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This paper reports the development of the Hong Kong consumer satisfaction index (HKCSI) – a new type of consumer‐oriented economic performance indicator representing the quality of products (commodities and services) sold in Hong Kong as evaluated by Hong Kong consumers – as well as the findings of HKCSI from 1998 to 2000, each year with more than 10,000 successful telephone interviews on about 60 products. According to Hong Kong's special economic structure, the HKCSI is constructed from a consumption point of view concentrating on products and the products’ consumers, rather than from the popular production point of view focusing on firms and the firms’ customers. Key features of HKCSI include: the direct introduction of consumer characteristics (such as age, education, and income) in model construction; the wide coverage of services, especially free services; and the adoption of a product weighting system based on consumer price index (CPI), not on gross domestic product (GDP). In this paper, a theoretical framework of consumer satisfaction is first presented after investigating the relevant literature, and then the large‐scale consumer survey scheme adopted to collect the data and the structural equation modeling technique employed to estimate the indexes are discussed. New considerations in the HKCSI in such areas as model structure, indicator and questionnaire design, and product classification are elaborated upon. Next, the estimated results are analyzed focusing on the reliability and validity of the model, on the relationships among consumer satisfaction and its antecedents and consequences, and especially on the effects of consumer characteristics on consumer satisfaction and the implications of such effects for marketing practice. The results are generally consistent across different products, broadly acceptable and in agreement with previous findings, and are also relevant to Hong Kong's special situation. The paper concludes with a summary and some remarks on problems in the present study and future research directions.

The transportation of fresh food requires cold chain logistics to maintain a low-temperature environment, which can reduce food waste and ensure product safety. Therefore, temperature control is a major challenge that cold chain logistics face.

This research proposes a prediction model of refrigerated truck temperature and air conditioner status (air speed and air temperature) based on hybrid mayfly algorithm (MA) and extreme learning machine (ELM). To prove the effectiveness of the proposed method, the mayfly algorithm–extreme learning machine (MA-ELM) is compared with the traditional ELM and the ELM optimized by classical biological-inspired algorithms, including the genetic algorithm (GA) and particle swarm optimization (PSO). The assessment is conducted through two experiments, including temperature prediction and air conditioner status prediction, based on a case study.

The prediction method is evaluated by five evaluation indicators, including the mean relative error (MRE), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and coefficient of determination (R²). It can be concluded that the biological algorithm, especially the MA, can improve the prediction accuracy. This result clearly proves the effectiveness of the proposed hybrid prediction model in revealing the nonlinear patterns of the cold chain logistics temperature.

The case study illustrates the effectiveness of the proposed temperature prediction method, which helps to keep the product fresh. Even though the performance of MA is better than GA and PSO, the MA has the disadvantage of premature convergence. In the future, the modified MA can be designed to improve the performance of MA-ELM.

In prior studies, many scholars have conducted related research on the subject of temperature monitoring. However, this monitoring method can only identify temperature deviations that have occurred that harmed fresh food. As a countermeasure, research on the temperature prediction of cold chain logistics that can actively identify temperature changes has become the focus. Once a temperature deviation is predicted, temperature control measures can be taken in time to resolve the risk.

Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF. This study proposes a bibliometric data-driven analysis from the literature to illustrate a clear overall concept of SSCF that reveals hidden indicators for further improvement.

A hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context.

The results show that blockchain, cash flow shortage, reverse factoring, risk assessment and triple bottom line (TBL) play significant roles in SSCF. A comparison of the challenges and gaps among different geographic regions is provided in both advanced local perspective and a global state-of-the-art assessment. There are 35 countries/territories being categorized into five geographic regions. Of the five regions, two, Latin America and the Caribbean and Africa, show the needs for more improvement, exclusively in collaboration strategies and financial crisis. Exogenous impacts of wars, natural disasters and disease epidemics are implied as inevitable attributes for enhancing the sustainability.

