Life on Earth requires healthy ecosystems. As environmental degradation and climate change accelerate, we need innovative digital solutions to foster more sustainable development. Information systems play a critical role in enabling sustainable behaviors at the individual level. However, existing sustainable human-computer interaction research has faced criticism for relying heavily on persuasive technologies that overlook user diversity and the complexity of everyday life. In this paper, we develop design principles for sustainable information systems that help individual users engage in responsible consumption with a focus on reducing food waste—a globally relevant and underexplored issue aligned with the United Nations’ Sustainable Development Goal 12.3. We use social cognitive theory and user archetypes as theoretical lens and apply established methods for deriving design principles. Based on an in-depth case study, we further refine design principles from both users’ and system providers’ perspective. Our study contributes to the sustainable human-computer interaction literature by presenting three meta-requirements and six design principles that accommodate diverse user motivations and behaviors. These principles move beyond one-size-fits-all persuasive strategies and offer actionable guidance for designing sustainable information systems that address the environmental, social, and economic dimensions of sustainability.
Despite several advantages over traditional donation fundraising models, donation crowdfunding is one of the smallest crowdfunding models by volume worldwide. Two typical challenges that donation crowdfunding faces are attaining legitimacy from potential donors and motivating potential donors to contribute. A fundraiser’s narrative and its linguistic style play an instrumental role in overcoming these challenges. However, academic research on the link between linguistic styles in fundraisers' narratives and potential donors' altruistic motivation remains in its infancy and is scarce in the donation crowdfunding literature. To bridge this gap, we use the multidimensional view of altruism to understand how fundraisers' narrative affects potential donors' intention to donate by provoking altruistic motivation in general and its seven dimensions in particular. Our findings from our experiment provide insights for fundraisers, crowdfunding platform designers, and scholars. In the donation crowdfunding context, our findings explain how linguistic narrative styles (social, emotional, and religious) may bring a change in potential donors' mixed altruistic motives (pure and pseudo-altruistic motives), which influence potential donors’ intention to donate.
Researchers and practitioners across multiple disciplines have paid significant attention to the ways individuals search for and use information. As tools evolve, so do users’ strategies. For the last decade or more, research has focused on information-acquisition approaches via search engines. However, as more advanced tools built on generative artificial intelligence (GenAI) have emerged, the way individuals search for information online has changed, which requires updates to previous information behavior models. We explore these changes by examining how 455 students at a university in the United States identified and attempted to satisfy information needs with a new generative AI tool— ChatGPT—during the first full semester of its public availability in early 2023. This new information behavior departs from previous search-focused models in that it underscores GenAI’s iterative nature and highlights the possibilities of human-machine interaction for information acquisition and generation. Specifically, we highlight four key informational needs that trigger a GenAI interaction (retrieval, generation, revision, and evaluation), and we identify three distinct ways (refinement, contextual, and reset) in which searchers iteratively interact with ChatGPT to arrive at the desired information—a phenomenon we term “cycling”.
Drawing on the persuasion and branded-entertainment literature and using an integrative theoretical lens for understanding in-game advertising (IGA) effectiveness, we conducted a dual experimental study to elucidate the mechanisms for persuasion and branding in IGA. Specifically, we studied the varying impacts of prominent and subtle brand placements and love for online games on IGA’s persuasive intent and their possible indirect influence on users’ brand recall, brand attitude, and brand advocacy. We also analyze how IGA-game uniqueness and users’ need for cognition moderate brand recall and brand attitudes. Results from the first study show that prominent brand placements enhance the identification of persuasive intent more than subtle brand placements and that love for playing online games helps individuals identify IGA persuasive intent. Results from the second study demonstrate that IGA that has a congruent and prominently placed brand enhances brand recall, IGA that has an incongruent and prominently placed brand improves brand attitude, and need for cognition moderates both relationships. This paper enriches the marketing field by demonstrating the relevance of an emerging concept called love for playing online games in the gamified advertising context and supports attention and persuasion models. Additionally, results suggest that brand managers need to use IGA-related and user-related factors in the right combinations to effectively persuade users through IGA.
