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Although there is a rising demand for organic food restaurants (OFRs), limited studies have attempted at understanding the drivers of customers' intention to visit OFRs. There is also a need to examine customers' choice behaviour in the context of OFRs.

This study has assessed the effect of consumption values (functional, emotional, epistemic, conditional, quality and social) on the intention to visit OFRs by utilizing 1863 online customer reviews about different Indian OFRs (Study 1) and 205 survey-based responses of Indian customers (Study 2).

Findings show an overall positive sentiment towards OFRs. Results of Study 1 and Study 2 reveal that functional, quality, social and emotional values have a significant impact on customers' OFR visit intention. Interestingly, Study 2 found that epistemic values have an insignificant impact on customers' visit intention.

Study findings suggest that the OFR management need to provide a better ambience, and good quality organic food in OFRs. Additionally, managers of OFRs can train their staff to be well mannered, cooperative and sincere.

The study on OFRs is still in its nascent stage. The findings of this study will thus provide academicians and policy makers an idea of the consumption values that affect customers' intention to visit OFRs.

The widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios.

In this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores.

Overall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance.

This study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.

This paper aims to explore the consumers’ and service-providers’ perspectives on the factors influencing adoption of e-services in rural India. The purpose is to enable better diffusion of technology for societal development in this digital era.

Using qualitative-based multiple-participant interviews, this study explores the factors affecting e-service adoption from two different perspectives. While interviews were conducted in five villages with 14 respondents to find out the perspectives of the consumers, this study also explores the service-providers’ perspectives through interviews conducted among 11 managerial respondents.

Catering to personal needs, improving perceived usefulness, value-added options, data analytics for better understanding customers and improving service delivery of the e-service are the major factors identified by the service-providers. The study also concludes that convenience, compatibility, societal influence and availability of value-added addition of the e-service are decisive in e-service adoption from the perspectives of the consumers.

The first limitation of this research is that there can be common method bias. Second, there were overlapping themes.

This study can help researchers working on the adoption of e-services in under-developed/developing countries. The findings of this study may help industries to focus on the determinants while designing the e-services for improving their rate of adoption.

This study will help in better diffusion of e-services in rural areas, which in turn will help in societal development in this digital era.

The focus is on societal development through the adoption of e-services in rural areas. To the best of the knowledge of the researchers, no qualitative study has been performed to capture the perspectives of both the service-providers and the consumers on the adoption of e-services in India.

Social media channels provide an avenue for expressing views about different services/products. However, unlike merchandise/company websites (where users can post both reviews and ratings), it is not possible to understand user's ratings for a particular service-related comment on social media unless explicitly mentioned. Predicting ratings can be beneficial for service providers and prospective customers. Additionally, predicting ratings from a user-generated content can help in developing vast data sets for recommender systems utilizing recent data. The aim of this study is to predict ratings more accurately and enhance the performance of sentiment-based predictors by combining it with the emotional content of textual data.

This study had utilized a combination of sentiment and emotion scores to predict the ratings of Twitter posts (3,509 tweets) in three different contexts, namely, online food delivery (OFD) services, online travel agencies (OTAs) and online learning (e-learning). A total of 29,551 reviews were utilized for training and testing purposes.

Results of this study indicate accuracies of 58.34%, 57.84% and 100% in cases of e-learning, OTA and OFD services, respectively. The combination of sentiment and emotion scores showed an increase in accuracies of 19.41%, 27.83% and 40.20% in cases of e-learning, OFD and OTA services, respectively.

Understanding the ratings of social media comments can help both service providers as well as prospective customers who do not spend much time reading posts but want to understand the perspectives of others about a particular service/product. Additionally, predicting ratings of social media comments will help to build databases for recommender systems in different contexts.

The uniqueness of this study is in utilizing a combination of sentiment and emotion scores to predict the ratings of tweets related to different online services, namely, e-learning OFD and OTAs.

The current study tries to better understand the resistance toward food delivery applications (FDAs). This study has adapted the existing criteria to measure different consumer barriers toward FDAs. It also examined the relationships between various consumer barriers, intention to use FDAs and word-of-mouth (WOM).

