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The authors test the weak-form efficiency in cryptocurrency markets using the most recent and comprehensive data as of 2021. The authors apply various technical indicators to take a long or short position on 99 cryptocurrencies and compare the 10-day returns based on the technical trading strategies to the simple buy-and-hold returns. The authors find that the trading strategies based on single indicators or the combination of two indicators do not generate higher returns than buy-and-hold returns among cryptos. These findings suggest that cryptocurrency markets are weak-form efficient in general.

This study aims to determine whether, by means of the application of genetic algorithms (GA) through the traditional technical analysis (TA) using moving average convergence/divergence (MACD), is possible to achieve higher yields than those that would be obtained using technical analysis investment strategies following a traditional approach (TA) and the buy and hold (B&H) strategy.

The study was carried out based on the daily price records of the NASDAQ financial asset during 2013–2017. TA approach was carried out under graphical analysis applying the standard MACD. GA approach took place by chromosome encoding, fitness evaluation and genetic operators. Traditional genetic operators (i.e. crossover and mutation) were adopted as based on the chromosome customization and fitness evaluation. The chromosome encoding stage used MACD to represent the genes of each chromosome to encode the parameters of MACD in a chromosome. For each chromosome, buy and sell indexes of the strategy were considered. Fitness evaluation served to defining the evaluation strategy of the chromosomes in the population according to the fitness function using the returns gained in each chromosome.

The paper provides empirical-theoretical insights about the effectiveness of GA to overcome the investment strategies based on MACD and B&H by achieving 5 and 11% higher returns per year, respectively. GA-based approach was additionally capable of improving the return-to-risk ratio of the investment.

Limitations deal with the fact that the study was carried out on US markets conditions and data which hamper its application in some extend to markets with not as much development.

The findings suggest that not only skilled but also amateur investors may opt for investment strategies based on GA aiming at refining profitable financial signals to their advantage.

This paper looks at machine learning as an up-to-date tool with great potential for increasing effectiveness in profits when applied into TA investment approaches using MACD in well-developed stock markets.

In the age of digital media, customers have access to vast digital information sources, within and outside a company's direct control. Yet managers lack a metric to capture customers' cross-media exposure and its ramifications for individual customer journeys. To solve this issue, this article introduces media entropy as a new metric for assessing cross-media exposure on the individual customer level and illustrates its effect on consumers' purchase decisions.

Building on information and signalling theory, this study proposes the entropy of company-controlled and peer-driven media sources as a measure of cross-media exposure. A probit model analyses individual-level customer journey data across more than 25,000 digital and traditional media touchpoints.

Cross-media exposure, measured as the entropy of information sources in a customer journey, drives purchase decisions. The positive effect is particularly pronounced for (1) digital (online) versus traditional (offline) media environments, (2) customers who currently do not own the brand and (3) brands that customers perceive as weak.

The proposed metric of cross-media exposure can help managers understand customers' information structures in pre-purchase phases. Assessing the consequences of customers' cross-media exposure is especially relevant for service companies that seek to support customers' information search efforts. Marketing agencies, consultancies and platform providers also need actionable customer journey metrics, particularly in early stages of the journey.

Service managers and marketers can integrate the media entropy metric into their marketing dashboards and use it to steer their investments in different media types. Researchers can include the metric in empirical models to explore customers' omni-channel journeys.

This study aims to investigate Malaysian stock market efficiency from the view of Sharīʿah-compliant and conventional stocks based on the effectiveness of technical trading strategies.

This study uses unconventional trading strategies that mix buy recommendations of Bursa Malaysia analysts with sell signals generated from 10 selected technical trading strategies (simple moving average, moving average envelopes, Bollinger Bands, momentum, commodity channel index, relative strength index, stochastic, Williams percentage range, moving average convergence divergence oscillator and shooting star) that are detected using ChartNexus. The period from 1 January 2013 until 31 December 2015 produces a total sample consisting of 1,265 buy recommendations of 125 Sharīʿah-compliant stocks and 400 buy recommendations of conventional stocks. The study period is extended until 31 March 2016 to provide an ample time for detecting the sell signal especially for buy recommendations that are released towards the end of 2015.

The resulting Jensen’s alpha show 8 out of 10 strategies are effective in generating abnormal returns in Sharīʿah-compliant samples while only 3 out of 10 strategies are effective in conventional samples. Prominent effectiveness of technical trading strategies in Sharīʿah-compliant stocks implies clear inefficiency in that stock market segment as opposed to those of the conventional stocks.

The results based on unconventional trading strategies provide new insights of Malaysian stock market efficiency especially in Sharīʿah-compliant and conventional stocks. The paper provides more robust findings on market efficiency as firms’ equity level data were focussed together with analysts’ buy recommendations from Bursa Malaysia.

The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driver’s physiological states.

Field tests with 17 participants were conducted in the connected and automated vehicle test field. All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed. Demographic data, vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit, such as electrocardiograph for heart rate, electromyography for muscle strength, electrodermal activity for skin conductance and force-sensing resistor for braking pressure.

This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs. The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driver’s take-over maneuver is needed.

The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time. Therefore, the driver states in distracted driving are significantly different than in regular driving, the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics. To the best of the authors’ knowledge, this is the first study using psychophysiological measures to quantify a driver’s response to an emergency stop during distracted driving.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

Electromagnetic interference (EMI) on communication systems of unban rail transit can hardly be clarified because of complicated factors around railways. This paper aims to target this issue and extend experimental and theoretical analysis.

