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Advances in autonomous technology have transformed customer experience. Most prior research has investigated the effect of augmented reality (AR) on purchase intention, yet few research has discussed the effect of semiautonomous AR in the context of service use. Semiautonomous AR recognizes content in the present reality, inserts and adjusts virtual content, supervises the users and enables them to feel in control of the virtual content overlaid in observed reality resulting in enriched user experience and thereby augmentation experience. This research demonstrates how perceived control of virtual content leads to higher perceived augmentation experiences among semiautonomous AR users than among non-AR users. In addition, this research examines the mediation effects of enriched user experience and perceived augmentation experience on user satisfaction and users’ willingness to continue using AR. Results also indicate that AR users perceive a higher augmentation experience than non-AR users. However, users’ willingness to continue using AR is not significantly different between AR and non-AR users.

This study derives six hypotheses and uses a preliminary study, a field study and a lab study to evaluate the hypotheses. A field study was conducted in a car dealership to test the hypotheses, and a lab experiment was conducted in a controlled setting to corroborate the results obtained in the field study and test the underlying causal effects.

Semiautonomous AR can constantly sense, plan and not necessarily always act over the virtual content to sustain the interaction with its users. Perceived control of virtual content enhances perceived augmentation experience, and its effect of perceived control of virtual content on perceived augmentation experience is higher among semiautonomous AR users than among non-AR users. Perceived control of virtual content is a key to enriched user experience, augmentation experience and thereby users’ attitude and behavior. In addition, results showed that enriched user experience mediates the effect of perceived control of virtual content on perceived augmentation. User satisfaction mediates the effect of perceived augmentation experience on users’ willingness to continue using AR. The theoretical and practical contributions are comprehensively discussed.

Some limitations of the studies are ascertained. First, a larger sample size might be required to achieve generalizability and a strong test of the applied theory. Second, new field studies can reflect customers’ real attitudes and behaviors so as to reveal realistic interactions between the device properties and the human will in solving actual problems. The user is interested in participating in the solution within the sensing-planning-acting process as depicted by this research. Third, new research to test AR’s capabilities in bounded and symbiotic conditions can illustrate the level of autonomy each type requires, providing additional insights into why supervised AR autonomy best reflects semiautonomous AR. The pioneering structural model offered in this study (perceived control of virtual content-perceived augmentation experience-users’ satisfaction-users’ willingness to continue using AR) should be tested with new samples in other industries, aside from including other variables that may enrich the model and increase its explanatory power. In addition, future research might use other AR devices such as smart glasses to explore the effects of AR on perceived control of virtual content, enriched user experience and perceived augmentation experience. Future studies can investigate the effect of auditory and visual augmentation on enriched user experience and perceived augmentation experience, and involve features of artificial intelligence (AI) to assist users in decision-making. Regarding context, this research showed that age and gender differences did not affect the results. Nonetheless, age and gender, and perhaps additional demographic characteristics, may concern future studies.

Some recommendations for technology developers are derived from this research. AR is revolutionizing service experience. As technologies are becoming autonomous, developers seek ways to design experiences to enhance consumers’ sense of control over their interaction with such systems. Companies cannot create customer experience (Becker and Jaakkola, 2020), yet they can leverage the level of autonomy in AR to sustain ongoing interaction with customers. It is vital to design an autonomous AR that focuses on users’ needs, desires and well-being (de Bellis and Johar, 2020) that drive novel experiences (Novak and Hoffman, 2019). This study recommends AR developers design autonomous features in AR that enable customers to interact with the virtual contents generated by AR and extend their own capabilities to perform a task and feel expanded. While designing a fully autonomous system may hinder users to feel in control of their choice (Schmitt, 2019), service companies can develop an AR system that sustains an interaction, involves the user in value co-creation and guides the user (Alimamy and Gnoth, 2022). AR can sustain an interaction with the users by continuously scanning the objects in the reality and providing sensory feedback such as product size recommendations (e.g. eyeglasses) that facilitate customers’ information processing (Poushneh, 2021b; Heller et al., 2019). To achieve this, developers may focus on technology qualities such as “image recognition,” a subset of AI. With image recognition, AR can effectively provide instruction as if the customer is in a real setting. The proper incorporation of image recognition in the design of AR while enabling users to interact with 3D virtual images sustains their interaction with AR and makes them feel in control of their interaction with AR. Service companies need to ensure users feel in control of their interaction and expand their capacities to engage in the service experience with AR to accomplish their desired tasks. AR’s capacities enable users to expand their abilities to fix their basic service problems without referring to or speaking to a service provider agent in a service context. Therefore, instead of taking their car back to the dealership, customers can use AR mobile applications or glasses provided by car manufacturers to learn and fix basic vehicle problems.

This research advances the marketing literature on how users feel in control of virtual content when they interact with a semiautonomous AR that subsequently influences enriched user experience, perceived augmentation experience, attitudes and behavior.

