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This paper aims to investigate how user expectations affect value-added service (VAS) investment and pricing decisions of two-sided platforms. It draws on the information asymmetry theory and offers suggestions on how platform operators can manage user expectations.

According to the game theory, this study considers three user expectations (responsive, passive and wary). By framing the Hotelling duopoly model and comparing the VAS investment, price and platform profits, the optimal platform decision is analyzed and discussed.

The conclusions demonstrate that the monopolistic two-sided platform obtains more profits from the informed users with responsive expectations than uninformed users with passive or wary expectations. The marginal investment cost and cross-network externalities are two key factors that determine the platform's VAS investment and pricing strategies of passive or wary users. Furthermore, considering the expectation preferences, i.e. the uniformed users hold wary expectations with more information and hold passive expectations with less or no information, the results suggest that the proportion of wary users to all uninformed users increases the platform's VAS investment, profits and the price of informed users, and increase (decrease) the price of uninformed users when the cross-network externalities of informed users are relatively small (larger).

These results can provide insightful enlightenment into how platform operators utilize bilateral users' expectations and information level to guide their VAS investment and pricing decisions.

This paper is one of the first to explore the impact of three user expectations and the heterogeneity of preferences in informing users' passive or wary expectations, based on different levels of information on the decision-making of two-sided platforms regarding VAS.

The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion.

A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction.

Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction.

The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

This paper aims to provide advanced market managers in Europe, the USA or Japan with a long-term framework to prepare for and seize emerging market innovation opportunities.

This paper is based on eight years of studying emerging market innovation opportunities, how to prepare for them and how to seize them. This study mainly draws on experiential learning in the automotive sector, particularly in China.

The framework details the strategic co-evolution of advanced market firms, emerging market governments, customers and competitors through four stages of innovation. Emerging markets such as China and India at different stages, depending on the national context of development as well as the particular market context.

This research is primarily based on cases in the automotive sector. Challenging the ongoing assumption of superior advanced market innovation, this paper contributes to insights on the new innovation world order and how to invest in emerging market innovation opportunities.

The framework helps advanced market managers identify the strategic stage they are in, depending on the emerging market context. It provides them with long-term strategic insights, helping them anticipate moves and countermoves by emerging market governments and competitors, as well as anticipate the rapidly shifting needs of emerging market customers.

The four-stage framework brings together advanced market firms, emerging market governments, customers and competitors in a clear big picture. It conditions emerging market success in the even bigger picture of advanced market firms drawing on their learning in emerging markets in stages 3 and 4 to accelerate the renewal of their core business.

Based on a stimulus–organism–response theory, the purpose of this study is to empirically examine structural equation model linking personal involvement, positive emotions, tourist satisfaction and destination loyalty among sport tourists to a mega sport event. Moreover, moderating effects of place attachment on the relation between the aforementioned variables were investigated.

The validities and reliabilities of the measures were investigated through confirmatory factor analysis, Cronbach's alpha and correlation analysis. A structural equation modelling with maximum likelihood estimation was tested to analyze the relationships among the research variables using 383 participants.

The results revealed positive associations among stimulus (personal involvement), organism (positive emotions) and response (tourist satisfaction and destination loyalty). Moreover, the authors found moderating effect of place attachment on the relationships between personal involvement and positive emotions, personal involvement and tourist satisfaction and tourist satisfaction and destination loyalty.

This study holds the potential to aid destination managers in acquiring a more profound comprehension of how personal involvement contributes to elicit positive emotions, keep tourists satisfied and build destination loyalty as well as demonstrating the moderating roles of place attachment. However, generalizing the findings to alternative contexts presents a formidable challenge. Enhancing the applicability of these findings could be achieved through prospective research endeavors that explore visitors in diverse cities spanning various continents.

The study contributed to the literature by providing empirical evidence that personal involvement evokes positive emotions while also plays significant role in improving satisfaction and loyalty. Given the importance of experiences in sport tourism, this study also confirmed the role of positive emotions on tourist satisfaction and destination loyalty. Additionally, this study examined the moderating effect of place attachment, which has not been investigated in sport tourism context.

Using perspectives from the technology affordance and social capital theories, this study aims to unpack the process through which platform-enabled co-development unfolds in supply chain contexts. Specifically, it explores how innovation outcomes can be fostered through platform affordances and supply chain relationship (SCR) capital.

The paper integrates literature on digital platforms, SCRs and co-development to produce an integrative framework, developing propositions on the relationships among digital platforms, SCR capital and innovation outcomes.

The authors identify affordances for distinctive strategic use of platforms: value co-creation, relationship building and strategic learning. The authors discuss ways in which each affordance contributes to the advances in SCR capital, thus altogether enabling focal firms to orchestrate and integrate internal and external resources to attain incremental and radical innovation.

Based on the proposed research framework, further empirical studies can use quantitative data to measure the relationship between affordances and SCR capital and use longitudinal case studies to explore how affordances and SCR capital evolve to provide more fine-grained and contextualised information in different research settings.

This paper sheds light on how the relation between the adoption of digital platforms and SCR capital shapes digitally enabled service co-development. The authors provide an alternative explanation of resource integration in platform-mediated supply chain contexts and enrich the related literature on how digital platforms can maximise value from introducing ambidextrous innovation by leveraging internal and external resources.