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In the digital economy, as artificial intelligence applications increase, big data analytical capability (BDAC) plays a crucial role, and intellectual capital is growing in importance. This study aims to examine the possible benefits and effects of intellectual capital, BDAC and integrations on operational performance.

This study collected data from firms in Asia, and a total of 257 senior managers completed a questionnaire. Confirmatory factor analysis and structural equation modeling (SEM) is used for statistical analysis.

Intellectual capital positively correlates with BDAC, and BDAC positively relates to internal integration but not to external integration. Furthermore, both internal integration and external integration positively correlate with operational performance. This study supports that internal integration is a significant mediator in the influence of BDAC on operational performance.

First, the authors provide empirical evidence that intelligent capital in intangible resources helps firms to build BDAC. Second, this study stresses the importance of BDAC in business, which enhances the integration of the whole supply chain and results in superior operational performance.

This is a first attempt from the perspective of intelligent capital and uses SEM to emphasize the relationships among BDAC, supply chain integration and performance based on unique and irreplaceable intangible resources, thus providing a new perspective on the contributing factors of BDAC.

The purpose of this paper is to investigate the relationships among corporate social responsibility (CSR), analyst forecast accuracy and firms’ earnings management behavior using US-based firms.

The authors use the Kinder, Lydenberg, Domini (KLD) database to construct CSR performance scores and divide all firms into ten groups from high to low as a proxy for CSR performance. The authors obtained an initial sample of 33,364 firm-year observations from 1991 to 2012. Filtering for records which exist in the KLD, Compustat, and Center for Research in Security Prices databases lefts a total of 16,807 firm-year observations and CSR evaluation reports for 5,896 firms.

The authors find that high CSR-score firms have lower rates of analyst forecast error than their low CSR-score counterparts, suggesting that CSR performance is a useful means of forecasting earnings. Furthermore, firms with better CSR performance have significantly lower accrual-based earnings management behavior. However, the level of the manipulation behavior of real earnings management (REM) activities increased significantly in better CSR firms, suggesting that high CSR-score firms substituted REM methods for accrual-based methods. REM methods are consistent with the stipulations of the Sarbanes-Oxley Act and allow high CSR-score firms to better manipulate earnings behavior. These results hold after the authors control for various factors related to firm financial characteristics.

Overall, the findings have important implications for investors and regulators to more easily assess firms’ earnings manipulation behavior and earnings stability under CSR performance and financial information in financial markets.

The purpose of this paper is to examine whether socially responsible firms behave differently from other firms in terms of financial risk using US-based firms from 1991 to 2012.

The authors used the KLD social performance rating scores as the measure of corporate social responsibility (CSR) performance and obtained an initial sample of 38,158 firm-year observations from 1991 to 2012. The authors obtained the monthly consensus earnings forecast for fiscal year one and the monthly dispersions for these earnings forecasts from I/B/E/S, and the bond spread from DataStream database. Specifically, the authors question whether firms that exhibit CSR obtain market approval to reduce financial risk, thereby providing investors and regulators with more reliable and transparent financial information, as opposed to firms that do not meet the same criteria.

The authors find that social responsible firms usually perform better in terms of their credit ratings and have lower credit risk, in terms of loan spreads when compared to corporate bond spreads, and in terms of distance to default. The results control for various measurements for CSR and time periods, consider various CSR dimensions and components, and use alternative proxies to improve the quality of financial risk estimates.

The findings demonstrate the importance of considering both positive and negative CSR performance. Positive CSR ratings are associated with reduced financial risk while negative CSR performance scores lead to increased financial distress. Investors respond to positive CSR ratings.

Driving cycle is an essential requirement to evaluate the exhaust emissions of various types of vehicles on the chassis dynamometer test. This study presents a real world comparison of the driving cycles of Edinburgh motorcycles in two world cities; Edinburgh in Scotland and Delhi in India. The two driving cycles (EMDC & DMDC) driving cycle (EMDC) that were was developed through the analysis of experimental data. This data was collected from trips on a number of routes in each city. In Edinburgh, five different routes between the home addresses in the surrounding areas and place of work at Edinburgh Napier University in Edinburgh were selected. In Delhi data were collected in East Delhi (Geeta Calony) to Central Delhi (Raisena Road). The data collected data was divided into two categories of urban and rural roads in the case of Edinburgh while it was only the urban route in Delhi.. Forty four trips were made on the five designated routes in both urban and rural areas and 12 trips were made in Delhi. The aims of the study were to assess the various parameters (i.e. motorcycle speed, cruise, accelerations and decelerations and percentage time spent in idling) and their statistical validity over total trip lengths for producing a real world EMDC in each of the two cities. The results show that EMDC in Edinburgh, the EMDC has a cycle length of 770 and 656 seconds for urban and rural trips, respectively, which was found more than ECE cycle length. Time spent in acceleration and deceleration modes were found to be significantly higher than any other driving cycle reported to date for motorcycles, reflecting a typical characteristic of the driving cycle in Edinburgh; this was presumably due to diverse driving conditions of motorcycles in the city. In Delhi on the other hand, the DMDC has a cycle length of 847.5 seconds for the urban trips, which higher than that of the EMDC length. The overall percentage time spent in acceleration in Delhi was higher than that of Edinburgh while the time spent in deceleration was lower in Delhi. The overall average speed in the case of Delhi was slightly higher than that of Edinburgh.

It has been noted that the spin-valve sensor exhibits lower sensitivity with higher temperature because of the variation of GMR ratio, which could lead to the measurement error in applications where working temperature changes largely over seasons or times. This paper aims to investigate and compensate the temperature effect of the spin-valve sensor.

