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While remanufactured products represent an increasingly researched phenomenon in the literature, not much is known about consumers' understanding and acceptance of such products. This study explores this issue in the context of the theory of perceived risk (TPR), investigating return policy leniency and distribution channel choice as potential factors to foster remanufactured products' sales.

This research utilizes an experimental design composed of a pre-test and a scenario-based main experiment to explore how return policy leniency might mitigate consumers' perceived risk and how their related purchase intention differs across two types of retail distribution channel structures (i.e. brick-and-mortar vs. online).

The investigation into the efficacy of return policy leniency within two retail distribution channel settings (i.e. brick-and-mortar vs. online) illustrates that providing a lenient return policy is an effective “cue” in increasing consumer purchase intention for remanufactured products. While prior literature has established that consumers value return policy leniency for new products, the authors provide empirical evidence that this preference also applies to remanufactured products. Notably, that return policy preference holds true in both channel settings (i.e. brick-and-mortar vs. online) under consideration. Additionally, and contrary to the authors’ predictions, consumers perceived remanufactured products sold via both channel settings as equally risky, thus highlighting that both are appropriate distribution channels for remanufactured products. Finally, while research on new products provides some initial guidance on consumer perceptions of quality and risk, the study provides empirical evidence into the difference of perceived risk with regard to new versus remanufactured products.

By employing the TPR, this research explored the role played by two supply chain management related factors (returns policy and channel structure) in reducing consumer's perceived risk and increasing purchase intention. In doing so, this study answers the call for more consumer-based supply chain management research in a controlled experimental research setting.

This study aims to develop a comprehensive adoption model that combines constructs from various theories and tests these theories against each other. The study combines a technology acceptance model, innovation diffusion theory and risk theory. It develops this model in a smart home applications context.

The study is based on an online survey consisting of 409 participants, and the data are analyzed using structural equation modeling.

Each theory provides unique insights into technology acceptance and numerous constructs are interrelated. Predictors from innovation diffusion and risk theory often display indirect effects through technology acceptance variables. The study identifies risk perception as a major inhibitor of use intention, mediated through perceived usefulness. Results reveal that the most important determinants of use intention are compatibility and usefulness of the application.

Studies which do not examine different theories together may not be able to detect the indirect effects of some predictors and could falsely conclude that these predictors do no matter. The findings emphasize the crucial role of compatibility, perceived usefulness and various risk facets associated with smart homes.

This study broadens the understanding about the necessity of combining acceptance and adoption drivers from several theories to better understand the usage of complex technological systems such as smart home applications.

This paper aims to determine the factors that influence the consumer’s online shopping intention in the current pandemic context (COVID-19). For this purpose, a conceptual model has been developed by introducing the constructs “attitude,” “perceived utility,” “intention” as well as the variable “perceived risk of contagion.”

After collecting data from the questionnaire diffused in Moroccan e-commerce websites, this study used various statistical analyses with the multiple regression model on the SPSS statistical software to confirm or refute the research hypotheses.

The results indicate that attitude and perceived utility positively affect online shopping intention. However, the variable “perceived risk of contagion” has a weak effect on such intention, which can be explained by the period in which the survey was started (a few months after the confinement).

The scientific contribution of this study lies in the insertion of a new factor that will be called “perceived risk of contagion” in the research model. This factor has been inspired by the perceived risk theory of Bauer (1960). Furthermore, all studies dealing with this topic have been carried out in developed countries, such as France, Great Britain, Germany and the USA. For this reason, the researcher believe that it is more appropriate to study the intention to buy online during the COVID-19 pandemic in one of the developing countries, such as Morocco. This is based on the fact that to develop theories, it is necessary to examine a given problem in several countries. The context plays a determining role in such situations.

This paper aims to examine travelers’ behavioral intention of traveling in the period of coronavirus by using the theory of planned behavior. The framework incorporates attitude, subjective norms, perceived behavioral control and a very crucial construct, i.e. perceived risk, as per the current critical scenario of COVID-19.

Data was collected using a survey instrument on the internet by posting the questionnaire link over social network web pages of online traveling websites. The data was analyzed using structural equations modeling with AMOS 22.0 and SPSS software and the proposed hypotheses were statistically tested. The sample under consideration constitutes 417 responses.

Empirical findings suggest that attitude, perceived behavioral control and perceived risk are significant for predicting behavioral intention while subjective norms do not. Then, these variables explained about 35% of the variance in the behavioral intention of traveling in the period of coronavirus.

This study can benefit travelers, the tourism and hospitality industry, governments, the aviation industry and other relevant organizations as this paper offers the latest updates and essential information regarding traveler’s intention of traveling in the period of coronavirus. The study mainly focuses on India, so the generalizations of results to other countries are unwanted.

The primary value of this paper is that it tested the theory of planned behavior by incorporating perceived risk in the context of COVID-19. To the best of the authors’ knowledge, in the Indian context, there is no study, which has tested the TPB by adding perceived risk in explaining the Indian citizens’ behavioral intention of traveling in the period of Coronavirus.

