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While prior studies have explored the relationship between visual appeal and purchasing decisions, the role of bookmarking has largely been underemphasized. This research aims to address this gap by focusing on the impact of bookmarking on consumer behavior, guided by the cognitive load theory and dual-system theory.

The authors executed a controlled experiment and analyzed the results using a two-stage regression method that linked visual appeal, bookmarking and purchase intent. Further empirical analysis was conducted to authenticate the authors' proposed model, utilizing real-world mobile commerce data from a clothing company.

This study's findings suggest that visual appeal influences purchase intent primarily through the full mediation of bookmarking, rather than exerting a direct influence. Furthermore, an increase in colorfulness corresponds positively with visual appeal, while visual complexity exhibits an inverted U-shaped relationship with it.

This study provides novel insights into the choice-set formation process through the theoretical lens of dual-system theory. Additionally, the authors employed an image processing technique to quantify a product's visual appeal as depicted in a photograph. This study also incorporates a comprehensive econometric analysis to connect the objective aspects of visual appeal with subjective responses.

This study examined how people assess health information from AI and improve their diagnostic ability to identify health misinformation. The proposed model was designed to test a cognitive heuristic theory in misinformation discernment.

We proposed the heuristic-systematic model to assess health misinformation processing in the algorithmic context. Using the Analysis of Moment Structure (AMOS) 26 software, we tested fairness/transparency/accountability (FAccT) as constructs that influence the heuristic evaluation and systematic discernment of misinformation by users. To test moderating and mediating effects, PROCESS Macro Model 4 was used.

The effect of AI-generated misinformation on people’s perceptions of the veracity of health information may differ according to whether they process misinformation heuristically or systematically. Heuristic processing is significantly associated with the diagnosticity of misinformation. There is a greater chance that misinformation will be correctly diagnosed and checked, if misinformation aligns with users’ heuristics or is validated by the diagnosticity they perceive.

When exposed to misinformation through algorithmic recommendations, users’ perceived diagnosticity of misinformation can be predicted accurately from their understanding of normative values. This perceived diagnosticity would then positively influence the accuracy and credibility of the misinformation.

Perceived diagnosticity exerts a key role in fostering misinformation literacy, implying that improving people’s perceptions of misinformation and AI features is an efficient way to change their misinformation behavior.

Although there is broad agreement on the need to control and combat health misinformation, the magnitude of this problem remains unknown. It is essential to understand both users’ cognitive processes when it comes to identifying health misinformation and the diffusion mechanism from which such misinformation is framed and subsequently spread.

The mechanisms through which users process and spread misinformation have remained open-ended questions. This study provides theoretical insights and relevant recommendations that can make users and firms/institutions alike more resilient in protecting themselves from the detrimental impact of misinformation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0167

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

Individual differences cause many differences in human behaviour, and the first source of these differences is personality. In various organisations, employees are encouraged to manage conflict through conflict management styles. The way people think can be an essential factor in their ability to conflict management. Difficult employees are individuals who constantly use problematic communication styles to express their feelings and thoughts to direct the behaviour of others. This empirical study aims to investigate the effect of thinking styles on individuals’ conflict management in dealing with difficult personalities.

To achieve the research purpose, a gamified situation was designed, and a survey was performed in laboratory settings and on an online platform. At first, participants’ reactions were measured in the simulated conflict management situation dealing with difficult personalities; subsequently, the dominant thinking style of participants was measured by the rational-experiential inventory (REI) and the cognitive reflection test. At the end, participants answered a series of demographic questions.

The collected data were then analysed by regression analysis. Based on the findings of this study, the rational thinking measured by the REI40 has a significant and positive effect on the performance of individuals in conflict management with difficult personalities in an organisational context; in other words, rational thinking leads to better performance in conflict management than experiential thinking.

The value of this article lies in the direct study of the impact of thinking styles on conflict management, which was done by focusing on difficult organisational personalities. Also, using gamification in research design is another research initiative.

