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Social question and answer (Q&A) systems have been rapidly developed on many e-commerce websites. The purpose of this paper is to explore how social Q&A systems influence consumers' information processing and purchase intention.

The authors design this research based on the information adoption model (IAM). First, the auhors consider the impacts of the central route (information factor) and peripheral route (social factor) on consumers' perception of information usefulness in Q&A systems. Then, the authors verify the influence of information and social aspects on purchase intention and empirically test the model with structural equation modelling (SEM) using 428 effective data samples.

On the whole, the authors prove that purchase intention is influenced by information and social aspects, which are two paths in Q&A systems. Specifically, both answer quality and social presence positively influence information usefulness. Interestingly, respondent credibility and answer consistency do not significantly impact information usefulness. Moreover, information usefulness positively affects information adoption, which positively affects consumer purchase intention.

This paper provides insights on social Q&A system mechanism design.

First, this paper is a useful complement to the research on social Q&A systems on e-commerce websites. Second, the authors provide a new theoretical lens through which the impacts of social Q&A systems on e-commerce websites are understood by extending the IAM. Third, the authors add answer consistency into original information process routes, which obtains a finding that is different from those of prior research.

This paper aims to relate Taoist Yin‐Yang thinking to Western nomology in terms of trust and trust building, seeking to explore the question of how trust impacts on conflict management.

A moderating model of trust in conflict management is proposed. Investigations and observations using primary and secondary data are described. Three cases are presented to explain the moderating effects of adjusting activities and trust on conflict and negotiation.

The proposed model was supported. The following findings have been obtained: keeping a balance between adjusting others and adjusting self is a key to resolving conflict; creating and retaining harmony is a bridge that leads both sides in conflict and negotiation to adjust themselves; taking indirect actions through relationships instead of by direct actions through power is a good way to trigger a state of harmony; and trust is shown to be the original driver and source that contribute to adapting actions, harmony and eventually to a win‐win negotiation outcome.

The investigations were limited in time and scope and consequently not conclusive.

This research may provide practical implictions for people and organizations interested in conflict resolution who wish to: take a position that values trust; take indirect actions through relationship instead of direct actions through power; create and retain harmony between both sides in conflict and negotiation; and keep a balance between adjusting others and adjusting self, so to achieve win‐win negotiation outcomes.

This research may enhance the understanding of Taoist Yin‐Yang thinking by linking it with the Western nomology.

This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis.

Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families.

From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy.

The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.