The metaverse, driven by advances in augmented reality (AR), virtual reality (VR), and artificial intelligence (AI), is reshaping digital marketing while raising complex ethical challenges. This systematic literature review analyzes fifty peer-reviewed literature reviews (2019-2024) retrieved from Scopus and AISeL, focusing on digital marketing, branding, and the metaverse. The review maps objectives, themes, contribution types, publication outlets, and methodologies, showing that most contributions are theoretical (68%), followed by empirical (16%), methodological (12%), and opinionbased (4%), with PRISMA emerging as the dominant reporting standard. Through meta-synthesis, we identify and analyze four core ethical dimensions: privacy, transparency, authenticity, and fairness. By integrating insights across fragmented domains, the review highlights how these ethical challenges intersect and require interdisciplinary responses involving marketers, platform providers, policymakers, and consumers. We advance theoretical implications by linking immersive marketing to foundational perspectives such as privacy theory, media richness, self-determination, and algorithmic transparency, while also outlining nine actionable implications for practice and future research. In doing so, this review of reviews establishes a foundation for responsible, inclusive, and sustainable digital marketing in the metaverse.
Traditionally associated with private life and individual pursuits such as fitness, sleep, and environmental sustainability, personal informatics (PI) systems are increasingly being integrated into workplace settings to support employee wellbeing initiatives and enhance productivity. Although research on this phenomenon is ongoing and of practical relevance, the scholarly landscape remains fragmented, which makes it challenging to understand research efforts in this emerging field. Therefore, we conducted a mapping review to synthesize existing knowledge and guide future research in this field. Based on the analysis of 168 studies, we propose a conceptual framework grounded in a socio- technical perspective structured around five components (technologies and tools, tracking domains, workers, context, and outcomes) and eight propositions to stimulate future scholarly work that broadens and deepens the scope of workplace PI research.
Advances in location-tracking capabilities of smartphone devices have raised major privacy concerns for users. This paper investigates how users form location privacy concerns about smartphones as a cognitive schema that emerges from their experiences with device-level location disclosure. We developed an extended privacy calculus model that addresses how the notion of control and extrinsic and intrinsic influences contribute to individuals' privacy concerns regarding location disclosure on smartphones. In analyzing data from 559 smartphone users, we found that their perceived device-level privacy control had a significant impact on how they assessed not only the overall risks but also the overall benefits experienced in location-disclosure instances on smartphones. Furthermore, we found that disclosure motivators and demotivators resulting from both social influences and privacy breach experiences were associated with the dual calculus trade-offs, but each had their own consequences. The findings contribute to the limited knowledge on location privacy concerns with using smartphone devices in contrast to privacy concerns with location-based services. The proposed theoretical model generates novel and granular insights into the complex mechanisms that frame privacy concerns based on the perceptions developed over information disclosure experiences.
Designing AI chatbots with human-like features is a key way to promote user engagement, such as self-disclosure. Prior research has shown that anthropomorphism can foster self-disclosure intentions via systematically enhancing trust and reducing privacy concerns, a mental process captured in the privacy calculus lens. Building on this prior work, we put forth a contextual privacy calculus approach to actual disclosure behavior. We identify two salient context factors in human-chatbot interactions: psychological social distance and information sensitivity and theorize their distinct roles in shaping the privacy calculus. Through an online experiment with 222 participants, we manipulated the design to induce anthropomorphism and observed participants' actual disclosure behavior. An ANOVA test together with Hayes's PROCESS macro analysis showed that: 1) anthropomorphism can reduce psychological social distance but may trigger the "uncanny valley" effect, 2) privacy concerns can reduce actual disclosure, but this tendency weakens under high-sensitivity conditions, 3) trust in AI chatbots may not necessarily lead to actual disclosure. These findings highlight the need for careful anthropomorphic design to avoid its downsides. We also show that actual sharing behavior follows different mechanisms than sharing intentions. We encourage future research to explore the interplay between anthropomorphic design, context factors, and actual behavior in human-chatbot interactions.
