Search results

This paper aims to propose an area under the curve model to represent ultraviolet (UV) exposure doses on EBT3 films (in mJ/cm²). The model was developed on a cross-section of the exposed films using visible absorbance method. Ultraviolet–A light emitting diodes (UVA–LEDs) with 20° and 60° half angle with distinctive peak emission wavelengths between 365 to 405 nm are used in this experiment. No similar experimental setup or findings have been reported thus far, though the various application of EBT3 for the measurement of solar UV (A + B) have been published since EBT3 is commercially available.

Two sets of UVA–LEDs were used as the UV radiation source in the experiment. The first set contains of four 5 mm low power UVA–LEDs with the 20° half angle and peak emission wavelength at 365, 375, 385 and 400 nm. The second set contains of five surface mount high power UVA–LEDs with the 60° half angle and peak emission wavelength at 365, 375, 385, 305 and 400 nm. The illumination setup for the two sets of LEDs is different between each other to obtain sufficient dose distribution on the films for spectroscopy analysis. This is due to the different illumination angle and irradiance intensity by each set of LEDs.

UV–LED with a peak emission of 365, 375 and 385 nm able to produce UV doses accurately measurable using EBT3 films, UVA–LEDs with peak emission at 395 nm and above produced much lower accuracy with R². From both set of LEDs, it can be concluded that peak emission wavelength of UVA–LED does influence the discoloration of the films. Shorter wavelength (higher energy) of UVA–LEDs discolors EBT3 films much intense compared to longer wavelength for a given UV dose exposure.

Despite various practical applicability and advantages of UV–LEDs, there are still no standard methods in measuring UV–LED radiation output. The proposed approach not only allows us to obtain the dose of UV–LED, where the sensitivity of measurement is wavelength (energy) depended but also allows us to visually observe the illumination pattern of invisible UV radiation through the application of EBT3 films.

The purpose of this paper is to present a failure analysis of the solder layer in a Darlington power transistor in a TO-3 package.

A failed Darlington power transistor in a TO-3 package was examined by different kinds of failure analysis techniques. At first, internal gas analysis was conducted to measure the atmosphere. Then, scanning acoustic microscopy (SAM) was performed to check the quality of the solder layers in the failed device, and the failure location was determined in the solder layer between chip and substrate. Next, the failed device was decapped to observe the defects. After removing the chip from the substrate, energy dispersive spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS) were applied and the main elemental composition of the solder layer was identified.

Internal gas analysis indicated that the moisture and oxygen contents exceeded the allowed maximum value. Large areas of voids were found in the solder layer by SAM. The main elemental compositions of the solder layer were identified by scanning electron microscopy and EDS. Furthermore, the valences of the chemical components in the solder layer were identified by XPS. Except for the few simple substances of the initial solder material, the chemical formulae of oxidation products in the solder layer were deduced. In addition, the root causes are also discussed.

This paper focuses on the solder layer failure of a power transistor. Factors such as the presence of oxygen, voids and other factors, which can cause transistor damage, were comprehensively analyzed. The analysis process is worth learning from and the results can be used to improve the reliability of power devices in this kind of package.

This paper aims to investigate whether there are relationships among corporate disclosure of environmental, social and governance (ESG) and firms’ operational (ROA), financial (ROE) and market performance (Tobin’s Q), and if these relationships are positives or negatives or even neutral.

The study sample covers US S&P 500-listed companies during the period 2009 to 2018. Panel regression analysis was used to examine the study hypotheses and achieve the study aims.

The results showed that ESG disclosure positively affects a firms’ performance measures. However, measuring ESG sub-components separately showed that environmental (EVN) and corporate social responsibility (CSR) disclosure is negatively associated with ROA and ROE. EVN and CSR disclosure is positively related to Tobin’s Q. Further, corporate governance (CG) disclosure is positively related to ROA and Tobin’s Q, and negatively related to ROE. More importantly, ESG, CSR, EVN and CG tend to be higher with firms that have high assets and high financial leverage. Furthermore, the higher level of ESG, EVN, CSR and CG disclosure, the higher the ROA and ROE.

