Progress In Electromagnetics Research B

PurposeChat Generative Pretrained Transformer (ChatGPT), a chatbot with artificial intelligence (AI) technology, opens up new directions for innovation. However, the extent to which literature has not considered the trustworthiness and satisfaction of ChatGPT. Those are important elements leading to continuous use (CU). Particularly, this study investigates the use of the ChatGPT Translate function. Requirements for task-AI-technology fit, trust and satisfaction relevant to ChatGPT Translate are addressed in this study.Design/methodology/approachTask-technology fit (TTF) theory forms the theoretical lens to examine the influences of TTF, AI-tech trust and satisfaction on CU of AI technology. A questionnaire survey was used for data collection. Structural equation modeling was employed to test the research model.FindingsThe findings show task and technology characteristics have positive effects on task-AI-technology fit. Task-AI-technology fit has a positive effect on AI-tech trust, which in turn has a positive effect on the CU of AI technology. Finally, the level of CU of AI technology by users satisfied with its responses is higher than users dissatisfied with its responses.Originality/valueThe results have important theoretical and practical implications for academia and industry to devise strategies and policies on a free-to-use AI system.

PurposeThis study investigates the impact of quality, technology and trust on customers’ purchase intentions and word-of-mouth in S-commerce.Design/methodology/approachData were collected from 1,162 respondents in India using a quantitative methodology and convenience sampling. The conceptual model and hypotheses were examined using partial least squares structural equation modeling (PLS-SEM).FindingsThe findings illustrate that perceived value is influenced by quality, technology and trust. Comprehending perceived value is essential for influencing customers’ buying choices and their inclination to promote favorable word-of-mouth regarding S-commerce websites. In addition, system quality was the most significant predictor among the nine predictors of perceived value for S-commerce sites.Originality/valueThis study represents an initial investigation to elucidate the influence of quality, technology and trust on customers’ purchase intentions and word-of-mouth behavior during Indian consumers’ transition to social commerce, thereby enabling marketers to enhance their business and customer retention strategies.

PurposeThis study aims to develop and validate a behavioral intention model for understanding the health information behavior of Generation Z females on social media. The model integrates variables such as familiarity, e-health literacy, trust and altruistic motivation.Design/methodology/approachConducted in Indonesia, the online survey involved 516 active female Generation Z respondents (aged 17–26) seeking health information on social media in the past six months. Utilizing the partial least squares structural equation model (PLS-SEM), the study employed Smart PLS Version 4.0 for rigorous model validation and hypothesis testing in two stages: measurement and structural model analyses. This encompassed evaluating reliability, convergent validity and discriminant validity.FindingsResults indicate that familiarity, e-health literacy, trust and altruistic motivation significantly influence the health information behavior of Generation Z females on social media. Furthermore, both familiarity and e-health literacy positively impact trust in social media as a reliable health information source. The study explores theoretical, managerial and policy implications.Originality/valueSocial media has become a crucial platform for health-related information, particularly among Generation Z females. Despite this, there is a significant research gap in the behavioral intention model for Generation Z females’ health information behavior on social media. This study introduces a unique information behavioral intention model shedding light on this behavior.

PurposeThe proliferation of Artificial Intelligence of Things (AIoT) devices has introduced notable privacy concerns, influencing user adoption and trust. This study integrates privacy calculus theory with the technology acceptance model to analyze how privacy risk perception affects users’ intentions to adopt and continue using AIoT devices.Design/methodology/approachA research model was developed and validated using data from 313 AIoT users. Findings indicate that perceived usefulness and ease of use significantly enhance users’ trust in AIoT devices. Additionally, prior privacy experiences and privacy knowledge amplify users’ privacy concerns.FindingsPrivacy risk perception and concerns reduce trust in AIoT devices but do not significantly deter continued usage intentions, highlighting a “privacy paradox” where functionality and convenience outweigh privacy concerns. Future research is encouraged to examine user attitudes across diverse demographics and controlled settings to gain deeper insights into privacy perceptions and behaviors toward AIoT.Originality/valueThese findings contribute to a comprehensive understanding of AIoT adoption dynamics and offer practical implications for designing privacy-conscious AIoT applications.

