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This paper aims to present spot pattern welding (SPW) as a scanning strategy for laser-foil-printing (LFP) additive manufacturing (AM) in place of the previously used continuous pattern welding (CPW) (line-raster scanning). The SPW strategy involves generating a sequence of overlapping spot welds on the metal foil, allowing the laser to form dense and uniform weld beads. This in turn reduces thermal gradients, promotes material consolidation and helps mitigate process-related risks such as thermal cracking, porosity, keyholing and Marangoni effects.

304L stainless steel (SS) feedstock is used to fabricate test specimens using the LFP system. Imaging techniques are used to examine the melt pool dimensions and layer bonding. In addition, the parts are evaluated for residual stresses, mechanical strength and grain size.

Compared to CPW, SPW provides a more reliable heating/cooling relationship that is less dependent on part geometry. The overlapping spot welds distribute heat more evenly, minimizing the risk of elevated temperatures during the AM process. In addition, the resulting dense and uniform weld beads contribute to lower residual stresses in the printed part.

To the best of the authors’ knowledge, this is the first study to thoroughly investigate SPW as a scanning strategy using the LFP process. In general, SPW presents a promising strategy for securing embedded sensors into LFP parts while minimizing residual stresses.

This study examines the relationship between Environmental, Social and Governance (ESG) practices and financial distress in times of uncertainty.

Thomson Reuters ESG database, Compustat and Center for Research in Security Prices (CRSP) were used to derive a final sample size of 1,572 firms and 11,618 firm-year observations from 2003 to 2022. Fixed-effects regression was used to analyze the data.

It was found that increasing ESG involvement leads to an increase in Z score (i.e. lower financial distress), and this impact was more profound during the COVID-19 period and also when firms' innovativeness increased. However, during the COVID-19 period, increases in capital expenditures weaken the positive effect of ESG on financial distress.

This study contributes to the growing body of literature on the impact of ESG performance on financial distress and the nature of this relationship during times of uncertainty such as COVID-19.

This study offers insights to managers and practitioners when developing their corporate financial strategies, particularly financial distress management, showing the potential benefits of innovativeness and capital intensity during turbulent times similar to COVID-19.

Little knowledge exists on how ESG engagement helps weather financial distress during periods of uncertainty due to external shocks (e.g. COVID-19). This paper looks at the effect of ESG engagement on financial distress and how capital intensity and innovativeness could influence this relationship while giving fresh insights into the impact of COVID-19.

This study analyses the effects of firm efficiency on firm performance and how controlling shareholders moderate the link between the two variables.

This study employs data envelopment analysis to estimate firm efficiency and the panel regression method to assess the hypothesised relationships among 1,295 firm-year observations of publicly listed firms in Malaysia from 2015 to 2019.

The results indicate that firm efficiency (technical efficiency, pure technical efficiency and scale efficiency) has mixed relationships with firm performance (return on assets, market-to-book ratio and operating cash flows), all of which are being moderated by controlling shareholdings.

This study highlights the importance of assessing firm efficiency as the key success factor for improving firm performance. Industrial managers should manage efficiently their resources or operating costs in achieving their corporate financial goals. Moreover, this study notes the presence of controlling shareholders, who can be either self-interested or company goal aligned.

This study suggests becoming efficient in transforming inputs into outputs is a prerequisite before investigating accrual-based and cash-based firm performance measures, and the presence of controlling shareholders matters in these regards.

The use of enterprise social media (ESM) can promote knowledge sharing within project teams. However, the potential mechanism of ESM affordances influencing knowledge sharing has not been fully studied. This paper aims to develop a theoretical model to explore how individual psychological cognition and environmental factors affect ESM affordances.

An empirical research using ESM applications was conducted in China, and 214 valid responses were collected for data analysis. Partial least squares structural equation modeling method was performed to test the theoretical model and hypotheses.

The results suggest the following implications: (1) the visibility, persistence, editability and association of ESM affordances all have a positive effect on the effectiveness of knowledge sharing in project teams. (2) The psychological safety and psychological empowerment of team members have a significant positive influence on ESM affordances. (3) The project task complexity positively moderates the positive effects which the visibility and association have on the effectiveness of knowledge sharing, and negatively moderates the positive relationship between the editability and knowledge sharing.

Based on the social cognitive theory, this paper highlights the roles of psychological cognitive factors and project task context in the effect of ESM affordances having on knowledge sharing within project teams. Moreover, it provides valuable suggestions for project managers in project and knowledge management.

Live streaming has become an extremely popular form of online service and allows live audiences to give virtual gifts or money to their favorite streamers. This study aims to investigate the impact of the audience's multidimensional social presence on their attitudes toward live streamers and intentions to give money or gifts to streamers, based on the interactive marketing perspective. This study considers live audiences' gift-giving intentions by integrating the theory of multidimensional social presence, which includes awareness, cognitive social presence and affective social interaction, and the theory of reasoned action (TRA).

This study invited audiences who had watched live streaming from several popular live-streaming platforms to respond to a web questionnaire. The unit of analysis was at the individual level. This study applied the purposive sampling technique for data collection. A sample of 258 eligible responses to the online survey was analyzed using SPSS software and the causal relationships between the measurement variables of this research model were verified through structural equation modeling.

The results indicate that the audiences' awareness of participating in live streaming enhanced their cognitive and affective social presence, which positively affected their attitudes toward live streamers. These attitudes had a further significant effect on their gift-giving intentions. Cognitive social presence and affective social presence were found to play significant mediating roles in the relationship between awareness and attitudes toward live streamers.