The study limns a vision of the role of ESG on firm performance. This study tries to determine whether there are relationships among all ESG disclosure and FP, and if they are positive, negative or even neutral.

Despite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables.

Quantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model.

The results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets.

This research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.

The purpose of this paper is to investigate how to support small organizations to navigate the context of an accelerated Digital Transformation using Enterprise Social Media platforms, in response to external contingencies, such as the COVID-19 pandemic.

A longitudinal action research study, supported by an exploratory analysis that follows a hybrid approach of deductive and inductive reasoning, has been conducted in the context of a small organization. Several data collection techniques were used for context understanding and problem-solving.

Findings suggest that value creation related to the use of Enterprise Social Media platforms supports small organizations in this accelerated context of Digital Transformation. Value perception is central in overcoming adoption barriers and achieving sustainable use of these platforms in daily basis activities, especially in remote working. External pressures, like those imposed by the COVID-19 pandemic, play an important role in catalyzing digital initiatives.

As the main limitations to this paper, we highlight the study of a single organization in a specific context and the number of actors involved; hence, there is room to extend the study to other industries, organization sizes and contexts.

This paper provides managers with insights into how to conduct their Enterprise Social Media initiatives in a turbulent environment, highlighting their key success elements, and their potential to create value for their organizations and stakeholders. Furthermore, managers could explore the potential of Enterprise Social Media platforms to support organizations in the Digital Transformation journey.

Small organizations play an important role in generating wealth for nations around the world. However, governments encounter difficulties in supporting the Digital Transformation of this type of organization. This paper provides insights into how to use an affordable and intuitive technology to include this type of organization in the Digital Transformation journey.

A long-term study of Enterprise Social Media is recommended, but quite rare in the Information Systems literature. This study adopts a longitudinal investigation to analyze the use of Enterprise Social Media to support a small organization to adapt, in balance with their internal and external contingencies, providing a further contribution to the contingency theory. This research also adds contributions to the sociotechnical system perspective, analyzing the deep imbrication between social and technical subsystems in the required organizational change, supporting a small organization for coping with the effects of the COVID-19 pandemic.

This study aims to assess the default risk of borrowers in peer-to-peer (P2P) online lending platforms. The authors propose a novel default risk classification model based on data cleaning and feature extraction, which increases risk assessment accuracy.

The authors use borrower data from the Lending Club and propose the risk assessment model based on low-rank representation (LRR) and discriminant analysis. Firstly, the authors use three LRR models to clean the high-dimensional borrower data by removing outliers and noise, and then the authors adopt a discriminant analysis algorithm to reduce the dimension of the cleaned data. In the dimension-reduced feature space, machine learning classifiers including the k-nearest neighbour, support vector machine and artificial neural network are used to assess and classify default risks.

The results reveal significant noise and redundancy in the borrower data. LRR models can effectively clean such data, particularly the two LRR models with local manifold regularisation. In addition, the supervised discriminant analysis model, termed the local Fisher discriminant analysis model, can extract low-dimensional and discriminative features, which further increases the accuracy of the final risk assessment models.

The originality of this study is that it proposes a novel default risk assessment model, based on data cleaning and feature extraction, for P2P online lending platforms. The proposed approach is innovative and efficient in the P2P online lending field.

Considering the emergence of Live Streaming Shopping (LSS) in Europe and the scant academic coverage it has attracted in terms of characterization, this study's aim is twofold: (1) explore how both consumers and retailers assess the distinctive attributes of LSS in order to identify its characteristics as a new form of commerce, or even retail format, and (2) analyze the motivations of both consumers and retailers for choosing it.

A hybrid literature review – bibliometric and structured – summarizes the body of research available on LSS. Considering the research gap and the authors' two research questions, a qualitative methodology was adopted. This incorporates three primary data sets, collected in France from LSS experts, retail managers and consumers over 18 months.

