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This paper aims to examine the unique Chinese context by analyzing the city labels (e.g. smart city and eco city) used by Chinese local governments at or above the provincial capital level to represent themselves (adopted city labels) and the developmental pathways they actually pursued (adopted developmental pathways).

The authors compared the city brand choices to those anticipated based on their geographic and economic contexts (predicted city labels and developmental pathways) as well as the directives outlined in national planning documents (imposed city labels and developmental pathways). The authors identified ten main categories of city labels used to designate themselves and establish the frequency of their use based on municipal plan documents, economic and geographic data and national plan documents and policy reports, respectively.

The authors discovered that both local economic development and geographic factors, as well as top-down administrative influences, significantly impact city branding strategies in the 38 Chinese cities studied. When these models fall short in predicting adopted city labels and pathways, it is often because cities favor a service-oriented reputation over a manufacturing-focused one, and they prefer diverse, multifaceted industrial images to uniform ones.

The originality and value of this paper lie in its contribution to the academic literature on city branding by developing a predictive model for brand development at the municipal level, with explicit attention to the national-local nexus. The paper’s approach differs from existing research in the first cluster of city branding by not addressing issues of stakeholder involvement or adoption and implementation processes. Additionally, the paper’s focus on the political power dynamics at the national level and urban governance details at the municipal level provides a unique perspective on the topic. Overall, this paper provides a valuable contribution to the field of city branding by expanding the understanding of brand development and its impact on the socioeconomic environment.

This study aims to validate whether enhancing interorganizational task interdependence among tier 2 suppliers can act as an effective approach for the tier 1 supplier to enhance collaborations among these suppliers in the buyer-tier 1 supplier-tier 2 supplier triadic new product development (NPD) project when buyer's requirements are unstable.

Based on social interdependence theory, a model linking interorganizational task interdependence to interorganizational collaboration and NPD product performance is built. The authors conducted a survey study to collect data from 169 automotive tier 1 suppliers and performed regression analysis to empirically test the hypotheses.

The test results suggest that interorganizational task interdependence among tier 2 suppliers can promote interorganizational promotion-oriented information sharing and interorganizational joint decision making among these suppliers. These improvements can ultimately improve supplier’s NPD product performance. However, the buyer’s requirements uncertainty can reduce the positive effects of such an interdependence arrangement.

This study provides new insights into the role of a tier 1 supplier to enhance the collaboration among tier 2 suppliers in the limited condition of this relationship while providing an alternative explanation regarding contradictory ideas about task interdependence among suppliers.

Continuum manipulators are often used in complex and narrow space in recent years because of their flexibility and safety. Vision is considered to be one of the most direct methods to obtain its spatial shape. However, with the improvement of the cooperation requirements of multiple continuum manipulators and the increase of space limitation, it is impossible to obtain the complete spatial shape information of multiple continuum manipulators only by several cameras.

This paper proposes a fusion method using inertial navigation sensors and cameras to reconstruct the shape of continuum manipulators in the whole workspace. The camera is used to obtain the position information, and the inertial navigation sensor is used to obtain the attitude information. Based on the above two information, the shape of the continuum manipulator is reconstructed by fitting Bézier curve.

The experiment result of single continuum manipulator shows that the cubic Bézier curves is applicable to curve fitting of variable curvature, the maximum fitting error is about 2 mm. Meanwhile, the experiment result shows that this method is not affected by obstacles and can still reconstruct the shape of the continuum manipulators in 3-D space by detecting the position and attitude information of the end.

According to the authors’ knowledge, this is the first study on spatial shape reconstruction of multiple continuum manipulators and the first study to introduce inertial navigation sensors and cameras into the field of shape reconstruction of multiple continuum manipulators in narrow space. This method is suitable for shape reconstruction of manipulator with variable curvature continuum manipulator. When the vision of multiple continuum manipulators is blocked by obstacles, the spatial shape can still be reconstructed only by exposing the end point. The structure is simple, but it has certain accuracy within a certain range.

There is an increasing popularity for the continuum robot in minimally invasive surgery owing to its compliance and dexterity. However, the dexterity takes the challenges in loading and precise control because of the absence of the shape tracking for the continuum robot. The purpose of this paper is to propose a new type of continuum manipulator with variable stiffness that can track the bending shape timely.

The low-melting-point alloy (LMPA) is used to implement the stiffness variation and shape detection for the continuum manipulator. A conceptual design for a single module is presented, and the principle of stiffness control based on the established static model is formulated. Afterward, a shape detection method is introduced in which the shape of the continuum manipulator can be detected by measuring the resistance of every LMPA. Finally, the effect of the proposed variable stiffness method is verified by simulation; the variable stiffness and shape detection methods are evaluated by experiments.

The results from the simulations and experiments indicate that the designed continuum manipulator has the ability of stiffness variation over 42.3% and the shape detection method has high precision.

Compared with conventional structures, the novel manipulator has a simpler structure and integrates the stiffness variation and shape detection capabilities with the LMPA. The proposed method is promising, and it can be conveniently extended to other continuum manipulators.