Sensemaking plays a critical role in social media, which features ambiguous, equivocal, and dynamic information, views, and opinions. While existing research has focused on sensemaking with respect to text entries related to extreme events, we examine sensemaking with respect to video content on social media. Drawing on sensemaking theories, we investigate the relationships between video features and sensemaking activities. Specifically, we analyze how information control, cue, and noise in YouTube videos affect sensemaking activities on YouTube and Reddit. Our findings reveal both similarities and differences in how these factors relate to sensemaking activities across the two platforms. This research enriches the current literature on social media sensemaking and provides insights for designing videos that better facilitate sensemaking activities and enhance user experiences.
Generative AI has transformed how we search for and process information in both personal and professional environments. Despite its rapid diffusion, the specific factors driving or hindering individual adoption remain underexplored. To bridge this gap, we conducted a qualitative study focused on ChatGPT and identified a range of technological, personal, organizational, and social factors influencing users' acceptance, resistance, and ambivalence. We also examined emotional, experiential, and ethical responses that explain divergent usage trajectories. Building on these insights, we propose a comprehensive framework that maps the constructs and relationships that shape generative AI adoption, resistance, and ambivalence. Further, we highlight a series of paradoxes (e.g., simplicity vs. complexity, technophobia vs. technophilia, techno-optimism vs. techno-pessimism, overdependence vs. independence, and mandatory vs. volitional use) that collectively complicate human-generative AI interactions. This study extends technology adoption literature by incorporating emergent themes specific to generative AI while offering practical implications for AI designers and policymakers.
In online environments, such as websites or mobile applications, users display one of two distinct search modalities: hedonic or utilitarian. Hedonic users typically navigate through a site or app for exploration, whereas utilitarian users focus more on retrieving specific information or completing a particular task. A platform's features and immersive user interface (referred to as the interaction modality) influence these behaviors. The interaction modality can be categorized into non-immersive or immersive types based on the degree of engagement and telepresence capabilities. We conducted a controlled laboratory study to investigate how search modality and interaction modality influence the flow search experience. Flow search experience refers to an elevated state of focus and pleasure that leads users to become fully absorbed in their search activity. We found that, while search modality did not significantly affect the flow search experience, the interaction modality did; specifically, an immersive interaction modality enhanced the flow search experience compared to a non-immersive one.
In recent years, smart healthcare systems (SHS) spurred by the Internet of things have revolutionized how people manage their health. Despite their numerous benefits, SHS still face many challenges worldwide, including limited user motivation. This study builds on the valence-instrumentality-expectancy theory to examine the motivational factors influencing the use of SHS, while also considering the moderating role of social support. We conducted two studies- one in the United States and the other in Qatar-to assess how well the proposed model applies across different cultural contexts. Our findings show that the model explains users' motivations to use SHS and the interdependencies between the valence-instrumentality-expectancy theory and factors like social support, self-efficacy, and trust in the system. Moreover, we highlight the significance and implications of cultural differences in the model. This study is among the first to investigate the motivational factors behind SHS use through cross-cultural samples, offering valuable insights for global SHS providers.
