Search results

In high-speed trains, the energy is supplied from a high voltage catenary to the vehicle via a pantograph catenary system (PCS). Carbon pantograph strips must maintain continuous contact with the wire to ensure safety and reliability. The contact is often confined to a particular spot, resulting in excessive wear due to mechanical and thermal damage, exacerbated by the presence of an electric arc and associated electrochemical corrosion. The effectiveness and reliability of the PCS impacts on the performance and safety of HSTs, especially under high-speed conditions. To alleviate some of these adverse effects, this paper aims to propose a configuration where a circular PCS replaces the currently used pantograph strips.

Two dynamic multi-physics models of a traditional PCS with a carbon strip and a novel PCS with a circular pantograph strip catenary system are established, and the electrical and mechanical characteristics of these two systems are compared. Moreover, a PCS experimental platform is designed to verify the validity and accuracy of the multi-physics model.

A novel circular pantograph system is proposed in this paper to alleviate some of the shortcomings of the traditional PCS. Comparing with a traditional PCS, the circular PCS exhibits superior performance in both electromagnetic and thermal aspects.

The paper offers a new technical solution to the PCS and develops a dedicated multi-physics model for analysis and performance prediction with the aim to improve the performance of the PCS. The new system offers numerous benefits, such as less friction heat, better heat dispersion and improved catenary-tracking performance.

This study examines how narratives and legitimacy formation affect crowdfunding capital assembly from distributed, heterogeneous investors.

The study explores a dataset of 80,181 projects from Kickstarter, a rewards-based crowdfunding platform, between 2009 and 2013. We explore the link between project-related variables, legitimacy formation and outcomes.

Entrepreneurs design narratives and create project legitimacy by exploiting crowdfunding platform-specific features. First, lower funding targets and shorter campaign durations confer positive project legitimacy. Second, entrepreneurs exploit reward-levels as narrative tools that encourage funders to engage with the project. Third, visual pitches transmit a broader sociocultural narrative, leveraging emotional rather than financial reasoning. We also note certain gender effects.

Crowdfunding platforms allow entrepreneurs to pitch business ideas to a broad online audience. We show that project legitimacy, including both structural and narrative elements, is linked to crowdfunding outcomes. In particular, legitimacy is co-created through the generation of a persuasive narrative linking the entrepreneur and investor cohort.

Entrepreneurs use crowdfunding platforms to generate a coherent narrative around unfamiliar business models. Generic platform tools may be set and manipulated in online crowdfunding pitches to support project legitimacy. Ultimately, these are less important than establishing an affinity-based narrative that engages and exploits investor participation. Successful crowdfunding pitches co-author the project story with investors.

Crowdfunding has been traditionally understood as simply an online-mediated venture resource assembly tool. A narrative framework highlights the critical role of legitimacy formation in a disintermediated investment system.

The purpose of this paper is to design a robust tracking algorithm which is suitable for the real-time requirement and solves the mistake labeling issue in the appearance model of trackers with the spare features.

This paper proposes a tracker to select the most discriminative randomly projected ferns and integrates a coarse-to-fine search strategy in this framework. First, the authors exploit multiple instance boosting learning to maximize the bag likelihood and select randomly projected fern from feature pool to degrade the effect of mistake labeling. Second, a coarse-to-fine search approach is first integrated into the framework of multiple instance learning (MIL) for less detections.

The quantitative and qualitative experiments demonstrate that the tracker has shown favorable performance in efficiency and effective among the competitors of tracking algorithms.

The proposed method selects the feature from the compressive domain by MIL AnyBoost and integrates the coarse-to-fine search strategy first to reduce the burden of detection. This paper designs a tracker with high speed and favorable results which is more suitable for real-time scene.

The learning outcomes of this study are as follows: identify key elements of luxury branding in the context of a new residential real estate brand; select target segment/s and outline the sales pitch for a luxury residential real estate brand; plot the pre-sales stage of the customer journey path (CJP) for a luxury residential real estate brand; and plan a pre-sales customer engagement strategy for a luxury residential real estate brand.