This study examines audiences' intention to give gifts to their favorite live streamers, based on the interactive marketing perspective. The interactive relationship between live streamers and online audiences is developed by audience members through the process of inner psychological transformation, which is measured through the multidimensional construct of social presence. This occurs through a mutual influence relationship in which awareness simultaneously influences cognitive social presence and affective social presence, and cognitive social presence impacts affective social presence.

The study contributes three noteworthy findings to the theory development through the integrated perspective of the TRA and the theory of social presence. (1) Exploring the influence of belief factors on internal psychological responses and intention in live streaming to expand an innovative application of the TRA. (2) Adopting the multidimensional social presence can help researchers more clearly describe various live-streaming situations and extend the research scope of the social presence theory to live-streaming interactive marketing strategies. (3) From the perspective of live-streamer marketing, this study broadens the research fields of electronic commerce and interactive marketing.

This study provides four practical implications for platform managers and live streamers. (1) To induce favorable attitudes toward live streamers, live streamers initiate various interactive activities sequentially to establish a social presence with the audience. (2) Live streamers should devote themselves to forming a joyful atmosphere for their followers, as this will trigger audiences' affective social presence to generate positive attitudes and increase followers' intentions. (3) To attract and retain young followers, live streamers must devise interesting content and provide fresh services. (4) Platform managers must create useful widgets to assist live streamers in managing their channels and followers.

Building friendly real-time interaction between the live streamer and the audience is an important task in live streaming and further influences the income of the live streamer and the platform. The study provides an effective approach to building friendly real-time interaction for the live streamer and manager of live-streaming electronic commerce through the interactive marketing perspective. The approach can help the live streamer manage nice communication with their audience and obtain virtual money and gift-giving from the audience.

Social live streaming services (SLSS) have infused gamification into interface design and feature applications. Firms adopt gamification mechanisms to win customer loyalty in the live streaming and SLSS markets. Based on the mechanics-dynamics-aesthetics (MDA) framework and uses and gratifications 2.0 theory (UGT 2.0), this study aims to investigate the effects of game mechanics (mechanics) on enjoyment and user retention (aesthetics) through rewards and social interaction (dynamics) in the context of SLSS.

This study used an online survey via Google Forms, SurveyCake and social media platforms like Facebook, Instagram and Line to collect data from 232 SLSS users in Taiwan. Partial least squares structural equation modeling (PLS-SEM) was adopted to analyze the data.

The results validated the relationships between game mechanics and dynamic elements (rewards and social interaction) that triggered aesthetic elements (enjoyment feelings) among users. In addition, users experienced a sense of enjoyment that led to usage retention when using the gamified SLSS. Further, this study found enjoyment crucial for users to stay interactive with gamified services.

Driven by UGT 2.0, this study closed the gaps by integrating the MDA framework into the SLSS context and better understanding how game mechanics are connected to rewards and social interaction, leading to enjoyment and user retention when using SLSS. This study provides fresh insights into gamification-oriented SLSS practices. It offers significant theoretical and managerial implications and provides guidelines for SLSS platform operators on fostering user retention.

The present study is performed to identify the propagation mechanism of the ripple effect as well as examine the simultaneous impact of risks on supply chain (SC) performance.

A theoretical framework with many hypotheses regarding the relationships between SC risk types and performance is established. The data are collected from a large-scale survey supported by a project of the Japanese government to promote sustainable socioeconomic development for the Association of Southeast Asian Nations (ASEAN) region, with the participation of 207 firms. Structural equation modeling (SEM) is used to test the hypotheses of the theoretical framework.

It is indicated that human-made risk causes operational risk, while natural risk causes both supply risk and operational risk. Furthermore, the impacts of human-made risk and natural risk on performance are amplified through operational risk.

This study is one of the first attempts that identifies the propagation mechanism of the ripple effect and examines the simultaneous impact of risks on performance in construction SCs.

Although many studies on risk management in construction SCs have been carried out, they mainly focus on risk identification or quantification of risk impact. It is observed that research on the ripple effect of disruptions has been very scarce.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

This paper aims to analyze the direct and indirect effects of investor protection on forced CEO turnover.

The authors investigate 5,175 firm-year observations from 16 European countries over 2012–2018, collect data on four national investor protection indicators, identify 196 forced CEO turnovers and use multiple logistic regression models.

The results show that a reduction in the degree of investor protection significantly increases the probability of a forced change of the company’s CEO. Furthermore, when the degree of investor protection increases, directors are attributed a lower degree of responsibility in the event of a decline in earnings performance. Therefore, the relation between a decrease in profitability and a forced change of CEO is reduced.

The research is focused on countries belonging to the European Economic Area and most of the investor protection indicators are derived from surveys. Concerning policy implications, the findings suggest that regulators should focus on the effective enforcement of investor protection mechanisms.

The results confirm that characteristics at the country level have an impact on corporate decisions, highlighting the importance of increasing the degree of investor protection as a means of mitigating agency conflicts and improving stewardship.

To the best of the authors’ knowledge, this study explores a relatively underinvestigated topic as it uses investor protection indicators to jointly evaluate both direct and indirect effects on forced changes of CEO through cross-national research.

The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders.

This study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further.

The results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy.

One limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics.

The study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools.

Utilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision.