Search results

Service providers on highly competitive online outsourcing platforms employ various signals to entice buyers to make online purchases. One such signal—the solution exemplars—attracts attention through depictions of exemplary prior work completed by the service providers. Unfortunately, it is not known the extent to which solution exemplar characteristics affect sales performance nor is it clear how such signals perform in the presence of complementary signals such as service provider expertise. This paper explores these issues.

Extending signaling theory, the authors develop a model to explore the effects of solution exemplar characteristics (i.e. exemplar quantity, exemplar diversity and exemplar popularity) on sales performance under the moderating impact of service provider expertise. The authors test the model using proprietary data from ZBJ.com, a leading online outsourcing platform in China.

Exemplar quantity and exemplar popularity positively affect sales performance; exemplar diversity has no significant impact on sales performance and service provider expertise positively moderates the relationships between exemplar quantity, exemplar popularity and sales performance.

This work makes several significant contributions. First, the authors enrich the research on signals in online outsourcing by exploring the impact of solution exemplar characteristics on sales performance. Second, the authors analyze three solution exemplar characteristics: exemplar quantity, exemplar diversity and exemplar popularity. Third, this work shows that service provider expertise moderates the relationship between solution exemplar characteristics and sales performance. Important practical implications for both online outsourcing platforms and service providers are discussed.

This study explores how app store awards shape the behavior of leading digital entrepreneurs, focusing on their engagement in technological innovation through feedback and re-signaling mechanisms. It aims to reveal the nudging effect of award signals on entrepreneurial behavior.

In this study, data from 349 leading mobile app entrepreneurs in the UK Google Play Store were collected over an extended period from various sources. Functionality novelty and explorative behavior were assessed by analyzing app release date and permission technologies in comparison to both their own prior apps and those of their closest competitors. Hypothesized relationships were tested using accelerated failure time parametric models.

This study extends the literature on signaling by showing that (1) the top developer award signal served to nudge entrepreneurs to improve the functionality novelty of their apps and those who succeeded were less likely to switch to another product category, (2) the award signal created a window of opportunity for non-award entrepreneurs to respond and those who released new apps around the midpoint of a normal app development cycle significantly improved the likelihood of winning the award in a subsequent round of award-giving, and (3) the effect of functionality novelty on winning the award was more pronounced when non-award entrepreneurs pursued more explorative than exploitative behavior in app development.

The results offer novel insights into an understudied area, specifically the influence of online award signals on nudging entrepreneurs to pursue technological innovation. The research also highlights the crucial role played by the app store as an intermediary signaler.

These implementations not only generate excessive voltage levels to enhance the quality of power but also include a detailed investigating of the various modulation methods and control schemes for multilevel inverter (MLI) topologies. Reduced harmonic modulation technology is used to produce 11-level output voltage with the production of renewable energy applications. The simulation is done in the MATLAB/Simulink for 11-level symmetric MLI and is correlated with the conventional inverter design.

This paper is focused on investigating the different types of asymmetric, symmetric and hybrid topologies and control methods used for the modular multilevel inverter (MMI) operation. Classical MLI configurations are affected by performance issues such as poor power quality, uneconomic structure and low efficiency.

The variations in both carrier and reference signals and their performance are analyzed for the proposed inverter topologies. The simulation result compares unipolar and bipolar pulse-width modulation (PWM) techniques with total harmonic distortion (THD) results. The solar-fed 11-level MMI is controlled using various modulation strategies, which are connected to marine emergency lighting loads. Various modulation techniques are used to control the solar-fed 11-level MMI, which is connected to marine emergency lighting loads. The entire hardware system is controlled by using SPARTAN 3A field programmable gate array (FPGA) board and the least harmonics are obtained by improving the power quality.

The simulation result compares unipolar and bipolar PWM techniques with THD results. Various modulation techniques are used to control the solar-fed 11-level MMI, which is connected to marine emergency lighting loads. The entire hardware system is controlled by a SPARTAN 3A field programmable gate array (FPGA) board, and the power quality is improved to achieve the lowest harmonics possible.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

This study aims to follow a rigorous approach to identify, critically analyze and synthesize 75 papers published from 2000 to 2022.