This case enumerates Aldeola de Siolim, Goa’s (ASG) pre-sales promotional challenges. ASG was an upcoming luxury residential property in Goa, India. Venky Infar – the developer of ASG – a family-owned civil construction firm – wanted to diversify into Goa’s vibrant luxury housing market. In India’s housing market, the success of a project often depends on the “pre-sales,” i.e. attracting target customers and maximizing the sales before the construction. V. Rama Rao, the project manager’s task, was challenging because ASG and Venky were new entrants in a mature and competitive market. However, Rao was determined to capture a slice of this lucrative market. The case discusses the following four points to help the students understand the marketing challenges and decision context. First, ASG’s key attractions, second, overview of the Indian real estate market, third, characteristics of Goa’s luxury home market and finally, Customer Journey Path for residential real estate purchase. The case elaborates on the nuances of strategic dilemmas and and presents competitors' practices and emerging consumer trends.

The case will help students analyze and formulate a pre-sales promotional plan for a luxury real estate product. It is suitable for marketing elective courses, e.g. branding, sales management and luxury management.

Teaching notes are available for educators only.

CSS 8: Marketing

The purpose of this paper is to empirically investigate the impact of economic policy uncertainty on firms' labor investment decision, which includes labor investment level and efficiency, especially human capital allocation.

This paper uses Economic Policy Uncertainty Index for China and Chinese A-share listed firms in the period 2002–2016 to constructs a sample of 20,779 firm-year observations and applies the methods of pooled OLS regressions to do an empirical study.

This paper finds that firms' labor investment is negatively correlated with economic policy uncertainty. And firms' labor investment efficiency (and overinvestment in labor) is positively (negatively) correlated with economic policy uncertainty, which is more significant for non-SOEs and firms with less government intervention. Further, the positive relation between economic policy uncertainty and labor investment efficiency is more significant for labor-intensive firms, firms in competitive industry, firms in developed labor market and firms under strong labor law protection. In addition, economic policy uncertainty induces firms to make adjustment on human capital structure and allocate more employees with high human capital, which eventually helps firms achieve higher total factor productivity.

The study of this paper indicates that the government needs to consider economic policies' impact on firms when introducing and changing policies and guide firms to improve human capital allocation under different internal and external conditions to finally realize the optimal allocation of social resources.

This paper studies the influence of external economic policy environment on firms' labor investment decision, which lacks adequate attention in the literature and indicates that under economic policy uncertainty, firms actively decrease labor demand and increase labor investment efficiency by optimizing human capital allocation.

The purpose of this paper is to provide an analytical model for low carbon supplier development. This study is focused on the level of investment and collaboration decisions pertaining to emission reduction.

The authors’ model includes a fuzzy c-means (FCM) clustering algorithm and a fuzzy formal concept analysis. First, a set of suppliers were classified according to their carbon performances through the FCM clustering algorithm. Then, the fuzzy formal concepts were derived from a set of fuzzy formal contexts through an intersection-based method. These fuzzy formal concepts provide the relative level of investments and collaboration decisions for each identified supplier cluster. A case from the Indian renewable energy sector was used for illustration of the proposed analytical model.

The proposed model and case illustration may help manufacturing firms to collaborate with their suppliers for improving their carbon performances.

The study contributes to the low carbon supply chain management literature by identifying the decision criteria of investments toward low carbon supplier development. It also provides an analytical model of collaboration for low carbon supplier development. Though the purpose of the study is to illustrate the proposed analytical model, it would have been better if the model was empirically validated.

Though the earlier studies on green supplier development program evaluation have considered a set of criteria to decide whether or not to invest on suppliers, these are silent on the relative level of investment required for a given set of suppliers. This study aims to fulfill this gap by providing an analytical model that will help a manufacturing firm to invest and collaborate with its suppliers for improving their carbon performance.

The purpose of this study is to examine the intergenerational influence across dyads of mothers and daughters from the USA and the People’s Republic of China (PRC), with a particular interest in discovering the cross-national differences in terms of the level of mother–daughter brand preference agreement, the directional influence from daughter to mother and leading factors for the observed differences.