This paper aims to examine the impact of online reviews on behavioral intentions via perceived risk. Perceived risk is both analytical and emotional. Stimulus–organism–response (S–O–R) framework guided this study to explore the interaction between online reviews, perceived risk and behavioral intentions.

The conceptual model proposed in this research has been validated using confirmatory factor analysis (CFA) and structural equation modeling to assess the measurement model and the validity of the scale, based on primary responses collected from 473 travelers.

Findings of this study suggest the role of online consumer reviews in reducing the perceived risk associated with experience dominant services like tourism. Process model test proves the mediating role of perceived risk between online reviews and behavioral intentions. Results indicate the significance of online review in lowering the perceived risk leading to positive behavioral intentions.

Destination marketing organizations (DMOs) should understand the role of online reviews in effectively reducing risk and uncertainty, thereby influencing behavioral intentions.

This paper is unique in attempting to empirically examine the mediating role of perceived risk between online reviews and behavioral intentions. The study is a forerunner in using S–O–R framework to test the interaction between online review, perceived risk and behavioral intention.

The purpose of this study is to develop a theoretical model for consumer behavioral intention by integrating the technology acceptance model (TAM) and the theory of perceived risk, which is tested on the intended use of credit cards in Vietnam.

The data were collected from 485 bank customers through a nationwide online survey. An exploratory and confirmatory factor analyzes were performed to validate the factor structure of the measurement items while structural equation modeling was used to validate the proposed model and testing the hypotheses.

The results of structural equation modeling reveal that perceived risk, perceived usefulness, social influence and perceived ease of use were significant determinants of consumer intention to use a credit card. Of them, only perceived risk discouraged the intended use of a credit card, which was synthesized from psychological, financial, performance, privacy, time, social and security risk.

This study measured the first-order risk dimensions based on the payment function of the credit card only; these measurements missed potential losses relevant to credit function of credit cards.

This study can be beneficial to banks enacting policies to attract more consumers and to help decide how to allocate resources to retain and expand their customer base.

The study adds value to the literature on consumer behavior by confirming the impact of second-order perceived risk on the intended use of credit cards, which most previous studies have not demonstrated. The research also provides an empirical evidence to the academic research platform on e-banking services in Vietnam, especially related to the credit card industry.

The aim of this study is to examine the behavioral intention (BI) to adopt the Coinbase Wallet by Spanish users.

A survey was administered to individuals residing in Spain between March and April 2021. There were 301 questionnaires analyzed. This research applies a new predictive model based on technology acceptance model (TAM) 2, the unified theory of acceptance and use of technology (UTAUT) model, the theory of perceived risk and the commitment trust theory. A mixed partial least squares structural equation modeling (PLS-SEM)/fuzzy-set qualitative comparative analysis (fsQCA) methodology was employed for the modeling and data analysis.

The results showed that all the variables proposed have a direct and positive influence on the intention to use a Coinbase Wallet. The findings present clear directions for traders, investors and academics focused on improving their understanding of the characteristics of these markets.

First, this study addresses important concerns relating to the adoption of crypto-wallets during the global pandemic. Second, this research contributes to the existing literature by adding electronic word of mouth (e-WOM), trust, web quality and perceived risk as new drivers of the intention to use the Coinbase Wallet, providing unique and innovative insights. Finally, the study offers a solid methodological contribution by integrating linear (PLS) and nonlinear (fsQCA) techniques, showing that both methodologies provide a better understanding of the problem and a more detailed awareness of the patterns of antecedent factors.

This study aims to investigate tourists’ attitudes and intentions regarding the usage of Chat Generative Pre-trained Transformer (ChatGPT) for accessing tourism information. Furthermore, by integrating the perceived risks associated with ChatGPT and the theory of planned behavior (TPB), this research examines the impact of three types of perceived risks, such as privacy risk, accuracy risk and overreliance risk, on tourists’ behavioral intention.

Data were gathered for this study by using two online survey platforms, thus resulting in a sample of 536 respondents. The online survey questionnaire assessed tourists’ perceived risks, attitude, subjective norm, perceived behavioral control, behavioral intention and demographic information related to their usage of ChatGPT.

The structural equation modeling analysis revealed that tourists express concerns about the associated risks of using ChatGPT to search for tourism information, specifically privacy risk, accuracy risk and overreliance risk. It was found that perceived risks significantly influence tourists’ attitude and intention toward the usage of ChatGPT, which is consistent with the hypotheses proposed in previous literature regarding tourists’ perceived risks of ChatGPT.

This work is a preliminary empirical study that assesses tourists’ behavioral intention toward the use of ChatGPT in the field of tourism. Previous research has remained at the hypothetical level, speculating about the impact of ChatGPT on the tourism industry. This study investigates the behavioral intention of tourists who have used ChatGPT to search for travel information. Furthermore, this study provides evidence based on the outcome of this research and offers theoretical foundations for the sustainable development of generative AI in the tourism domain. This study has limitations in that it primarily focused on exploring the risks associated with ChatGPT and did not extensively investigate its range of benefits.