This study contributes to (1) boundary SSCF foundations by data driven, (2) identifying the critical SSCF indicators and providing the knowledge gaps and directions as references for further examination and (3) addressing the gaps and challenges in different geographic regions to provide advanced assessment from local viewpoint and to diagnose the comprehensive global state of the art of SSCF.

This paper aims to research the lubrication performance of large-size rectangular oil pad in hydrostatic thrust bearing for heavy computer numerical control (CNC) vertical lathe.

The research establishes the mathematical models of velocity, flux and pressure fields, etc., for lubrication performance distribution, and analyzes its load-bearing behavior.

When hydrostatic thrust bearing’s rotating speed is within ω₁-ω₂, the oil flow generated by plate’s relative motion is greater than that generated by pressure difference and centrifugal force, and in the opposite direction, making it not easy to emit friction heat, so the rotating speed range ω₁-ω₂ should be avoided for bearing.

The research provides powerful theoretical basis for the structure design, operating reliability and practical application of large size rectangular oil pad hydrostatic thrust bearing, and realizing the prediction of its lubrication performance.

Information sharing is one of essential collaboration methods for building effective system-level disruption responses and communication for supply chain resilience. However, supply chain members are often reluctant to share the members' business information for fear of losing competitiveness. To facilitate the cooperation among these members, the supply chain members' should be made aware of the value of information. As a result, the purpose of this paper is to quantify the benefit of information sharing and evaluate its magnitude under various factors.

In this paper, information sharing is measured in a two-stage supply chain containing a manufacturer and a retailer. A demand function is constructed as a linear combination of a first-order autoregressive [AR(1)] process, the retail and reference prices. The values of information sharing are quantified for four scenarios: (1) no information sharing, (2) full information sharing, (3) limited information sharing and (4) partial information sharing. Based on the four scenarios, the conditions for valuable information sharing are determined. In addition, the impact of several demand parameters on the usefulness of information sharing is analyzed.

When the demand function is a pure AR(1) process (i.e. there is no impact from the retail and reference prices), information sharing is always valuable regardless of the autoregressive coefficient. Under the influence of the retail price and consumer behavior via the reference price, information sharing is not always beneficial. The boundaries for useful information sharing are analytically constructed. In addition to full information sharing, this study also quantifies the value of information under a partial sharing scheme. The results indicate that the information is more valuable as long as the information is inducible.

This study highlights several specific conditions for a beneficial information sharing agreement in consideration of consumer behaviors. These conditions enable supply chain members to design a sustainable partnership.

To take the advantages of terrain-adaptive capability of legged platform and fast-moving ability of wheeled platform, this paper aims to design a leg-wheel mobile platform for obstacle surmounting and analyze the feasibility and locomotivity of different moving modes.

The platform consists of six leg-wheel units. Each of the units has a close-chain mechanical leg and an actuated wheel at the end of the leg. The platform moves with two modes: legged mode and leg-wheel composite mode. The legged mode achieves high mobility driven by crank motors, while the leg-wheel composite mode achieves obstacle-surmounting ability actuated by crank motors and pitch link motors and obtains high efficiency with the hub motors. The gait planning in different modes has been carried out and the obstacle-surmounting capacity has been analyzed.

Based on the results of kinematic analysis and gait planning of the close-chain leg-wheel platform, the high mobility and efficiency obstacle-surmounting ability are demonstrated with the two movement modes. The feasibility of the design and the performance of the mobile platform is verified with the prototype experiment. The results of this paper show that the platform possesses good obstacle-surmounting capability.

The work presented in this paper is a novel exploration to design a close-chain leg mechanism and with an actuated wheel in series. The close-chain leg mechanism has the advantages of high leg lift and single degree of freedom characteristics, which makes the platform obtain the ability of obstacle-surmounting.

The call for supply chain transparency (SCT), especially the environmental, social and governance (ESG) aspect, is getting increasingly louder. Based on the signaling theory, our study investigates the operational benefit of supply chain transparency in terms of ESG (SCT-ESG). To further clarify the signaling process, the moderating roles of digitalization of the firm and signal strength are also examined.

