic. Fortunately, this taboo has been gradually eroded since the 1990s as reflected in the tremendous amount of research that has been done on corruption in the Asia-Pacific countries. Corruption has emerged in the 1990s as “a truly global political issue eliciting a global political response” (Glynn, Kobrin, & Naim, 1997, p. 7). Indeed, the globalization of corruption has given rise to an overriding concern with how to combat corruption in many countries among their governments and many international agencies. Consequently, many international organizations like the Asian Development Bank, Commonwealth Association for Public Administration and Management, Eastern Regional Organization for Public Administration, International Institute for Administrative Sciences, Organization of American States, Organization for Economic Co-operation and Development, Transparency International, United Nations Development Programme, World Bank, and World Economic Forum have organized numerous conferences, symposia and workshops on various aspects of corruption.

The above quotations highlight the adverse consequences of corruption in many countries around the world today. Indeed, the research taboo on corruption, which Gunnar Myrdal identified in 1968, no longer exists, and the silence on the “C” word (corruption) in the World Bank was broken by James Wolfensohn in his famous October 1996 speech, which focused on the negative consequences of the “cancer of corruption” on the World Bank's aid programs.

Immersion is one of the key steps during the preparation of silane-based hybrid films, which has important effects on the performance of films after curing. In this paper, the formation process of Zr-doped silane film (i.e. the adsorption of silane and deposition of zirconium compounds) on carbon steel immersed in Zr(NO₃)₄/silane mixed solutions was investigated.

The method of in situ monitoring the open circuit potential of a two-electrode system, consisting of carbon steel and saturated calomel electrode, was used. The effects of immersion conditions (i.e. the concentration of Zr(NO₃)₄ and pH of Zr(NO₃)₄/silane mixed solution) on the open circuit potential were investigated in detail. Furthermore, the surface coverage rate of different cured films (i.e. Zr cured film, silane cured film and Zr/silane composite cured film) after curing on carbon steel was calculated according to the results of polarization curves. Electrochemical impedance spectroscopy (EIS) was used to study the self-healing property of Zr-doped silane cured film.

The results indicate that in Zr(NO₃)₄/silane mixed solutions, most zirconium compounds deposit on the surface of carbon steel at the initial immersing stage, then the adsorption of silane on the residual surface of carbon steel dominates the following immersing stage. EIS results show that the Zr-doped cured film has improved self-healing property.

First, the method of in situ monitoring the open-circuit potential of two-electrode system was applied to investigate the deposition of Zr and the adsorption of silane on carbon steel immersed in Zr(NO₃)₄/silane mixed solutions. Second, the formation process of Zr-doped silane film was proposed.

This study aims to propose a novel method based on model learning with sparsity inducing norms for estimating dynamic gravity terms of the serial manipulators. This method is realized by operating the robot, acquiring data and filtering the features in signal acquisition to adapt to the dynamic gravity parameters.

The core principle of the method is to analyze the dictionary composition of the basis function of the model based on the dynamic equation and the Jacobian matrix of an arm. According to the structure of the basis function and the sparsity of the features, combined with joint-angle and driving-torque data acquisition, the effective features of dynamic gravity parameters are screened out using L1-norm optimization and learning algorithms.

The theoretical analysis revealed that training data obtained based on joint angles and driving torques could rapidly update dynamic gravity parameters. The simulation experiment was carried out by using the publicly available robot model and compared with the previous disassembly method to evaluate the feasibility and performance. The real 7-degree of freedom (DOF) industrial manipulator was used to further discuss the effects of the feature selection. The results show that this estimation method can be fully operational and efficient in industrial applications.

This approach is applicable to most serial robots with multi-DOF and the dynamic gravity parameters of the robot are estimated through learning and optimization. The method does not require prior knowledge of the robot arm structure and only requires joint-angle and driving-torque data acquisition under low-speed motion. Furthermore, as it is a data-driven-based method, it can be applied to gravity parameters updating.

Different from previous general robot dynamic modelling methods, the sparsity of the analytical form of dynamic equations was exploited and model learning was formulated as a convex optimization problem to achieve effective gravity parameters screening. The novelty of this estimation approach is that the method does not only require any prior knowledge but also does not require a specifically designed trajectory. Thus, this method can avoid the laborious work of parameter calibration and the induced modelling errors. By using a data-driven learning approach, the new parameter updating process can be completed conveniently when the robot carries additional mass or the end-effector changes for different tasks.