The purpose of this paper is to investigate the moderating effect of disease risk in terms of the major signals (i.e. status, reputation and self-representation) on the e-consultation platforms.

In this study, the proposed research hypotheses are tested using the transaction data collected from xywy.com (in Need of Therapy). In fact, xywy.com is one the leading e-consultation service websites in China that provides a platform for the interactions between the physicians and patients (Yu et al., 2016; Peng et al., 2015). Generally speaking, it has all the needed design elements and in other words, a standard e-consultation website should have such items/components as physician homepage, physician review, free consultation, paid consultation and recommendation systems.

The obtained results reveal that all attributes including status, reputation and self-representation have a positive impact on physician’s online order volume. Moreover, there is a positive moderating effect of disease risk onto the online reputation, indicating a higher effect exists for the diseases with high risk. However, the effect of offline status and online self-representation is not moderated by the disease risk, indicating market signals (online reputation) may have a stronger predictive power than seller signals (offline status and online self- representation), and therefore market signals are more effective when/if the disease risk is high.

E-consultation has gradually become a significant trend to provide the healthcare services, in the emerging economy such as China because of shortage of medical resources but having an adequate access in internet usage. The impacts of signals on the health care market have been validated by previous studies. However, the research focusing on the moderating effect of signaling environment in the health care industry is still lacking. As a result, the value of this research helps to bridge the aforementioned research gap.

The purpose of this paper is to compare the growth kinetics of interfacial intermetallic compound (IMC) layer and its effect on the tensile strength of two solder (Sn3.0Ag0.5Cu and Sn0.4Co0.7Cu) joints.

The samples annealed, respectively, at 85, 120 and 150°C up to 1,000 h were tensile tested and their cross‐sections were observed by scanning electron micrography.

The results showed that, for both solder joints, an approximately linear reduction in tensile joint strength with an increase in the IMC layers' thickness occurred. The tensile strength of Cu/Sn3.0Ag0.5Cu solder joints is slightly better than that of Cu/Sn‐0.7Co‐0.4Cu solder joints under analogous aging conditions. In addition, the growth kinetics of the overall interfacial IMC layer in Sn0.4Co0.7Cu solder joints can be simply described by the classical growth kinetic theory for solid‐state diffusion with an activation energy of 2,996.85 J/mol and interdiffusion constant of 4.15×0⁻¹⁷ m²/s which are relatively lower, compared with Sn3.0Ag0.5Cu solder on copper with 14,167.8 J/mol and 65.33×10⁻¹⁷ m²/s, respectively.

The paper is of value in evaluating the growth kinetics of Sn3.0Ag0.5Cu and Sn0.4Co0.7Cu solder joints, and in discussing and contrasting the influence of IMC growth on their tensile strength.

Despite the prevalent use of professional status and service feedback in online healthcare markets, the potential interaction relationship between two types of information is still unknown. This study used the signaling theory to examine the substitute relationship between professional status and service feedback in patients' doctor choice, as well as the moderating effect of illness severity.

To test the paper's hypotheses, we constructed a panel data model using 418 doctors' data collected over a period of six months from an online healthcare market in China. Then, according to the results of the Hausman test, we estimated a fixed-effects model of patients' choice in online healthcare markets.

The empirical results showed that the effect of a doctor's professional status and service feedback on a patient's doctor choice was substitutable. Moreover, patients' illness severity played a moderating role, in that the influence of professional status on a patient with high-severity illness was higher than that on a patient with low-severity illness, whereas the influence of service feedback on a patient with low-severity illness was higher than that of a patient with high-severity illness. In addition, we found that illness severity negatively moderated the substitute relationship between professional status and service feedback on a patient's choice.

These findings not only contribute to signaling theory and research on online healthcare markets, but also help us understand the importance of professional status and service feedback on a patient's choice when seeking a doctor online.

This study aims to study the mechanisms and conditions of users' intention to continue to use online health platforms from an information technology (IT) affordance perspective.

b This research proposes that a critical affordance effect on an online health platform, users' intention to continue the use of the platform, is affected by five platform affordances via two actualized affordances (i.e. perceived benefits (PBs) and online engagement (OE)). Perceived health threat moderates the effect generated by affordance actualization. A dataset involving 409 users from the “Ping An Health” platform was collected through an online survey and analyzed to validate the research hypotheses.

The data analysis results confirm that the proposed online health platform affordances affect users' PBs and OE, which influence users' intentions to continue using the platform. Perceived threats (perceived vulnerability (PVU) and perceived severity (PSE)) moderate the relationship between PBs and continuance intention (CI) and between OE and CI.

The research provides important recommendations for online health platform designers to develop IT affordances that can support users' needs for healthcare services.

Limited studies investigated why users continue participating in online diagnosis and treatment. This study provides a new perspective to expand the affordance framework by combining technology features and user health behavior. The study also emphasizes the importance of perceived threats in IT use.

Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.

This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.

The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.

This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.