The television (TV) content ecosystem has shifted from traditional broadcasting systems to dedicated content producers and over-the-top (OTT) services. However, less empirical effort has been paid to the actual behaviors of the mobile users who watch TV content when explaining the impact of OTT service and mobile network profiles in watching TV content. This study aims to investigate the impact of gratifications and attitude formed by mobile TV users on actual mobile TV watching behaviors, as well as the moderating impacts of paid OTT service subscriptions and mobile network profiles, based on gratification theory, cognition–affect–behavioral (CAB) framework, sunk cost effect and walled-garden effect.

This study employs the generalized linear model (GLM) with generalized estimating equations (GEE) to test hypothesized relationships. A total of 338 mobile phone users who have been watching TV content using a mobile phone participated in the survey. The moderating variables, 4 types of paid streaming platform subscriptions, were classified based on the walled gardens formed by mobile telecom services.

The study’s results revealed that obtained gratifications and opportunity constructs substantially influenced a mobile phone user’s attitude and behaviors. Additionally, mobile network profiles and the degree of access to paid platform services played significant moderating roles in the relationship between users’ attitudes and behavior.

This research enriches the existing OTT service literature and is one of the pioneering studies investigating the walled-garden effect’s role in mobile phone users’ actual watching behaviors, offering valuable practical implications for the OTT platform providers.

The manuscript aims to introduce the managerial practice of content recycling – that is, a firm's recycling of its posts on social media platforms. I define and distinguish the phenomenon from related ones and offer propositions for future research to test empirically.

Review of the practitioner literature, in-situ observations with content managers, and a survey of content managers and Facebook users.

Managers recycle their posts to recoup the costs of content. Under some conditions, recycled content may yield more benefits than costs.

I define the phenomenon of content recycling and differentiate it from related terms. I offer propositions for future research.

I inform managers of the benefits and costs of recycling content and conditions under which benefits may override costs.

The research is novel and helps develop a common managerial practice.

Meat is a crucial source of protein and other nutrients for human health. However, excessive consumption of meat products is not advisable due to their elevated sodium and animal fat levels. Hence, there is a strong recommendation for reducing sodium and fat content in meat products. This study aims to delve into the current sodium, total and saturated fat content of meat products in the Brazilian market.

A total of 1,600 products underwent analysis.

The highest sodium concentrations were identified in jerked beef (5.48 g/100 g), charqui (5.21 g/100 g) and salted pork meat (2.58 g/100 g). In contrast, the highest total and saturated fat levels were observed in bacon (35.33 and 12.50 g/100 g), salami (26.00 and 9.25 g/100 g) and pork coppa (22.00 and 9.75 g/100 g). Most meat products were categorized as medium in terms of sodium (77.75%), total fat (52.93%) and saturated fat (48.25%). However, many meat products exhibited high total and saturated fat levels.

This study represents the first comprehensive examination of the sodium, total fat and saturated fat content listed on the labels of many meat products in Brazil.

The study explores the impact of food influencers on consumer behaviour in the social media context. It assesses the interplay between the number of followers an influencer has and the type of content this influencer communicates to the audience. Doing so, the research contributes to the strategic refinement of influencer marketing practices, especially in the food industry.

The study employed an experimental between-subject design 2 (influencer type: micro vs macro) x 2 (content type: informational vs entertaining). It recruited 197 Prolific participants (45.7% female, Mage = 45.076), testing their perceptions towards the influencer and the endorsed product in the social media post.

There was a significant interaction between influencer type and content type on consumers’ attitudes towards and their willingness to buy the advertised product. Specifically, the notion that “the more followers, the better” may only be applicable when consumers peruse the content for entertainment purposes, whereas while they read it for information purposes, a micro influencer (with hundreds to thousands of followers) may have as much impact on consumer behaviour as a macro influencer (with hundreds of thousands to a million followers).

Our findings offer a nuanced understanding into the conventional wisdom that people often follow crowd behaviour. Using the Heuristic-Systematic Model (HSM), we explicate when the number of followers matters and when the content type prevails.

The study aims to investigate the influence of HIX.AI, an artificial intelligence (AI) tool that humanizes the generated content, on the detection capabilities of AI-generated text detectors.

The study investigates the reliability of six AI-generated content detection tools by passing ten essays, five each generated using Chat Generative Pre-Trained Transformer (ChatGPT) and Bard (Gemini) before and after passing through HIX.AI, which humanizes the AI-generated content.

The study found that the selected AI-generated text detectors identified the generated content with inconsistencies. Some of the essays were falsely identified as human-written by a few detectors, indicating that the detectors are unreliable. Post-HIX.AI application found that all the essays were passed as human-written except two, which identified as AI-generated and mixed content by two separate detectors.

The findings present the evolving field of AI-generated text detectors and the tools that can bypass the detectors highlighting the difficulties in identifying the generated content in the presence of the humanization tool. Passing the generated content as human-written has serious consequences, especially in academics. Hence, the study recommends more robust detectors to distinguish human-written and AI-generated content accurately.

The study contributes to the existing literature on AI text detectors and highlights the challenges that humanization tools pose in identifying AI-generated text by AI text detectors.