PurposeThis research focuses on the analysis of the recommendation algorithms employed by Google Discover, utilizing data from two e-commerce platforms operating in Poland.Design/methodology/approachThe study uses the information obtained from Google Search Console in a time span of 17 months. The examination of Google Discover focuses on the number of displays, clicks and click-through ratio, from the viewpoints of content publishers and web users.FindingsThe results suggest that user engagement positively influences a website’s efficiency in Google Discover, yet the algorithm also considers variables such as the popularity of similar content on other websites, user location and content update frequency. Thus, a website may be excluded from Discover despite a substantial click count.Originality/valueThere is a lack of studies on how Google Discover is perceived by users based on real data. We offer a quantitative perspective, which has not yet been done. This study offers an overview of the history and evolution of Google Discovery, an overview of data we used to show the perception of the service, and two unique perspectives on recommender service, users and publishers.

PurposeAs the two sides of the same coin, usefulness and usability have emerged as pivotal research themes in user experience field. This study compares cognitive effort and cognitive resource allocation strategy across documents varying perceived usefulness and then across documents with different objective usability (unimodal vs multimodal discourses).Design/methodology/approachA controlled user study of four identifying tasks related to public health epidemics was conducted to collect data, including document usefulness as perceived by participants, presentation modes of the document and gaze behaviors on each document.FindingsUsefulness and modality discourse impact cognitive effort and resource allocation strategy in health information search. In useless health documents, spatial encoding resource spending increased significantly with multimodal discourse, and a spatial browsing strategy with an evident exploratory feature was applied; while in useful documents, including low-useful and high-useful, both spatial and information encoding resource spending increased significantly with multimodal discourse, and an information processing strategy with an evident comprehensive feature was applied. Notably, multimodal discourse failed to enhance decision-making effectiveness. Furthermore, in useful documents, the interaction effect of the presentation mode of useful information and multimodal discourse on cognitive effort followed an inverted U-shape pattern.Originality/valueThis paper sheds new light on the interaction effect of usefulness and usability on cognitive effort and resource allocation strategy, highlighting its significance in cognitive effort detecting for multimodal discourse and improving effectiveness and efficacy of health information identification by optimizing information presentation mode design.

PurposeThis article investigates the impact of consumers’ promotion strategies on the effectiveness of promotional campaigns for subscribing to Over-the-Top (OTT) streaming services, from a prospective theoretical perspective. Additionally, it explores the moderating effects of platform type and subscriber type on promotion strategies in the context of OTT streaming services.Design/methodology/approachThis study employs an experimental design to separately examine the promotion strategies of subscribers. The first part investigates the effect of promotion strategies on promotional effectiveness, regardless of platform and subscriber types, while the second part considers the moderating effects of both platform type and subscriber type on promotional effectiveness.FindingsThe analysis reveals that renewed subscribers exhibit a positive attitude toward incremental promotions and have stronger intentions to subscribe and recommend. Nonrenewed subscribers, in contrast, prefer discount promotions, demonstrating a positive attitude toward them and expressing higher intentions to subscribe and recommend.Originality/valueThis study applies prospect theory to OTT promotion strategies, uncovers the psychological drivers behind promotion effectiveness and examines the moderating role of subscriber type and platform type, thereby providing actionable insights to improve consumers’ attitudes and behavioral intentions.

