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This paper explores how task characteristics in terms of trigger type and task topic influence individual participation in community-based free/libre open source software (FLOSS) development by considering participation in individual tasks rather than entire projects.

A quantitative study was designed using choose tasks that were carried out via the email discourse on the developers' email fora in five FLOSS projects. Choice process episodes were selected as the unit of analysis and were coded for the task trigger and topic. The impact of these factors on participation (i.e. the numbers of participants and messages) was assessed by regression.

The results reveal differences in participation related to different task triggers and task topics. Further, the results suggest the mediating role of the number of participants in the relationships between task characteristics and the number of messages. The authors also speculate that project type serves as a boundary condition restricting the impacts of task characteristics on the number of participants and propose this relationship for future research.

Empirical support was provided to the important effects of different task characteristics on individual participation behaviors in FLOSS development tasks.

The findings can help FLOSS participants understand participation patterns in different tasks and choose the types of tasks to attend to.

This research explores the impact of task characteristics on participation in FLOSS development at the task level, while prior research on participation in FLOSS development has focused mainly on factors at the individual and/or project levels.

The purpose of this paper is to compare the relative impacts of trust and risk on individual’s transaction intention in consumer-to-consumer (C2C) e-marketplaces from both the buyers’ and the sellers’ perspectives.

Two surveys were used to collect data regarding buyers’ and sellers’ perceptions and transaction intentions at a typical C2C e-marketplace. Partial least squares was used to analyze the data. A complementary qualitative study was conducted to triangulate the results from the quantitative study.

Institution-based trust (IBT) exerts a stronger influence on transaction intentions for buyers than for sellers. Sellers perceive a stronger impact of trust in intermediary (TII) than buyers on transaction intentions. The impacts of perceived risk in transactions are not different between buyers and sellers. Furthermore, IBT mediates the impacts of TII and perceived risk on transaction intentions for buyers.

The results indicate that the impacts of trust and risk on transaction intention in e-marketplaces do differ between buyers and sellers. This suggests a need to further investigate the buyer–seller difference in online transactions.

Intermediaries need to focus on different types of trust-building mechanisms when attracting buyers and sellers to make transactions in the e-marketplace.

C2C e-marketplaces cannot survive without participation from both buyers and sellers. Most prior research is conducted from the buyers’ perspective. This research sets a starting point for future research to further explore the differences between buyers’ and sellers’ behavior in C2C e-commerce environments.

Nitrogen oxides (NO_x) have been considered as primarily responsible for many serious environmental problems. Removing NO is the key task to remove NO_x hazards. To clarify, NO removal process for pitch-based spherical-activated carbons (PSACs), an online prediction and optimization technique in real-time based on support vector machine algorithm in regression (support vector regression [SVR]) is discussed. The purpose of this paper is to develop a predictor and optimizer system on selective catalytic reduction of NO (SCRN) using experimental data and data-driven SVR intelligence methods.

Predictor and optimizer using developed SVR have been proposed. To modify the training efficiency of SVR, the authors especially customize batch normalization and k-fold cross-validation techniques according to the unique characteristics of PSACs model.

The results present that SVR provides a property regression model since it can linkage linear and non-linear process and property relationships in few experimental data sets. Also, the integrated normalization and k-fold cross-validation show a satisfying improvement and results for SVR optimization. The predicted results of predictor and optimizer in single and double factor systems are in excellent agreement with the experimental data.

SCRN-PO for predicting and optimization SCRN problems is developed by data-driven methods. The outperformed SCRN-PO system is used to predict multiple-factors property parameters and obtain optimum technological parameters in real-time. Also, experiment duration is greatly shortened.

This study empirically investigates the long-run and interactive effect of Chinese foreign direct investment (CFDI) on Africa's industrialization process.

The authors employed industry and manufacturing value-added (% GDP) as the dependent variables and applied the two-step GMM and panel-corrected standard errors' (PCSE) techniques involving a panel of 49 African countries from 2003 to 2020.

The industry value-added (% GDP) results show that the presence of CFDI propels industrial productivity by contributing to value-addition in the short and long run. Moreover, the study shows that the magnitude of the CFDI effect on industrialization is pronounced in the short-run when it is associated with labor and natural resources. This result reveals efficiency-seeking behavior of CFDI and the CFDI-Africa industrialization nexus is not primarily resource-driven. More importantly, the authors found human capital, electricity and political stability, as primary factors that magnify CFDI's effect on industrialization in the short and long run.

This study is the first to use macro-level data to empirically investigate and find the significant effect of CFDI on Africa's industrialization in the long run. More importantly, the authors investigated channels through which CFDI magnifies industrialization in Africa in the short and long run.

Informal learning networks are critical to response to calls for practitioners to reskill and upskill in off-site construction projects. With the transition to the coronavirus disease 2019 (COVID-19) pandemic, social media-enabled online knowledge communities play an increasingly important role in acquiring and disseminating off-site construction knowledge. Proximity has been identified as a key factor in facilitating interactive learning, yet which type of proximity is effective in promoting online and offline knowledge exchange remains unclear. This study takes a relational view to explore the proximity-related antecedents of online and offline learning networks in off-site construction projects, while also examining the subtle differences in the networks' structural patterns.

Five types of proximity (physical, organizational, social, cognitive and personal) between projects members are conceptualized in the theoretical model. Drawing on social foci theory and homophily theory, the research hypotheses are proposed. To test these hypotheses, empirical case studies were conducted on two off-site construction projects during the COVID-19 pandemic. Valid relational data provided by 99 and 145 project members were collected using semi-structured interviews and sociometric questionnaires. Subsequently, multivariate exponential random graph models were developed.

The results show a discrepancy arise in the structural patterns between online and offline learning networks. Offline learning is found to be more strongly influenced by proximity factors than online learning. Specifically, physical, organizational and social proximity are found to be significant predictors of offline knowledge exchange. Cognitive proximity has a negative relationship with offline knowledge exchange but is positively related to online knowledge exchange. Regarding personal proximity, the study found that the homophily effect of hierarchical status merely emerges in offline learning networks. Online knowledge communities amplify the receiver effect of tenure. Furthermore, there appears to be a complementary relationship between online and offline learning networks.

Proximity offers a novel relational perspective for understanding the formation of knowledge exchange connections. This study enriches the literature on informal learning within project teams by revealing how different types of proximity shape learning networks across different channels in off-site construction projects.

The objective of this study is to examine the various factors that exert an influence on the green economy. This study also investigates the impact of foreign direct investment (FDI) on the Malaysian economy, specifically focusing on its position as a mediator. This research also examines the correlation between FDI and its influence on the contemporary green economy.

The authors employed quantitative methodologies and a self-administered survey to evaluate data and derive a definitive conclusion. The result was constructed using SPSS and SEM-PLS as the analytical software.

The study reveals that technological advancement, investment country and government policy significantly and positively affect the green economy, catalyse SDG goals and restructure the economy in better shape.

The current empirical research bridges the research gap in the context of technology advancement in government policy from emerging economies by exploring important factors, proposing their impact on the performance of the green economy, and empirically testing those hypothesized relationships. This study deciphers that FDI influences the green economy, where the investment country plays a significant role. Also, for a graphical presentation of this abstract, see the online appendix.

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.