The notion of using a generative artificial intelligence (AI) engine for text composition has gained excessive popularity among students, educators and researchers, following the introduction of ChatGPT. However, this has added another dimension to the daunting task of verifying originality in academic writing. Consequently, the market for detecting artificially generated content has seen a mushroom growth of tools that claim to be more than 90% accurate in sensing artificially written content.

This research evaluates the capabilities of some highly mentioned AI detection tools to separate reality from their hyperbolic claims. For this purpose, eight AI engines have been tested on four different types of data, which cover the different ways of using ChatGPT. These types are Original, Paraphrased by AI, 100% AI generated and 100% AI generated with Contextual Information. The AI index recorded by these tools against the datasets was evaluated as an indicator of their performance.

The resulting figures of cumulative mean validate that these tools excel at identifying human generated content (1.71% AI content) and perform reasonably well in labelling AI generated content (76.85% AI content). However, they are perplexed by the scenarios where the content is either paraphrased by the AI (39.42% AI content) or generated by giving a precise context for the output (60.1% AI content).

This paper evaluates different services for the detection of AI-generated content to verify academic integrity in research work and higher education and provides new insights into their performance.

The overload of user-generated content in online mental health community makes the focus and resonance tendencies of the participating groups less clear. Thus, the purpose of this paper is to build an early identification mechanism for users' high attention content to promote early intervention and effective dissemination of professional medical guidance.

We decouple the identification mechanism from two processes: early feature combing and algorithmic model construction. Firstly, based on the differentiated needs and concerns of the participant groups, the multiple features of “information content + source users” are refined. Secondly, a multi-level fusion model is constructed for features processing. Specifically, Bidirectional Encoder Representation from Transformers (BERT)-Bi-directional Long-Short Term Memory (BiLSTM)-Linear are used to refine the semantic features, while Graph Attention Networks (GAT) is used to capture the entity attributes and relation features. Finally, the Convolutional Neural Network (CNN) is used to optimize the multi-level fusion features.

The results show that the ACC of the multi-level fusion model is 84.42%, F1 is 79.43% and R is 76.71%. Compared with other baseline models and single feature elements, the ACC and F1 values are improved to different degrees.

The originality of this paper lies in analyzing multiple features based on early stages and constructing a new multi-level fusion model for processing. Further, the study is valuable for the orientation of psychological patients' needs and early guidance of professional medical care.

This study analyzes the indirect (i.e. through brand content engagement-BCE) and direct effects of informative, credible, novel, and aesthetically pleasing attributes of influencer-generated branded content on endorsed brand-related attitude and a subsequent attitudinal spillover effect on the followers' firm-generated endorsed brand-related ad click behavior.

Data for this study were collected from 300 users who follow the top 40 Pakistani social media macro-influencers using an online survey. The conceptual model and hypotheses were tested through process macro and structural equation modeling.

This study finds informative, credible, novel, and aesthetically pleasing factors vital attributes of influencer-generated branded content, which indirectly (i.e. via BCE) and directly affects followers’ endorsed brand-related attitude. This study also finds that these factors have a positive attitudinal spillover effect on followers’ firm-generated endorsed brand-related ad-click behavior on social media.

This study presents guidelines to firms and their partner influencers about designing and implementing follower-valued content-driven influencer marketing campaigns.

This study contributes to extant influencer marketing literature by integrating multiple attributes of influencer-generated branded content, which carry different values for followers, and using the Self-perception theory, the Multi-attribute attitude model, and the Spillover theory to explain their indirect and direct effects on endorsed brand-related attitude and a subsequent attitudinal spillover effect on firm-generated endorsed brand-related ads.

The purpose of this paper is to explore the factors influencing the encountering information adoption of virtual live streaming from the perspective of the immersion experience. In addition, the paper aims to provide new theoretical perspectives and analytical frameworks for virtual live information behavior.

Based on a review of relevant literature and theories, a model of the encountering information adoption of virtual live streaming users is constructed. In order to complete the empirical study, two experiments and questionnaires have been designed to investigate the relationship between high and low immersion experiences. A total of 1,332 valid survey samples were collected and analyzed, utilizing the structural equation model. In order to delineate the regimes, Gradient Boosted Regression Tree (GBRT) and Lasso regression were further utilized.

The research findings indicate that users' immersion experience in virtual live streaming has a positive effect on perceived usefulness, trust, and commitment. Furthermore, perceived usefulness and trust have a positive effect on users' emotional arousal and enhance the content experience, while commitment has a negative effect on the content experience. The emotional arousal and content experience of users contribute to their encountering information adoption. The effect of immersion experience on encountering information adoption is partially mediated by perceived usefulness, trust, commitment, emotional arousal, and content experience. The relationship between content experience and encountering information adoption is moderated by digital literacy to a significant extent. In the context of virtual live streaming, the factors influencing users' encountering information adoption can be divided into three distinct regimes. The most significant factors affecting encounter information adoption are trust and commitment, which are located in the first regime. Emotional arousal and digital literacy are situated in the third regime, with the least significant influence on encountering information adoption.

This study constructs a model of virtual live streaming users' encountering information adoption and explores the formation mechanism of encountering information adoption from the perspective of immersion experience, which provides a new perspective for further understanding the influence of virtual live-streaming users' encountering information adoption.