PurposeTo explore the influence factors and pathways of users’ willingness to participate in the misinformation purification process on the Weibo platform. The findings of this study are expected to provide valuable insights that can enhance the self-purification mechanisms for misinformation on Weibo, thereby contributing to the effective misinformation control.Design/methodology/approachThe theoretical framework of the quantitative study is a conceptual model integrated with the theory of planned behavior (TPB), social exchange theory (SET) and co-dependency theory. This model was developed to elucidate the influence factors of users’ willingness to participate in the purification of misinformation on the Weibo platform, the conceptual model was tested and refined through questionnaire surveys, structural equation modeling (SEM) was used to assess its validity and reliability.FindingsThe findings reveal that the attitude toward misinformation purification on the Weibo platform exerts the most significant positive influence on the willingness to engage in such activities. Within the context of this research, community involvement and reciprocity are identified as the factors that have the most substantial positive impact on users’ attitude toward misinformation purification. Conversely, risk perception does not demonstrate a significant influence on users’ attitude toward misinformation purification.Originality/valueTaking the Weibo platform as an example, this is a pioneering study on the investigation and mechanism of social media self-purification on misinformation and proposes a new perspective to improve the effectiveness of the social media self-purification mechanism from the perspective of focusing on user intention and motivation.

PurposeChatbots are increasingly embodied in business and IS contexts to enhance customer and user experience. Despite wide interest in chatbots among business and IS academics, surprisingly, there are no current comprehensive reviews to reveal the knowledge structure of chatbot research in such areas.Design/methodology/approachThis study employed a mixed-method approach that combines systematic review and bibliometric analysis to provide a comprehensive synthesis of chatbot research. The sample was obtained in December 2023 after searching across six databases: EBSCOhost, PsycINFO, Web of Science, Scopus, ACM Digital Library and IEEE Computer Society Digital Library.FindingsThis study reveals the major trend in publication trends, countries, article performance and cluster distribution of chatbot research. We also identify the key themes of chatbot research, which mainly focus on how users interact with chatbots and their consequences, such as users’ cognition and behavior. Moreover, several important research agendas have been discussed to address some limitations in the current chatbot research in business and IS fields.Originality/valueThe present review is one of the first attempts to systematically reveal the ongoing knowledge map of chatbots in business and IS fields, which makes important contributions and provides useful resources for future chatbot research and practice.

PurposeThe purpose of this research is to examine generative artificial intelligence (AI) user continuance intention based on the stimulus-organism-response model.Design/methodology/approachWe adopted a mixed method of structural equation modeling and fuzzy-set qualitative comparative analysis to conduct data analysis.FindingsThe results found that generative AI content quality (perceived personalization, perceived accuracy and perceived credibility) and system quality (perceived interactivity, perceived anthropomorphism and perceived intelligence) affect sense of empowerment and satisfaction, both of which further determine continuance intention.Originality/valueExtant research has identified the effect of flow, trust and parasocial interaction on generative AI user continuance, but it has seldom disclosed the internal decisional process of generative AI user continuance intention. This research tries to fill this gap, and the results enrich the extant research on generative AI user continuance.

PurposeThis study aims to explore the impact of emotional cues in knowledge product descriptions on users’ willingness to gather information. It specifically focuses on how different types of textual emotional cues, including heuristic cues like “emotional titles” and systematic cues like “emotional synopses,” influence users’ information-gathering willingness and examines the mediating role of emotional arousal in this process.Design/methodology/approachA conceptual model was developed by integrating the heuristic-systematic model with cue utilization theory. The experimental design employed knowledge product descriptions from the “Knowledge Column” section of the Zhihu platform. A controlled experiment was conducted to investigate the effect of varying emotional cues in these descriptions on participants’ willingness to gather information.FindingsThe study identified two types of emotional cues – heuristic cues, such as “emotional titles,” and systematic cues, such as “emotional synopses” – that significantly and positively influence users' information-gathering willingness. Additionally, emotional arousal was found to mediate the relationship between emotional cues and users’ willingness to gather information in the context of knowledge payments.Originality/valueThis study confirms that emotional cues in knowledge product descriptions, mediated by emotional arousal, can enhance the information-gathering willingness of knowledge payment users. The research deepens the theoretical exploration of information behavior among online knowledge payment users, providing valuable insights for knowledge producers on effectively leveraging emotional cues to attract potential customers as well as offering guidance for knowledge payment users in their information-gathering practices.