First, to address privacy concerns that pose significant challenges for chatbots various measures, such as data encryption, secure storage and obtaining user consent, are crucial. Second, despite concerns and uncertainties, the introduction of ChatGPT holds promising prospects for the tourism industry. By offering personalized recommendations and enhancing operational efficiency, ChatGPT has the potential to revolutionize travel experiences. Finally, recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises.

Recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises. As their interest in adopting ChatGPT grows, increased investments and resources will be dedicated to developing and implementing ChatGPT solutions. This enhancement may involve creating customized ChatGPT solutions and actively engaging in training and development programs to empower employees in effectively using ChatGPT’s capabilities. Such initiatives can contribute to improved customer service and overall operations within the tourism industry.

This study integrates TPB with perceived risks in ChatGPT, thus providing empirical evidence. It highlights the importance of considering perceived risks in tourists’ intentions and contributes to the sustainable development of generative AI in tourism. As such, it provides valuable insights for practitioners and policymakers.

本研究旨在调查游客对使用ChatGPT获取旅游信息的态度和意向。此外, 通过将与ChatGPT相关的感知风险与计划行为理论（TPB）相结合, 本研究探讨了三种感知风险（隐私风险、准确性风险和过度依赖风险）对游客行为意向的影响。

本研究通过两个在线调查平台收集了536名受访者的数据。在线调查问卷评估了游客对ChatGPT使用的感知风险、态度、主观规范、感知行为控制、行为意向以及与其使用ChatGPT相关的人口统计信息。

结构方程建模分析显示, 游客对使用ChatGPT搜索旅游信息的相关风险表示关切, 特别是隐私风险、准确性风险和过度依赖风险。发现感知风险显著影响游客对使用ChatGPT的态度和意向, 与先前有关游客对ChatGPT感知风险的文献中提出的假设一致。

本研究将TPB与ChatGPT中的感知风险相结合, 提供了实证证据。它强调了在考虑游客意向时考虑感知风险的重要性, 并为旅游中生成AI的可持续发展提供了贡献。因此, 它为从业者和政策制定者提供了宝贵的见解。

This paper investigates the extent to which the COVID-19 pandemic has accelerated service innovation in the restaurant sector. It explores the use of digital technologies (DT) as a safety-empowerment and resilient strategy in the food-service industry during the pandemic. It also investigates the impact of DT on customers' risk perception (CRP) and customers' intention to go to restaurants (CIR) in Italy.

Based on the theory of planned behaviour and perceived risk theory, this study investigates a sample of customers residing in Italy. Multiple regression and mediation analyses are conducted to test the research hypotheses, adapting the logic model developed. Using the bootstrapping technique, this study also explores whether the pandemic has moderated the relationship among several variables adapted from the literature. Robustness tests are also performed to corroborate the analysis.

The pandemic has accelerated the food-service industry's digital transformation, forcing restauranteurs to implement DT to survive. Findings show that DT support restauranteurs in implementing innovative services that reduce interactions and empower cleanliness among workers and customers, reducing CRP and preserving CIR. Thus, managing risk perception is helping the restaurant sector to recover.

Practical implications are presented for policymakers to catalyse the digital transformation in small- and medium-sized restaurants. The results may also be beneficial for entrepreneurs who can implement innovative service practices in order to reduce interaction and empower cleanliness levels. Moreover, academics can use these results to conduct similar research in other geographical contexts.

The present research represents the first study investigating the relationship between the use of digital technologies and the intentions of customers to go out for dinner during the ongoing pandemic in Italy.

The authors aim to study a conceptual model based on behavioural theories (UTAUT-3 model) to evaluate the adoption, usage and recommendation for neobanking services in India.

The authors propose this model based on the UTAUT-3 integrated with perceived risk constructs. Hypotheses were developed to determine the relationships and empirically validated using the PLSs-SEM method. Using the survey method, 680 Delhi NCR respondents participated in the survey.

Empirical results suggested that behavioural intention (BI) to usage, adoption and recommendation affects neobanking adoption positively. The research observed that performance expectancy (PE), effort expectancy (EE), perceived privacy risk (PYR) and perceived performance risk (PPR) are the essential constructs influencing the adoption of neobanking services.

Limited by geographic and Covid-19 constraints, a cross-sectional study was conducted. It highlights the BI of neobanking users tested using the UTAUT-3 model during the Covid-19 period.

The study's outcome offers valuable insights into Indian Neobanking services that researchers have not studied earlier. These insights will help bank managers, risk professionals, IT Developers, regulators, financial intermediaries and Fintech companies planning to invest or develop similar neobanking services. Additionally, this research provides significant insight into how perceived risk determinants may impact adoption independently for the neobanking service.