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Recent literature on customer engagement has introduced the concept of “actor engagement,” which serves as the foundation for this study. The study aims to investigate the formation of engagement and engagement's impact on the performance of sharing economy platforms in an international context.

The study analyses unstructured data from 145,434 service providers and 1,703,266 customers on Airbnb across seven countries (USA, Canada, United Kingdom, Australia, South Africa, China and Singapore). Machine learning techniques are used to measure actor engagement, and the research model is tested using structural equation modelling (SEM).

The findings suggest that actor engagement, encompassing the reciprocal relationship between customer engagement and service provider engagement, has a significant impact on platform performance. The moderator analysis highlights the role of cultural differences in the relationship between customer engagement and service provider engagement and between actor engagement and platform performance. Specifically, the study reveals that actor engagement exhibits a more pronounced impact on platform performance in Western countries (such as the USA, Australia and the UK), compared to Eastern countries (such as China and Singapore).

The analysis of the conceptual model is based on the utilisation of behavioural data obtained from the Airbnb website. Due to the nature of the available data, proxies are employed as measures for variables such as platform performance.

This research is amongst the first to provide empirical evidence for actor engagement formation and the function's role in platform performance in the sharing economy. The global nature of Airbnb as a platform facilitates the investigation of country-level factors, specifically cultural values, across seven diverse countries and highlight differences from business to customer (B2C) business models.

The study aims to investigate customers' emotional and behavioral responses to price promotion in omnichannel retailing through the integration of the expectancy-disconfirmation theory, feelings-as-information-theory and regret regulation theory.

An online survey was designed in Qualtrics and distributed by an online survey to collect data from 786 (main study) and 150 (a follow-up study) customers from the USA. The participants were randomly assigned to different scenarios related to the need to purchase a toothbrush, laptop or health supplement. After the first purchase, the participants received a discount on the same product that has just been purchased. The discount can be used at an online store or a physical store. The three levels of price promotion after the purchase were 10% (low), 25% (moderate) and 50% (high).

The study found that consumers are likely to feel more surprised and less discontented when being offered a higher discount. The emotions further significantly impact their anticipated regret. Further, different discount levels influence patronage intention and omnichannel usage via emotional responses and anticipated regret. These relationships are moderated by product involvement.

The study extends knowledge of price promotion and provides insights that can assist retailers in increasing the effectiveness of their sales promotion strategy. Addressing the lacuna in the current literature, which predominantly focuses on the cost and benefits analysis of sales promotion, the study revealed that cross-channel price promotion results in consumers' sophisticated emotional responses.

The purpose of this research is to investigate the negative impact of social media influencers (SMIs) (human vs virtual) on customer well-being. Additionally, it aims to explore how the fear of missing out (FOMO) mediates and regulatory focus moderates this relationship from the social comparison theory lens.

In the first study, text mining and machine learning were employed to measure the level of followers' well-being in response to 40 SMIs (human vs virtual) posts on Instagram. In the second study, a randomized between-subjects experiment was conducted with three groups (human vs virtual vs control) and a sample size of 412 participants to confirm the results of the first study and investigate how FOMO mediates and regulatory focus moderates the relationship between SMI beauty product endorsement and consumer well-being.

The findings from text mining indicate that SMIs have a greater impact on consumers well-being, which is higher for virtual than human influencer. Additionally, the result of the experimental study shows the mediating role of FOMO in their relationship between SMIs and well-being. The moderator analysis reveals that there is a moderating effect of regulatory focus in the model.

The findings inform marketing managers about the differences between virtual than human influencer in their impact on customer well-being in endorsing beauty product, especially among the younger generation.

This paper is among the first research studies that examine the dark side of SMIs, which diminishes their follower's well-being through social comparison theory lenses.

This paper aims to present a comprehensive review of the laser engineered net shaping (LENS) process in an attempt to provide the reader with a deep understanding of the controllable and fixed build parameters of metallic parts. The authors discuss the effect and interplay between process parameters, including: laser power, scan speed and powder feed rate. Further, the authors show the interplay between process parameters is pivotal in achieving the desired microstructure, macrostructure, geometrical accuracy and mechanical properties.

In this manuscript, the authors review current research examining the process inputs and their influences on the final product when manufacturing with the LENS process. The authors also discuss how these parameters relate to important build aspects such as melt-pool dimensions, the volume of porosity and geometry accuracy.

The authors conclude that studies have greatly enriched the understanding of the LENS build process, however, much studies remains to be done. Importantly, the authors reveal that to date there are a number of detailed theoretical models that predict the end properties of deposition, however, much more study is necessary to allow for reasonable prediction of the build process for standard industrial parts, based on the synchronistic behavior of the input parameters.

This paper intends to raise questions about the possible research areas that could potentially promote the effectiveness of this LENS technology.