Search results

Accurate information is the basis for well-informed decision-making, which is particularly challenging in the dynamic reality of a pandemic. Search engines are a major gateway for obtaining information, yet little is known about the quality and scientific accuracy of information answering conspiracy-related queries about COVID-19, especially outside of English-speaking countries and languages.

The authors conducted an algorithmic audit of Google Search, emulating search queries about COVID-19 conspiracy theories in 10 different locations and four languages (English, Arabic, Russian, and Hebrew) and used content analysis by native language speakers to examine the quality of the available information.

Searching the same conspiracies in different languages led to fundamentally different results. English had the largest share of 52% high-quality scientific information. The average quality score of the English-language results was significantly higher than in Russian and Arabic. Non-English languages had a considerably higher percentage of conspiracy-supporting content. In Russian, nearly 40% of the results supported conspiracies compared to 18% in English.

This study’s findings highlight structural differences that significantly limit access to high-quality, balanced, and accurate information about the pandemic, despite its existence on the Internet in another language. Addressing these gaps has the potential to improve individual decision-making collective outcomes for non-English societies.

In recent years, deep learning and extended reality (XR) technologies have gained popularity in the built environment, especially in construction engineering and management. A significant amount of research efforts has been thus dedicated to the automation of construction-related activities and visualization of the construction process. The purpose of this study is to investigate potential research opportunities in the integration of deep learning and XR technologies in construction engineering and management.

This study presents a literature review of 164 research articles published in Scopus from 2006 to 2021, based on strict data acquisition criteria. A mixed review method, consisting of a scientometric analysis and systematic review, is conducted in this study to identify research gaps and propose future research directions.

The proposed research directions can be categorized into four areas, including realism of training simulations; integration of visual and audio-based classification; automated hazard detection in head-mounted displays (HMDs); and context awareness in HMDs.

This study contributes to the body of knowledge by identifying the necessity of integrating deep learning and XR technologies in facilitating the construction engineering and management process.

This paper explores the relevance of the concept of self in the process of independent technological innovation. In‐depth interviews were conducted with technological innovators from start‐up firms in IT, biotech and advanced services concerning the subjective and social forms of engagement in the innovation process. Emerging factors in the interview data revealed aspects pertaining to the innovator’s reflexive self‐conception, innovator ego‐involvement in the venture, forms of commitment and control, personal and social stakes, and various self‐oriented cognitive strategies. It is argued that the self‐concept allows the innovator to come into view as a social and subjective being who is involved in reflexive activities such as dynamic role‐taking, “is” vs “ought” reflections and social negotiations.

The purpose of this paper is to explore the effects of demand planning practices on the disruptions induced by operational risk. The study reveals whether the negative consequences of operational risk factors (covering demand, supply, control and process risks) can be absorbed or amplified through the application of specific demand planning practices in supply chains.

The study involves the partial least squares path model procedure. Likewise, the items of the constructs in the outer model were subjected to a purification process by principal component analysis with the orthogonal (varimax) and oblique (Promax) methods of rotation.

The findings suggest that although one may not observe uniformity and standardization in the role of demand planning in alleviating the negative effects of operational risks, still some regularities can be obtained. Having said that some demand planning practices tend to mitigate or reinforce disruptions driven by operational risk, whereas the other practices simultaneously absorb and amplify disruptions driven by operational risk.

The study shows that different managerial instruments, which are not inherently dedicated to risk management, when appropriately applied, may have an indirect impact on the mitigation of supply chain risk. In particular, the concept of demand planning might be very helpful for managers when dealing with demand and control risks.

The study simultaneously examines a more detailed bundle of practices forming the demand planning process. The research attempts to investigate the link between the demand planning process and operational risk consequences, derived from all sources (supply, demand, process and control). The paper shows that risk management is not a sole tool to mitigate disruptions. Among the concepts, which contribute to decrease risks is the demand planning process. The study demonstrates that the demand planning process when applied as a component of supply chain management, may contribute to mitigate certain operational risks.

This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.

Using the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.

The event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.

The authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.