Using a parallel survey method, responses were obtained regarding participants’ brand preferences, as well as their perceptions of their dyad partners’ preferences, for 20 product categories. A total of 76 dyads in the USA and 114 dyads in the PRC were collected.

Results not only confirmed the existence of intergenerational influence in mother–daughter dyads’ brand preferences after removing the nominal bias that previous studies commonly suffered but also suggested two interesting cross-national differences. Specifically, the authors find that US mother–daughter dyads possess a higher level of brand preference agreement than their PRC counterparts; however, the influence from daughters to mothers in the PRC is greater than in the USA. The authors further find that two potential leading factors contribute to the observed cross-national differences; mother–daughter communication is stronger but less influential in the USA than in the PRC, while children’s peer influence, measured as information influence of peers, is weaker but more influential in the USA than in the PRC.

Understanding intergeneration influences in different cultural contexts may be applicable in developing communication strategies leading to brand preference.

This study contributes to the consumer socialization literature by examining the cross-national differences of intergenerational influence in brand preferences and their leading causes of such differences in the context of the two biggest economies.

The shape of a product plays a crucial role in shaping consumer behavior. Despite the voluminous research on factors influencing consumers’ shape preferences, there remains a limited understanding of how the busy mindset, a mentality increasingly emphasized by marketing campaigns, works. This study aims to fill this gap by exploring the relationship between a busy mindset and the preference for angular-shaped versus circular-shaped products and brand logos.

This research consists of seven experimental studies using various shape stimuli, distinct manipulations of busy mindset, different assessments of shape preference and samples drawn from multiple countries.

The findings reveal that a busy mindset leads to a preference for angular shapes over circular ones by amplifying the need for uniqueness. In addition, these effects are attenuated when products are scarce.

This research represents one of the pioneering efforts to study the role of a busy mindset on consumers’ aesthetic preferences. Beyond yielding insights for practitioners into visual marketing, this research contributes to the theories on the busy mindset and shape preference.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

Well-being at work is a prime concern for learning organizations where work is knowledge-intensive and the need for updated learning exerts high work pressure. This study aims to examine the mediating influence of organizational learning capability in facilitating routine and novel knowledge sharing to foster employees’ well-being at work in Indian information technology (IT) organizations. This research explores whether the sharing of routine knowledge and novel knowledge contributes to employees’ well-being at work by enhancing organizational learning capability.

Using a quantitative approach, the authors collected data from 209 employees in IT organizations in India via a questionnaire survey. After verifying the reliability and validity of the data, the authors analysed the data using co-variance-based structural equation modelling using AMOS 26.

The results show that the indirect effect of routine and novel knowledge sharing on well-being at work was influenced by the mediating role of organizational learning capability. Routine knowledge sharing has a significant positive impact on organizational learning capability and well-being at work. While novel knowledge sharing positively predicted organizational learning capability, it did not have a direct impact on well-being at work. Moreover, organizational learning capability has a direct positive effect on employees’ well-being at work.

The cross-sectional design of the study makes the cause-and-effect relationship difficult to conclude. Moreover, the use of self-report measures poses methodological biases. Thus, longitudinal studies with objective measurements are recommended. Future studies can examine the role of individual characteristics such as learning orientation and personality in the studied framework.

Employee well-being and organizational learning can be enhanced through knowledge sharing practices, promoted by human resource policies and leaders. This promotes on-the-job learning, reducing working hours for training and learning purposes. By fostering a culture of openness, mutual trust and networking, organizations can enhance their employees’ work−life balance and overall performance.

This paper addresses a paucity in the literature concerning the outcomes of knowledge sharing and factors that lead to well-being at work. Drawing on the learning-based well-being perspective and job-demand resource theory, this research pioneers the examination of the mediating effect of organizational learning capability in the link between routine and novel knowledge sharing and employees’ well-being in IT learning organizations in India. Findings of this study may help managers of IT firms boost organizational learning and improve knowledge workers’ well-being, thus helping to maximize their performance and enhance employee retention and welfare.