By analyzing tourist choices in Side and Alanya, well-known destinations for tourists in Türkiye’s thriving urban tourism sector, this study aims to fill a crucial vacuum in the body of knowledge about urban tourism. The study examines the changing dynamics of consumer preferences for advertisements and closely examines the underlying factors that influence these preferences, both pre and post-influential COVID-19 period.

This study clarifies the complex interplay between tourism marketing and prospective tourists’ decision-making processes through a thorough examination. This research greatly improves our understanding of urban tourism marketing strategies by examining the varying effects of advertising channels and comparing the persuasive power of emotional versus numerical advertising messages.

This study’s findings significantly advance our understanding of urban tourism. Examining how visitors react to advertisements in the various urban environments of Side and Alanya offers insightful information on how marketing strategies and visitor preferences correlate. This research also reveals the subtleties of efficient communication techniques, providing a practical basis for improving urban tourism experiences.

Being the first study of its sort, to the best of the authors’ knowledge, this research’s originality is supported by its insights into how advertising, consumer preferences and the urban tourism environment interact. The significant contribution to knowledge highlights the implications for those involved in urban tourism and provides practical advice for improving advertising tactics in the post-COVID-19 age.

This study aims to develop a database of existing academic information in house purchase decision (HPD) using systematic literature review (SLR), to facilitate worldwide advancement of research under HPD domain.

This research examined papers from two reputable databases – Scopus and Google Scholar – from 1992 to 2022 using a scoping review technique (Arksey and O’Malley, 2005) and a theme analysis method. Out of 374, 181 articles fit the inclusion parameters and were evaluated using the theme analysis approach.

Data from 181 articles was evaluated thematically to create a thematic map of HPD research. Five main themes and their sub-themes were identified: consumer behaviour, housing attributes, factors influencing purchasing decisions, investment analysis and demographics, which proved essential in understanding HPD and customer preferences for house purchase.

Data from 181 articles were evaluated thematically to create a thematic map of HPD research. This SLR intends to provide useful new insights on consumer concerns about home purchases in the rapidly developing residential real estate market and the issues that marketers, housing sector stakeholders, real estate industry and existing and future researchers should prioritize.

This research is unique such that it is the only 30-year-long SLR on the subject matter of HPD. This paper makes a significant contribution to residential real estate domain signifying the present state of research in HPD.

Researchers continue to address the concept of self-disclosure because it is foundational for helping social networking sites (SNS) function and thrive. Nevertheless, the authors' literature review indicates that uncertainty remains around the underlying mechanisms and factors involved in the self-disclosure process. The purpose of this research is to better understand the self-disclosure process from the lens of dual-process theory (DPT). The authors consider both the controlled factors (i.e. self-presentation and reciprocity) and an automatic factor (i.e. social influence to use an SNS) involved in self-disclosure and broaden The authors proposed a model to include the interactive facets of enjoyment.

The proposed model was empirically validated by conducting a survey among users of WeChat Moments in China.

As hypothesized, this research confirms that enjoyment and automatic processing (i.e. social influence to use an SNS) are complementary in the SNS self-disclosure process and enjoyment negatively moderates the positive relationship between controlled factor (i.e. self-presentation) and self-disclosure.

Theoretically, this study offers a new perspective on explaining SNS self-disclosure by adopting DPT. Specifically, this study contributes to the extant SNS research by applying DPT to examine how the controlled factors and the automatic factor shape self-disclosure processes and how enjoyment influences vary across these processes – enriching knowledge about SNS self-disclosure behaviors. Practically, the authors provide important design guidelines to practitioners concerning devising mechanisms to foster more automatic-enjoyable value-added functions to improve SNS users' participation and engagement.

This study is based on the heuristic-systematic model (HSM) to dynamically examine the effect of review variance on sales and the boundary conditions that mitigate this effect.

Based on the theoretical domain of HSM, a conceptual model is proposed that analyzes the nonlinear relationship between review variance and sales and the interaction and motivation factors that moderate these relationships. Review data from websites targeting the film industry in the USA and South Korea (Korea) were collected to empirically analyze the authors' hypothesis, and panel regression analysis was used for confirmation.