This study utilized the innovation resistance theory (IRT) and a mixed-method approach comprised of qualitative essays submitted by 125 respondents and primary surveys (N = 366) of FDA users.

Tradition barrier (trust) shared a negative association with use intention, while image barrier (poor customer service) shared a negative association with WOM. The intention to use was positively associated with WOM. Additionally, the study results reveal that image barrier (poor customer experience) and value barrier (poor quality control) were, in fact, positively related to WOM. This study also discusses the managerial and theoretical implications of these findings and the scope for further research on FDAs.

FDAs have revolutionized the food delivery industry and made it more comfortable and convenient for the consumers. However, FDA service providers are facing challenges from both customers and restaurants. Although scholars investigated customer behavior toward FDAs, no prior study has focused on consumer barriers toward FDA usage.

The study investigated the relationship between future time perspective and financial well-being among Chinese working millennials and its serial mediators, such as financial goal clarity, subjective financial knowledge and responsible financial behaviour, to foster consumer resilience in the financial realm.

A total of 526 Chinese working millennials (Mage = 31.78) participated in the online survey in response to questions on demographic characteristics and items to measure the variables adopted in the research model. Covariance-based structural equation modelling (CB-SEM) and AMOS version 27 were used to test the research hypotheses.

The results revealed a positive correlation between future time perspective and financial well-being. Moreover, the results showed that financial goal clarity, subjective financial knowledge and responsible financial behaviour serially mediated the correlation between future time perspective and financial well-being.

The findings provide implications for companies and policymakers to refine their intervention programmes to boost young millennials' future time perspectives in reinforcing their financial knowledge and financial goal clarity which in turn fosters their responsible financial behaviour in contributing to financial well-being in boosting their overall consumer resilience. Future studies should deepen the way in which the studied factors are leveraged as a tool to improve individuals' resilience in the economic realm.

The findings of this study shed light on the underlying mechanisms that drive and promote the financial well-being of Chinese working millennials.

The purpose of this paper is to investigate the role of the “Big Five” personality traits (extraversion, openness, agreeableness, conscientiousness and neuroticism) on the adoption of augmented reality (AR), with a particular focus on the role AR may play in interactive marketing.

A quantitative-based approach was followed by a questionnaire survey, which was completed by 230 respondents comprising graduate and postgraduate students, using structural equation modelling.

While the trait of openness was positively associated with the perceived ease of use of AR, the usefulness of AR and subjective norms, the trait of neuroticism was negatively associated with the perceived ease of use of AR. Extraversion was positively associated with subjective norms. Perceived ease of use of AR, the usefulness of AR and subjective norms were positively associated with attitudes toward AR.

The data gathered will add a valuable contribution to the currently limited data available on empirical consumer behaviour research, particularly in relation to the adoption of AR for interactive marketing.

The findings of this study will benefit academics working on the adoption of technology in rapidly developing fields such as automation and artificial intelligence; the study also contributes to the emerging interdisciplinary domain of psychology, information systems, marketing and human behaviour.

The paper aims to explore and examine the factors that influence the post-consumption behavioral intentions of education consumers with the help of online reviews from a Massive Open Online Course (MOOC) platform in the knowledge payment context.

The paper adopted a novel mixed-method approach based on natural language processing (NLP) techniques. Variables were identified using topic modeling drawing upon 14,585 online reviews from a global commercial MOOC platform (Udemy.com). The relationships among identified factors, such as perceived quality dimensions, consumption emotions, and intention to recommend, were then tested from a cognition-affect-behavior (CAB) perspective using partial least squares structural equation modeling (PLS-SEM).

Results indicate that course content quality, instructor quality, and platform quality are strong predictors of consumers' emotions and intention to recommend. Interestingly, course content quality displays a positive effect on invoking negative emotions in the MOOC context. Additionally, positive emotions mediate the relationships between three perceived qualities and the intention to recommend.

Limited research has been conducted regarding MOOC consumers' post-consumption intentions in the knowledge payment context. Findings of this study address the limited literature on MOOC qualities and consumer post-consumption behaviors, which contribute to a comprehensive understanding of MOOC learners' experiences at a meso-level for future paid-MOOC creators.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2021-0482/