LSS is a new online retail format, to be added to the range of existing options as it exhibits a typical retailing mix that is substantially different from other formats. From the retailer's perspective, three main motivations are identified while for the consumer, a total of seven motivations to participate in LSS sessions emerged. This new online format appears to be part of the continuum reflecting omnichannel integration.

Once the LSS literature has been further developed, the bibliometric review could be repeated to better map the field within contemporary research.

Today's retailers cannot ignore LSS: it is a multifaceted hybrid format and touchpoint that is part of an omnichannel strategy and through which both quantitative and qualitative objectives can be achieved.

This paper (1) structures the emerging literature on LSS by developing the first hybrid literature review on the topic; (2) substantiates what characterizes LSS as a retail format from the consumer and retailer perspectives and a retail touchpoint in the consumer's omnichannel journey; and (3) proposes a definition of LSS and a research agenda.

The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths.

Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities.

This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions.

It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities.

Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities.

This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.

On December 20, 2021, Viya, a social media influencer (SMI) with the largest number of followers in China, was exposed for having evaded RMB 643 million in taxes during 2019 and 2020. Consequently, she was fined a total of RMB 1.341 billion by the tax authorities. While the strict government regulations demonstrated in the Viya event may build confidence in the consumers for future purchases, the exposure of issues and problems through implementation of the stronger government regulations may warn consumers off. Thus, the main objective of this paper is to examine the effect of government regulations on consumers' usage intentions of live streaming e-commerce by taking the Viya event as an example.

The authors consider both the positive effect of consumers' perceived benefits of the government regulations and the negative effect of their perceived risks of the Viya event on the usage intentions of live streaming e-commerce. After collecting 314 subjects with diverse gender, ages, education levels and income profiles, the data are processed by partial least squares-based structural equation modeling (PLS-SEM) and SmartPLS software.

The results demonstrate that strict government regulations can build trust in consumers of live streaming e-commerce by increasing the perceived benefits of restricting the behavior of SMIs. Among the potential perceived risks (social risk, safety risk and psychological risk), the safety risk is supported to have a negative effect on consumers' trust in live streaming e-commerce platforms. Besides, the authors have also identified different types of usage intentions in live streaming e-commerce, i.e. watching intention and purchase intention, and have empirical support for the positive relationships between the consumers' trust in live streaming e-commerce platforms and different usage intentions.

The authors' findings contribute to the application of commitment-trust theory, institutional theory and organizational control theory in the context of the live streaming e-commerce industry. Particularly, the authors use the Viya event as an example to quantitatively examine the effects of strict government regulations, which enriches the existing literature on this topic.

The promotion of new energy vehicles (EVs) is an effective way to achieve low carbon emission reduction. This paper aims to investigate the optimal pricing of automotive supply chain members in the context of dual policy implementation while considering consumers' low-carbon preferences.

This article takes manufacturers, retailers and consumers in a main three-level supply chain as the research object. Stackelberg game theory is used as the theoretical guidance. A game model in which the manufacturer is the leader and the retailer is the follower is established. The author also considered the impact of carbon tax policies, subsidy policies and consumer preferences on the results. Furthermore, the author investigates the optimal decision-making problem under the profit maximization model.

Through model solving, it is found that the pricing of EVs is positively correlated with the unit price of carbon and the amount of subsidies. The following conclusions can be obtained by numerical analysis of each parameter. Changes in carbon prices have a greater impact on conventional gasoline vehicles. Based on the numerical analysis of parameter β, it is also found that when the government subsidizes consumers, supply chain members will increase their prices to obtain partial subsidies. Compared with retailers, low-carbon preferences have a greater impact on manufacturers.

The new energy automobile industry involves many policies, including tax cuts, tax exemptions and subsidies. The policy environment faced by the members of a supply chain is complex and diverse. Therefore, the analysis in this article is based only on partial policies.

The authors innovatively combine the three factors of subsidy policy, carbon tax policy and consumer low-carbon preference, with research on the pricing of EVs. The influence of policy factors and consumer preferences on the pricing of EVs is studied.