This paper take the Nanjing Dashengguan Bridge as an example, because it carries the most tracks in the world and bears three kinds of trains running through, providing a perfect complex environment. First, it investigates the three communication systems, terrestrial trunked radio, communications-based train control (CBTC) and passenger information system (PIS) that Nanjing Metro uses, and select appropriate devices accordingly. Second, it establishes a system level platform and conduct three tests to analyze their respective operating principles and performance difference under common electromagnetic environments. Third, it adopts theoretical formula to verify test results.

The experiment results and theoretical analysis mutually corroborate each other and present practical recommendations: an 8 m or more distance between two tracks will ensure no obvious EMI created by a passing train on communication systems; two certain communication systems should not share the same frequency band; interference level is more related to field strength than weathers and building materials; and CBTC DSSS waveguide mode as well as PIS LTE mode are preferred.

This research also provides a practical method of investigating EMI for other complex situations.

This paper aims to determine what the brand performance consequences of corporate social responsibility (CSR) activities would be during times of recession for well-known brands.

Based on signaling theory, this paper investigates if CSR activities serve to signal higher brand value for consumers via perceptions of better quality and greater differentiation, specifically during recessions. This study incorporates a representative longitudinal sample of known US firms for the analyses, which is accomplished through generalized method of moments estimations.

The findings empirically demonstrate that CSR initiatives during recessions are actually associated with increased perceptions of brand value. More specifically, during recessions, CSR initiatives such as charitable contributions provide a signal to customers of higher brand quality.

This study did not control for the costs of doing specific CSR activities that may be less visible to consumers.

While individual firms or managers may not be able to prevent recessions from happening, they can limit the negative impact of recessions on their performance by engaging in CSR activities (or refrain from cutting back) during these times.

Because CSR initiatives during recessions result in more favorable consumer perceptions of the brand, engaging in CSR aligns both social and managerial interests, owing to the economic gains from CSR investments.

During times of recession, some critics indicate that CSR may be an unaffordable luxury. On the contrary, this research shows that managers may want to consider CSR activities as a means of increasing the value of their brands, especially during economic recessions.

The purpose of this paper is to analyze the impact of employees’ perception of performance appraisal (PA) practices on innovative behavior (IB). The authors also propose to analyze consistency, a dimension of Human Resource Management (HRM) system strength, as a moderating variable in the aforementioned relationship.

A quantitative study was conducted, using a sample of 166 employees carrying out highly qualified, intensive knowledge jobs in four industrial companies in the Valencian region of Spain. The hypotheses were tested by applying the Smart-PLS 3.2 software.

The findings confirmed that in a context of professional and qualified work, PA practices have a direct and positive effect on IB. However, the results obtained did not enable us to affirm that employee perceptions of the consistency of the HR system moderated the relationship between PA and IB.

The paper’s originality lies in including the role of consistency, a dimension of HRM system strength, in the analyzed relationship. When employees believe that PA achieves the goals for which this practice was designed, and that this appraisal follows a clear strategic direction over time, they perceive that the assessment system is not arbitrary, i.e., that this HR practice is being applied consistently. Consequently, the present work shows the relevant role of the perception of consistent PA when the firm wants to encourage IB. This fact opens up the field to study how to define and implement control mechanisms that tell managers whether there is a fit between employees’ perceptions and the intentionality of the HRP these managers have defined.

本研究旨在分析僱員對工作表現評核實務的看法，如何影響其創新行為。作者們亦建議去分析人力資源管理系統的其中一個優點特質： 一致性，並且以一致性作為上述關係的一個調節變量來進行分析。

此為一項定量研究。樣本為166名在西班牙巴倫西亞地區四間工業公司內從事高質素及知識密度高的工作的僱員。各項假設已應用了Smart-PLS 3.2 軟件進行了測試。

研究結果確認了在專業及合資格工作的環境下，工作表現評核實務對創新行為會起直接及積極的影響。唯取得的研究結果不能讓我們確認僱員對人力資源系統一致性的看法、調節了表現評核與創新行為之間的關係。

本研究的原創性、在於就該關係的分析研究內，納入了人力資源管理系統強度的一個層面 -一致性 - 的角色。若僱員相信表現評核達到其設立的預期目的，並相信該評核長期跟隨著一個清晰的戰略方向，他們會覺得有關的評核制度不是隨機的；即是說，他們會覺得該人力資源實務是一貫地應用的。因此，本研究明確顯示了如公司欲鼓勵創新行為，僱員覺得表現評核有一致性這個觀感是有其相關角色的。這項事實、開啟了研究如何界定及執行機制，讓主管知悉員工的看法與他們所釐定的人力資源實務的目的是否吻合。﹞

The purpose of this research work is to examine the financial effect of supply chain disruptions (SCDs) caused by coronavirus disease 2019 (COVID-19) and how the magnitude of such effects depends on event time and space that may moderate the signaling environment for shareholder behaviors during the pandemic.

This study analyses a sample of 206 SCD events attributed to COVID-19 made by 145 publicly traded firms headquartered in 21 countries for a period between 2020 and 2021. Change in shareholder value is estimated by employing a multi-country event study, followed by estimating the differential effect of SCDs due to the pandemic by event time and space.

On average, SCDs due to pandemic decrease shareholder value by −2.16%, which is similar to that of pre-pandemic SCDs (88 events for 2018–2019). This negative market reaction remains unchanged regardless of whether stringency measures of the firm's country become more severe. Supply-side disruptions like shutdowns result in a more negative stock market reaction than demand-side disruptions like price hikes. To shareholder value, firm's upstream or downstream position does not matter, but supply chain complexity serves as a positive signal.

This study provides the first empirical evidence on the financial impact of SCDs induced by COVID-19. Combining with signaling theory and event system theory, this study provides a new boundary condition that explains the impact mechanism of SCDs caused by the pandemic.