Amazon Go is a cashierless convenience store concept, which is seen as a disruption in the grocery retail segment. Although Amazon Go has the ability to disrupt the retail segment, there are speculations on how Amazon Go will be perceived by users. Existing studies have not utilized user-generated content to understand the factors that affect customer behaviour in case of Amazon Go. Additionally, in case of phygital retail, studies have not attempted at understanding the effect of emotions and gratifications on user behaviour. To address the gap of exploring user perspectives based on their experience, we have examined the impact of gratifications and emotions on the acceptance of phygital retail using user-generated-content. A mixed-method approach has been utilized using only user-generated content. Utilizing topic-modelling based content analysis and emotion analysis on 30 articles related to Amazon Go, we found themes like, convenience, technology, experience, personalization, enjoyment and emotions like, bad, good, annoyance, success. In the empirical analysis, we have utilized 522 reviews about Amazon Go from the cognition and emotion theory stance, and found that hedonic gratifications have a positive impact on challenge emotions. We also found a significant impact of emotions on customer's favourite behaviour.

The smartphone is a necessary communication and productivity tool. Unlike other neutral, utilitarian technologies, smartphones are also a social display of wealth, i.e., of conspicuous consumption. Behavior around necessity and conspicuous goods are normally separate research tracks, and we can find no studies for technology products that exhibit both traits. We therefore propose a four-factor model that explains conspicuous consumption of "necessary" smartphones: dependency, social influence, the need for social connection, and convenience, in the United States, Mexico, and the Philippines. This paper provides confirmatory support for extant literature of the four constructs; it demonstrates the viability of the survey instrument across countries; and it shows similar effects among country models. Our structural model results imply the borderless nature of smartphone conspicuous consumption.

While prior work has conducted individuals' password security behavior, there is a relatively neglect to examine individuals' affect and feelings of password fatigue in password change context. Therefore, this study explicated individuals' affective response to the feelings of password fatigue by drawing on several theoretical lens. Survey data collected from 267 users were used to test the model using partial least square analysis. This study found that feelings of password fatigue positively affected the negative password fatigue-induced affect, and also both the feelings of password fatigue and the negative password fatigue-induced affect were negatively related to attitude toward changing passwords, which in turn, leads to the intention to change passwords. Furthermore, this study found that shadow work recognition negatively moderated the relationship between attitude and behavioral intention. This study could offer a new theoretical perspective to understand an individual's security behavior and provide empirical evidences for practitioners in charge of IT security in organizations.

The study makes several contributions to not only the adoption literature by examining the influence of Emotional Intelligence (EI) and Big-Five traits on adoption of Augmented Reality (AR) but also given its utility in both industry and research, it contributes to the interesting inter-disciplinary domain of psychology, information systems, and human behaviour. A quantitative based approach using a sample of 275 respondents was undertaken. It is found that emotional intelligence influence both perceived ease-of-use and perceived usefulness. They in turn influence intention to use. Another important observation is that personality traits (openness and agreeableness) have a significant moderating effect on the relation between attitude and intention to use AR. This research will help academicians and executives working on the adoption of AR in various sectors ranging from retail industry to the education sector. The originality of this study is that it explores the impact of EI on the acceptance of AR and helps in extending the literature in interdisciplinary research.

The objective of this study is to provide a deeper understanding of the factors that lead to the development and adoption of AI-based chatbots. We analyze the structural relationship between the organizational (externalities), systematic (fit), and the consumer-related (psychological) factors and their role in the adoption of AI-based chatbots. Founded on the theories of task-technology fit and network externalities, we present a conceptual model overlooking common perception-based theories (e.g., Technology Acceptance Model). We collected 380 responses from Indian banking consumers to test the model using the PLS-SEM method. Interestingly, the findings present a positive impact of all factors on consumers' intention to adopt AI-based chatbots. However, the interplays between these factors provide a mixed perspective for literature. Apart from employing a combination of factors that have been used to study technology adoption, our study explores the importance of externalities and their relationship with fit factors, a unique outlook often overlooked by prior research. Moreover, we offer a clear understanding of latent variables such as trust, and the intricacies of their interplays in a novel context. Thereby, the study offers implications for literature and practice, followed by future research directions.

With the rapid advancement of social media analytics and artificial intelligence, many studies have used online customer reviews as an important source to measure service quality in many industries, including the hotel industry. However, these studies have failed to identify the relative importance of different dimensions of service quality and their role in customer satisfaction. To fill this research gap, this study aims to identify the effects of service quality on hotel customer satisfaction from the multidimensional perspectives using sentiment analysis with self-training on online reviews. Additionally, the moderating role of the brand of origin for each service quality dimension is also investigated. Drawing on the SERVQUAL model and brand of origin concept, this study develops 12 hypotheses and empirically tests them using 30,070 online customer hotel reviews collected from TripAdvisor.com. The results indicated that overall service quality and each dimension of SERVQUAL significantly influenced customer satisfaction of hotels. The results also confirmed the moderating effects of brand of origin on overall service quality. However, the moderating effects of brand of origin for the tangible, reliability, and empathy dimensions of service quality were significant, whereas the effects for responsiveness and assurance were not. This study sheds new light on service quality measurement by analyzing the multidimensional features of service quality and the role of brand of origin in the hotel service context.