Longitudinal secondary data from multiple databases are matched and analyzed using ordinary least squares (OLS) regressions to validate the proposed hypotheses.

Results suggest that with SCT-ESG, firms have a weakened disparity between production variance and demand variance, and the supply chain experiences a reduced bullwhip effect. Further, digitalization of the focal company and signal strength reinforce the negative effect of SCT-ESG on the bullwhip effect.

The study integrates the SCT and ESG literature through SCT-ESG, extending benefits of ESG disclosure to the supply chain context. It extends the application of the signaling theory in OSCM by including contextual factors of digitalization and signal strength.

The purpose of this paper is to investigate the attitude cooperation control of multi-spacecraft with in-continuous communication.

A decentralized state-irrelevant event-triggered control policy is proposed to reduce control updating frequency and further achieve in-continuous communication by introducing a self-triggered mechanism.

Each spacecraft transmits data independently, without the requirement for the whole system to communicate simultaneously. The local predictions and self-triggered mechanism avoid continuous monitoring of the triggering condition.

This investigation is suitable for small Euler angle conditions.

The control policy based on event-triggered communication can provide potential solutions for saving communication resources.

This investigation uses event- and self-triggered policy to achieve in-communication for the multi-spacecraft system.

Multiutility tunnel (MUT) has been recognised as a more sustainable method to place underground utilities than the traditional directly buried (DB) method. However, the implementation of MUT is hindered because of high initial construction costs and the difficulty to demonstrate its benefits, especially social benefits that are hard to be quantified. To address the limitation, this paper aims to quantify and compare both economic costs and traveller loss (i.e. an important part of social costs) of the MUT and DB method.

An agent-based model (ABM) is developed, which considers attributes and actions of vehicles, interactions between vehicles and interactions between vehicles and the road network. The ABM is used to estimate traveller loss by comparing traveller time when the MUT and DB method is adopted, respectively. The traveller loss is combined with economic costs to estimate and compare the LCC of the MUT and DB method. To verify the ABM-based approach, it is implemented in an MUT project in Shanghai, China.

Results of the study indicate: (1) When the DB method is adopted, periodic E&Rs cause severe traffic congestion and substantial traveller loss. (2) When traveller loss is not included in the LCC estimation, the DB method has a lower LCC in most scenarios. (3) When traveller loss is included, the relative LCC of MUT and the time it takes to cover the LCC of the MUT and DB method is largely reduced. Thus, when social costs are considered, MUT will bring more benefits than the DB method.

Previous studies on comparing the MUT and DB method focus on investigating economic costs, while other costs, e.g. social costs, are not well addressed quantitatively. Besides, current studies of traveller loss estimation lack consideration of factors such as unique attributes, actions and interactions of vehicles and the network. Hence, this paper applies an ABM-based approach to involve these factors and produce more reliable estimation of traveller loss than existing approaches. Moreover, by integrating traveller loss into LCC analysis, this paper helps to understand the benefits of MUT thus assisting decision-making in selecting utilities placement methods.

The purpose of this study is to examine the influence of virtual currency (VC) development on financial stocks’ value in selected Asian equity markets and the moderating role of investor attention on this relationship.

The pooled ordinary least squares regression is used on a sample of 138 listed financial firms from four emerging Asian countries for the period 2016–2020.

This study finds that changes in VC values have greater spillover effects on the values of financial stocks in countries which do not recognize the legitimacy of VCs than in countries which do, due to the lack of breadth and depth of the former markets. Moreover, this paper also reports evidence of the greater moderating role of investor attention on this relationship in countries which do not recognize the legitimacy of VCs than in countries which do.

Although numerous studies have been conducted on the influence of VCs on stock performance, majority of these studies did not distinguish whether the sample countries being studied actually recognize the legitimacy of VC transactions or not. Moreover, extant literature has not considered the moderating role of investor attention on this relationship. It is the aim of this study to address these research voids by using a refined three-factor theory model of capital asset pricing model incorporating VCs to better represent stock performance in the digital economy era.