The study presents a systematic literature review on identity expressiveness (IE), clarifying and expanding what is currently known about the concept.

To synthesize current knowledge on IE, the study uses the overarching framework of antecedents-phenomenon-consequences, using this same framework to identify gaps and future research directions. The findings show individual and brand-related factors such as the need for uniqueness and anthropomorphism as antecedents of IE, and eWOM/WOM, impulse purchases and upgrading to more exclusive lines as consequences of IE.

The study contributes to theory by synthesizing and mapping current understanding of the state of knowledge on the concept of IE while highlighting gaps in the extant literature and paving future research directions for scholars in the field.

The study offers useful insights for practitioners, broadening marketers’ actionable options in identity-based marketing. Marketers can use insights from this study to inform marketing strategy and communication campaigns for different types of brands.

To the best of the authors’ knowledge, this study is the first of its kind and offers an integrative review of the current literature on IE, thus enhancing understanding of the concept, its antecedents and consequences. The study also contributes to knowledge by highlighting future research priorities for researchers in this field of enquiry.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

The purpose of this paper is to present an up-to-date survey on the non-orthogonal multiple access (NOMA) technique with co-operative strategy, a fast-evolving fifth-generation (5 G) technology. NOMA is used for serving many mobile users, both in power and code domains. This paper considers the power-domain NOMA, which is now discussed as NOMA.

The first part of the paper discusses NOMA-based cooperative relay systems using different relay strategies over different channel models. In various research works, the analytical expressions of many performance metrics were derived, measured and simulated for better performance of the NOMA systems. In the second part, a brief introduction to diversity techniques is discussed. The multiple input and multiple output system merged with cooperative NOMA technology, and its future challenges were also presented in this part. In the third part, the paper surveys some new conceptions such as cognitive radio, index modulation multiple access, space-shift keying and reconfigurable intelligent surface that can be combined with NOMA systems for better performance.

The paper presents a brief survey of diverse research projects being carried out in the field of NOMA. The paper also surveyed two different relaying strategies that were implemented in cooperative NOMA over different channels and compared several performance parameters that were evaluated and derived in these implementations.

The paper provides a scope for recognizable future work and presents a brief idea of the new techniques that can be united with NOMA for better performance in wireless systems.

Platform certification constitutes an effective mechanism for managing the lemon problem concerning food e-commerce. This work aims to evaluate the market effect of platform certification and analyzes its correction mechanism for lemon problem combined with reputation mechanism.

Utilizing the Gold Seller certification of Taobao.com to serve as an illustration, the authors conducted an empirical study based on the sales data of hairy crabs among 2,239 sample sites over six points in time from October to December 2019, systematically examining the market effect of food e-commerce platform certification along with the interaction between food e-commerce platform certification and reputation mechanisms, followed by a heterogeneity test by product price.

This study finds that sellers with platform certification can significantly increase their sales. The market effect of platform certification is more easily observed in the low-price product market. In addition, platform certification and reputation mechanisms have complementary effects. In a low-price product market, the complementary effect of platform certification and product reputation diminishes, while the complementary effect of platform certification and seller reputation disappears.

This study explores the market effect of food e-commerce platform certification, reveals the market effect of certification mechanism when multiple signaling mechanisms exist simultaneously and conducts an empirical test based on real market data. It provides a better comprehension of how platform certifications work in food e-commerce.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

Leveraging signalling theory and institutional environment theory, this study aims to examine how the entrepreneurial orientation of emerging market firms impacts initial public offering (IPO) performance.

The authors conduct regression analysis based on archival data from 312 firms’ IPOs in India.

The results in the Indian context suggest it differs from IPO performance in developed markets. In an emerging market context, the findings suggest that only competitive aggressiveness is valued by investors in IPOs. The findings further show that proactiveness and autonomy negatively influence IPO underpricing.

The research propositions imply that, owing to institutional voids in emerging markets, investors’ risk propensity and, hence, rewarding a firm’s entrepreneurial orientation differ from those in developed markets.

Extant literature has given limited attention to the dynamics of entrepreneurial orientation and the effect of each dimension of entrepreneurial orientation on IPO performance in emerging markets.