A spin-valve sensor is fabricated based on microelectronic process, and its temperature relevant properties are investigated, in which the transfer curves are acquired within a temperature range of −50°C to 125°C with a Helmholtz coil and temperature chamber.

It is found that the sensitivity of spin-valve sensor decreases with temperature linearly, where the temperature coefficient is calculated at −0.25 %/°C. The relationship between sensitivity of spin-valve sensor and temperature is well-modeled.

The temperature drift model of the spin-valve sensor’s sensitivity is highly correlated with tested results, which could be used to compensate the temperature influence on the sensor output. A self-compensation sensor system is proposed and built based on the expression modeled for the temperature dependence of the sensor, which exhibits a great improvement on temperature stability.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

The purpose of this paper is to present the use of artificial immune systems (AISs) to solve constrained design optimization problems for active magnetic bearings (AMBs).

This research applies the AIS approach, more specifically, a representative clonal selection-based AIS called CLONALG, to the single-objective structural design optimization of AMBs. In addition, when compared with a genetic algorithm (GA) developed in the previous work, the CLONALG fails to produce best solutions when a nearly zero feasible ratio occurs in an AMB design problem. Therefore, an AIS called ARISCO (AIS for constrained optimization) is proposed to address the above issue.

A total of six AMB design cases are solved by the GA, CLONALG, and ARISCO. Based on the simulation results, in terms of solution quality, the ARISCO is shown to have better overall performance than the CLONALG and GA. In particular, when solving a problem with a nearly zero feasible ratio, the ARISCO and GA perform equally and both outperform the CLONALG.

In summary, the contributions of this paper include: this research applies the AIS approach, more precisely, the CLONALG, to the single-objective structural design optimization of AMBs; the ARISCO overall produces better AMB designs than the CLONALG and a GA developed in the previous work; in situations where a nearly zero feasible ratio occurs, the ARISCO and GA perform equally, and they both outperform the CLONALG.

The definition of cycle time is the time from the wafer start to the wafer output. It usually takes one or two months to get the product since customer decides to produce it. The cycle time is a critical factor for customer satisfaction because it represents the response time to the market. Long cycle time reflects the ineffective investment for the capital. The cycle time is very important for foundry because long cycle time will cause customer unsatisfied and the order loss. Consequently, all of the foundries put lots of human source in the cycle time improvement. Usually, we make decisions based on the experience in the cycle time management. We have no mechanism or theory for cycle time management. We do work‐in‐process (WIP) management based on turn rate and standard WIP (STD WIP) set by experiences. But the experience didn’t mean the optimal solution, when the situation changed, the cycle time or the standard WIP will also be changed. The experience will not always be applicable. If we only have the experience and no mechanism, management will not be work out. After interview several foundry fab managers, all of the fab can’t reflect the situation. That is, all of them will have an impact period after product mix or utilization varied. In this study, we want to develop a formula for standard WIP and use statistical process control (SPC) concept to set WIP upper/lower limit level. When WIP exceed the limit level, it will trigger action plans to compensate WIP Profile. If WIP Profile balances, we don’t need too much WIP. So WIP level could be reduced and cycle time also could be reduced.

The purpose of this paper is to extend the utilitarian value of the dedication-based relationship maintenance mechanism of social exchange theory and customer perceived relationship investment to investigate the relationship performance of a retailer launching a self-service technology (SST). Computer anxiety and time consciousness are hypothesized to moderate the effects among these relationships.

The results of the structural equation model, with in-store kiosk use experience data collected for 211 respondents, supported the research model. Multiple regression analysis was used for testing the moderating effects.

The utilitarian value of dedication-based relationship maintenance is related to perceived relationship investment. Higher levels of customer-perceived relationship investment impact relationship performance. Computer anxiety and time consciousness act separately as both partial and full moderators.

First, this study did not consider different kinds of products/services to have different effects with regard to customer cognition. Second, most of the respondents were students, and this is a limitation in business research, because of such factors as lower incomes and higher information technology ability as compared to individuals with other occupations. Third, it is difficult to distinguish whether the level of perceived convenience is due to the convenience stores per se or the in-store kiosks that they have. Future research may thus consider analyzing in more detail how perceived convenience is evoked. Finally, future research can consider constraint-based relationship maintenance mechanisms with regard to operating in-store kiosk businesses.

Retailers who are willing to continually launch SSTs should tie such efforts to their relationship marketing strategies. Moreover, retailers who are willing to launch e-businesses should establish strategies designed to enhance customer experience with regard to the use of technology. Finally, launching SSTs should involve the continual development of an effective purchasing process and functional relationship marketing strategies.

This paper can help managers organize relationship maintenance mechanisms, especially with regard to the development of user utilitarian value, in order to obtain improved relationship performance.

Social networking sites (SNSs) have significantly influenced people's lives and changed their behavior. Although previous research has explored self-disclosure in virtual communities, little is known about the impact of other users, particularly their online social support, on self-disclosure. The aim of this study is to explore how online social support dimensions (i.e., emotional, informational, esteem, instrumental and network support) influence people's self-disclosure, which in turn affects their commitment to SNSs.

Based on social exchange theory, this study proposes a research model that explores the role of other users on self-disclosure. This study collects data from a sample of 558 respondents and applies the structural equation modeling technique to test the research model.

The findings show that users are motivated to disclose information and commit to a specific SNS because of the supportive climate. Results also show that self-disclosure mediates the effect of online social support on users' commitment to SNSs.

This study focuses on the influence of other users' roles on self-disclosure on SNSs, extending the application of social exchange theory.