Artificial Intelligence (AI) and the technologies powered by AI fuel the fourth industrial revolution. Being the primary adopter of such innovations, banking has recently started using the most common AI-based technology, i.e., conversational agents. Although research extensively focuses on this niche area and provides bibliometric understanding for such agents in other industries, a similar review with scientometric insights of the banking literature concerning AI conversational agents is absent till date. Furthermore, in the era following the pandemic, banks are faced with the imperative to provide solutions that align with the changing landscape of remote consumer behavior. As a result, banks are proactively integrating technology-driven solutions, such as automated agents, to effectively address the growing demand for remote customer support. Hence more research is needed to perfect such agents. In order to bridge these existing gaps, the present study undertook a comprehensive examination of two decades' worth of banking literature. A meticulous review was conducted, analyzing approximately 116 papers published from 2003 to 2023. The aim was to provide a scientometric overview of the topic, catering to the research needs of both academic and industrial professionals. Holistically, the study seeks to present a macro-view about the existing trends in AI based banking conversational agents' literature while focusing on quantity, qualitative and structural indicators that are effectively necessary to offer new directions for the AI-based banking solutions. Our study, therefore, presents insights surrounding the literature, using selected techniques related to performance analysis and science mapping.

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.

Effective brand management using images has been a challenge for the brand managers. The brand identity-brand image alignment on the social media is an important yet mostly-overlooked phenomenon. We proposed a scalable Google Cloud Vision-based approach for measuring the alignment between brand identity and brand image, and understanding the brand positions. We analyzed 3247 images of 13 leading Indian brands on Instagram. Images containing wordy announcements by the firms are in stark contrast with the relatively more emotive images by the users. It leads to a noticeable disconnect between the brand identity and brand image. Also, the private sector brands do not always outperform the public sector brands in branding efforts. By offering practical guidance on how to measure and reduce the misalignment, this study paved a feasible path towards better visual branding on Instagram.

Recently, Web 3.0 services such as virtual currency, block chain, and NFT are attracting public attention. Users who depended on platforms are moving away from being dependent on service providers and moving to Web 3.0 services. Representative services of Web 3.0 include P2E (Play to Earn) and M2E (Move to Earn). In the case of P2E, various studies have been conducted as it is widely covered in the press and media, but research on M2E is relatively lacking. This study attempts to identify the intention to use M2E by using the expanded technology acceptance model. External factors were selected based on M2E's own characteristics and personal characteristics, a research model was designed, and the proposed hypotheses were verified through factor analysis and goodness of model fit. As a result of the study, it was confirmed that profitability, innovativeness, and self-expression had a positive effect on perceived characteristics, and that perceived usefulness, perceived enjoyment, and social influence had a positive effect on intention to use. Through the research results, practical implications for efficient service operation that meets the needs of users are presented to M2E platform providers.

We propose a mobile coupon strategy designed to increase the effect of push notifications on redemption. The proposed strategy recommends that firms deliver mobile coupons with distant expiration dates and remind them through push notifications framed negatively once these expiration dates become imminent, rather than frequently sending coupons with near expiration dates. We test the effectiveness of the proposed strategy using data collected through a randomized field experiment. The findings indicate that push notifications enhance coupon redemption rates for coupons that are held longer by customers than those that are recently received. Additionally, we found that sending negatively framed push notification messages to remind customers of imminent coupon expiration dates further resulted in higher coupon redemption rates. The findings can be employed to offer useful guidance on how to effectively design mobile coupons for achieving higher redemption rates.

The rapid adoption of smartphones and tablets among employees has recently forced organizations to proactively embrace bring your own device (BYOD). The situation of COVID-19 makes the concept of BYOD even paramount. Allowing employees to bring their own mobile devices to the workplace has helped companies realize productivity gains and cost benefits. However, BYOD has also blurred the boundaries between work and personal life, thereby creating a stressful environment for employees (Doargajudbur and Hosanoo, 2023). This study explores the relationships between several BYOD attributes and investigates the effects of these attributes on work-to-life conflict. It contributes by introducing workplace connectivity after hours as a new dimension of BYOD attribute to influence work-to-life conflict. Based on boundary theory, a theoretical model is developed and tested with an online survey. The results reveal that flexibility and workplace connectivity after hours positively influence productivity, and flexibility helps to relieve instead of increase work overload. Meanwhile, a higher level of flexibility and productivity can help reduce work-to-life conflict, and a higher level of work overload induces a higher level of work-to-life conflict.