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Online retailers widely use self-service parcel delivery as a solution to the last-mile logistics problems. The purpose of this paper is to investigate the factors that affect the consumer’s intention to use self-service parcel delivery service.

The authors integrate prior research and propose a comprehensive three-factor model. The study combines individual and situational factors and proposes a socialized factor.

This study found that location convenience, optimism, innovation, and the need for human interaction positively affect the consumer’s intention to avail of the self-service parcel delivery service. It also identifies that socialized factor positively influences the consumer’s intention to use self-service parcel delivery services.

The test results show that the explanatory power of the individual factors of the model is better than that of the situational factors. However, this does not imply that the situational factors cannot explain the consumer behavior well. Future studies should employ additional situational factors to explain the consumer behavior.

This study offers valuable theoretical and managerial implications. Delivery service providers should concentrate on their marketing force and customize their services for consumer groups who have specific individual characteristics, such as optimism and innovation.

Strengthening service interactions in the social factor and choosing optimal locations for self-service pickup machines are also essential for the expansion of the users’ population and enhancement of service experience.

The authors combined situational and individual factors, proposed a socialized factor, and presented the three-factor model of the consumer’s intention to use self-service parcel delivery service.

This study adopts self-determination theory and stimulus-organism-response framework to develop a model that explores the motivations of such donors by considering their self-determination needs and extrinsic and intrinsic motivations.

Based on online survey data collected from 436 crowdfunding donors in China, this study follows a structural equation modeling analysis to test hypotheses.

The results indicate that perceived ease of use, perceived self-efficacy and social connection have positive effects on the donation intentions of backers through a combination of extrinsic and intrinsic motivations.

The findings shed light on various extrinsic and intrinsic motivations advancing knowledge of individual fund motivation in donation-based crowdfunding and provide guidelines for the development of donation-based crowdfunding theory and practice.

This study examines the determinants of medical crowdfunding performance. Drawing on signaling theory, the authors investigate how funding-related signals (funding goal and duration), story-related signals (text length, text sentiment, and use of first-person pronouns), and donor-related signals (donor identity disclosure) affect medical crowdfunding performance.

This study analyzed the data of 754 medical crowdfunding projects collected from the Qingsongchou platform in China to test the proposed model.

The empirical findings reveal that both funding goal and funding duration exhibit a U-shaped relationship with crowdfunding performance. Additionally, the authors find evidence that story text length and donor identity disclosure are positively related to crowdfunding performance, whereas the use of first-person pronouns is negatively related to crowdfunding performance.

This study extends the understanding of the determinants of medical crowdfunding performance through the signaling theory. Specifically, this study provides new insights into the roles of funding goal and funding duration in predicting medical crowdfunding performance and identifies several new predictors of crowdfunding performance, including the use of first-person pronouns in project story text and donor identity disclosure.

Drawing on the technology-organization-environment (TOE) framework, this study aims to investigate determinants of performance of artificial intelligence (AI) adoption in hospitality industry during COVID-19 and identifies the relative importance of each determinant.

A two-stage approach that integrates partial least squares structural equation modeling (PLS-SEM) with artificial neural network (ANN) is used to analyze survey data from 290 managers in the hospitality industry.

The empirical results reveal that perceived AI risk, management support, innovativeness, competitive pressure and regulatory support significantly influence the performance of AI adoption. Additionally, the ANN results show that competitive pressure and management support are two of the strongest determinants.

This research offers guidelines for hospitality managers to enhance the performance of AI adoption and presents policy-making insights to promote and support organizations to benefit from the adoption of AI technology.

This study conceptualizes the performance of AI adoption from both process and firm levels and examines its determinants based on the TOE framework. By adopting an innovative approach combining PLS-SEM and ANN, the authors not only identify the essential performance determinants of AI adoption but also determine their relative importance.

This study aims to explore how specific cues with new manifestations (i.e. herding message and price discount information) and customer cognitive style influence attention allocation and purchase intention.

To empirically validate the research hypotheses, an eye-tracking experiment with a 2 × 2 × 2 mixed design was conducted on a sample of 44 participants recruited from a university in China. Repeated measures analysis of variance was employed for data analysis.

The results show that herding message and price discount information play different roles in viewers' attention and have an interactive effect on attention. Moreover, individual cognitive styles moderate the impact of herding message on attention allocation. Still, two cues positively affect customer purchase intention.

This study guides future research by applying cue utilization theory to investigate the effects of two cues in live streaming. Findings offer practical implications for how live streaming cues affect viewers' attention allocation and purchase intention.

As retailers have increasingly embraced an omnichannel retailing strategy, explaining and predicting the helpfulness of online review should consider both online-based and offline-based reviews. The paper aims to discuss this issue.

Based on the signaling theory, this study intends to examine the impacts of review-related and reviewer-related signals on review helpfulness in the context of omnichannel retailing. The proposed research model and corresponding hypotheses were tested by using negative binomial regression.

The results shown that both review-related (review rating and review sentiment strength) and reviewer-related (reviewer real name and reviewer expertise) signals positively affect review helpfulness. Contrary to the authors’ expectations, review length negatively affects review helpfulness. Specifically, when the review submitted from an omnichannel retailer’s offline channel, the positive impacts of reviewer real name on review helpfulness will be stronger, and the positive impacts of reviewer expertise on review helpfulness will be weaker.

Unlike many previous studies tend to explore the antecedents of review helpfulness in a single-channel setting, the study examined the factors that affect review helpfulness in an omnichannel retailing context.