Moderated by interactive and motivational factors, review variance exhibits an inverse-U-shaped relationship with review variance. Specifically, as an interaction factor, review valence and owned social media (OSM) resulted in positive interaction effects, and as a motivation factor, the number of alternatives exhibited a positive interaction effect with review variance. The effect of review variance was less pronounced in the USA than in Korea.

The study outcomes reveal a nonlinear relationship between review variance and sales, thus supporting the contradictory findings of previous studies. This study contributes to the literature by using the HSM as a theoretical framework to verify various HSM mechanisms using online review data. This exploratory study also contributes to the international marketing literature by showing that the effects of review variance vary across cultures.

The purpose of this paper is to explore the tourists' perception of smart tourism with the application of virtual reality and design a framework of smart tourism with elements of VR for Indian Tourism especially in the periods of the pandemic COVID-19. The ever-evolving and unprecedented COVID 19 situation had posed extreme challenges for the travel and tourism industry. In such conditions, it is becoming increasingly necessary to rely on digital technologies, ICT and smart tourism. ICT has served as a catalyst for innovations in tourism.

This study investigates the impact of smart tourism and virtual reality technology on the perception of tourists towards travelling decisions during and post COVID-19 scenario. The respondents involved in the study were tourists travelling in India, the tourists come from different parts of India. A structured questionnaire has been administered to collect data from 224 travellers across India. The questionnaire consisted 22 constructs. The constructs in this section were measured using a five-point Likert scale ranging. In the first step, the first order Confirmatory Factor Analysis (CFA) is carried out, by using the software IBM AMOS-20. The initial model is generated ix constructs, and outcomes are used to analyse the model's goodness of fit and construct validity. In the second step, Structural Equation Modelling (SEM) is carried out to do the path analysis of the proposed model. The effect of relationships amongst the theoretical constructs is also analysed using SEM.

The findings imply that the application of smart tourism along with virtual reality forms a positive perception of tourists and provides a sustainable platform for tourism organizations in Indian tourism. Virtual reality-based tourism has emerged as alternate for the tourism industry during the times of Covid, which in long run can be seen as a substitute to traditional tourism. The increasing use of blue ocean concepts, to delivery high-value experience at low cost has complimented the tourism industry. The researchers have made a modest attempt by proposing a blended model of smart tourism with virtual reality as a blue ocean strategy and which would ultimately facilitate the sustainability of the Industry by creating multi-dimensional values of experience for tourists in India.

The researchers have made a modest attempt by proposing a blended model of smart tourism with virtual reality as a blue ocean strategy, which would ultimately facilitate the industry's sustainability by creating multi-dimensional values of experience for tourists in India.

This qualitative study designs a smart tourism system with the use of the recent advances in ICT and Virtual Reality (VR), as a bridging solution and the saviour of the tourism sector in India during COVID 19. The integration of ICT into the travel experience has resulted in the social phenomena of smart tourism. This has led to a rise in use of smart tourism tools among tourism service providers.

With the proliferation of ideas submitted by users in firm-built online user innovation communities, community managers are faced with the problem of user idea overload. The purpose of this paper is to explore the influencing factors on the idea adoption to identify high quality ideas, and then propose a method to quickly filter high value ideas.

The authors collected more than 110,000 data submitted by Xiaomi community users and analyzed the factors affecting idea adoption using a multinomial logistic regression model. In addition, the authors also used BP neural network to predict the idea adoption process.

The empirical results show that idea semantics, number of likes, number of comments, number of related posts, the existence of pictures and self-presentation have positive impact on idea adoption, while idea length and idea timeliness had negative impact on idea adoption. In addition, this paper calculates the idea evaluation value through the idea adoption process predicted by neural network and the mean value of idea term frequency inverse document frequency (TF-IDF).

This empirical study expands the theoretical perspective of idea adoption research by using dual-process theory and enriches the research methods in the field of idea adoption research through the multinomial logistic regression method. Based on our findings, firms can quickly identify valuable ideas and effectively alleviate the information overload problem of online user innovation communities.