PurposeUser engagement is critical for online health Q&A communities. Financial incentives, which vary across different communities and reward schemes, are expected to motivate such contribution behaviors. Even though financial incentives have been extensively examined in prior studies, the impact of newly designed contingent financial incentives of a new pay-for-answer reward scheme has not been empirically examined in any online health Q&A community. Given this research gap, our study aims to perform an exploratory investigation of the effects of contingent financial incentives on user engagement in terms of knowledge contribution and social interactions.Design/methodology/approachBased on expectancy-value theory and equity theory, a research model was developed to reflect the influences of contingent financial incentives on user engagement. A unique dataset was gathered from a large online health Q&A community utilizing this contingent financial incentive reward structure, and the Heckman selection model was applied using a two-step procedure to test these hypotheses. Possible endogeneity issues were also addressed in the robustness check.FindingsOur results demonstrate that the effect of contingent financial incentives on answer quantity and quality is quadratic. Additionally, our study reveals that this contingent financial incentive enhances both comment and emotional interactions among users.Originality/valueOur study enriches the literature on financial incentives, knowledge contribution and user engagement by revealing the nuanced effects of financial incentives within a novel pay-for-answer scheme. This study also offers significant implications for practitioners involved in online community incentive design.

PurposeIn China, it is prevalent for parents to share health information on WeChat and receive feedback from their children. This study aims to investigate the feedback from younger generations regarding their parents’ health information sharing. It will examine the different types of feedback, explore the factors influencing it and analyze the outcomes that result from this feedback exchange.Design/methodology/approachThe empirical findings draw on the qualitative analysis using grounded theory. This study collects data from 34 participants (17 pairs of one young person and one parent) through in-depth interviews and WeChat chat records. Then, a theoretical model was developed through open, axial and selective coding.FindingsFeedback can be classified into five types: support, correction, perfunctoriness, ostracism and rejection as well as into “Affective-Behavioral-Cognitive” dimensions. Younger generations’ feedback types are influenced by a variety of factors, including information, emotion and individual and family-related factors. Each feedback type has distinct effects, such as altering older generations’ emotional and communication responses.Originality/valueThis pioneering study explores how younger generations in China perceive their parents’ health information sharing on social media. It highlights the importance of feedback in this context, providing actionable insights to enhance digital literacy among older adults, strengthen family bonds and enhance the spread of valuable and scientific health information online.

PurposeThis study aims to investigate the impact of ChatGPT on users’ continuous knowledge contributions in online question-and-answer (Q&A) communities based on social exchange theory and stimulus-organism-response (SOR) theory.Design/methodology/approachData were collected from a Chinese online Q&A community, and the difference-in-differences (DID) model was employed to verify the proposed hypotheses.FindingsThe results show that ChatGPT negatively impacts users’ continuous knowledge contributions in online Q&A communities, with variations observed across different knowledge domains.Originality/valueThis study is the first attempt to examine the impact of ChatGPT on users’ continuous knowledge contributions in online Q&A communities. The findings provide valuable insights for community managers to develop strategies for mitigating the effects of ChatGPT on online Q&A communities.

PurposeHealth short videos are serving as a powerful tool for encouraging individuals to actively adopt healthier behaviors. The sensory cues applied in these videos can be useful for engaging peripheral processing and enhancing attitudes. While previous research has examined the effects of various single cues, this study features a pioneering attempt to explore the roles of audiovisual cross-modal correspondence, encompassing multisensory cues perceived through different modalities, in health communication.Design/methodology/approachA 2 (color: warm/cool) × 2 (music tempo: fast/slow) between-subjects experiment was conducted to observe 120 participants’ responses to a health short video promoting eye health that was created using four different combinations of background color and background music tempo.FindingsIt was found that the congruent color–tempo pairings, that is blue & slow and orange & fast, led to more positive attitudes toward the videos than the incongruent pairings, that is blue & fast and orange & slow. The effect of cross-modal correspondence on attitude was fully mediated by processing fluency, with gender acting as a moderator between the two variables. Furthermore, individuals’ attitudes toward a short video positively influenced their health behavioral intentions.Originality/valueThese findings not only lend support to the theoretical framework of “multisensory cues-fluency-attitude-intention” chain for persuasion purposes but also have practical implications for creating effective health short videos.