The inability to assess borrowers' default behavior severely threatens the development of micro-lending platforms. Call activity and online social activity, as important components of soft information, have great potential for assessing borrowers' default risk. Although a wealth of literature on microfinance explores the relationship between borrowers' call activity and default behavior, few studies have focused on borrowers' online social activity and combined the two types of information to assess their default risk. This study explores the impact of borrowers' call activity (call activity frequency and call activity stability) and online social activity (online social activity frequency and online social activity stability) on the default behavior of borrowers on micro-lending platforms based on bonding and bridging capital in social capital theory. We collected 154,579 loan records from 10 micro-lending platforms in Indonesia as research data, constructed an empirical research model to verify our hypotheses, and used multiple robustness tests to demonstrate the consistency of the results. The empirical results show that call activity frequency and call activity stability have a significant negative impact on default behavior. In contrast, online social activity frequency and online social activity stability have a significant positive impact on default risk. Our results not only contribute to social capital theory and relevant research on microfinance but also provide practical insights for improving the credit assessment model of micro-lending platforms.
While the widespread adoption of smartphone and tablet devices has led to a rapid proliferation of mobile apps, these apps seldom feature user interfaces (UI) geared towards older users (i.e., 60 years and older). In this paper, we report on a scoping review that we conducted to investigate what design considerations researchers consider when developing mobile apps for older users. Structured along the transmission model of communication, we conceptualize users' interaction with mobile apps as a bidirectional communication process that involves source, transmitter, receiver, and destination. Building on this conceptualization, we synthesize the considerations that the reviewed studies applied in their mobile app design for older users and provide design guidelines in the form of a practical checklist for system developers. Our findings provide important insights into the challenges that researchers encounter when designing mobile apps for older users and how they can address these challenges.
We investigate the factors influencing knowledge workers' intention to adopt ChatGPT in the workplace. Using technology affordance and constraints theory, along with focus group discussions, we identify three ChatGPT affordances (i.e., automatability, information quality, and productivity) and two constraints (i.e., perceived risk and lack of regulation) for early-stage users. Using a survey approach, we examine how these affordances and constraints influence the psychological perceptions of ChatGPT, which in turn affect adoption intention. We also explore the role of personal innovativeness and organizational innovation culture in moderating the effects on adoption intention. Our findings indicate that the three ChatGPT affordances positively influence perceptions of ChatGPT's effectiveness, whereas the lack of regulation is a constraint that increases discomfort with using ChatGPT. Furthermore, personal innovativeness enhances the positive effect of perceived ChatGPT's effectiveness on adoption intention and mitigates the negative impact of discomfort with using ChatGPT. This study provides valuable insights and implications for generative AI researchers and managers seeking to effectively leverage ChatGPT in the workplace.
AI influencers, also known as virtual influencers, refer to computer-generated digital characters that have gained a large social media following in recent years. These computer-generated entities perform similar tasks to human influencers, such as promoting products, engaging with followers, and building online communities. Despite not being real individuals, AI influencers have become trusted tastemakers in various niches and offer companies various advantages, such as lower costs, greater content control, and more personalized content delivery. However, AI influencers have not yet achieved widespread adoption and face significant challenges. We used the fuzzy analytical hierarchy process (FAHP) method to identify and classify the various factors that impede organizations from adopting AI influencers using a multi-stakeholder perspective. We identified seven primary barriers and 38 subbarriers. We performed a sensitivity analysis to confirm our approach's robustness.
As organizations increasingly adopt chatbots, the complexity of chatbot-human conversations also increases. The success of chatbots depends on their ability to interpret discourse contexts and provide meaningful responses that resonate with users. Discourse-analysis methods, which can elucidate the intricacies of conversation design, discourse structures, and semantics, play an instrumental role in guiding efforts to design human-chatbot conversations and ensuring that they resemble human-human conversations. For this research, we meticulously reviewed 92 scholarly papers that considered discourse-analysis methods for chatbot development to unravel the connections between discourse structures and the dimensions of chatbot design. Furthermore, we examine the extent to which the chatbot dimensions across the four distinct chatbot conversation lifecycle phases align with one another to ensure a comprehensive, cradle-to-grave approach. Additionally, we delved into the conversation modeling dilemmas that emerge when designing contextually sensitive chatbots.
We examine the effects of key metaverse characteristics on users' experiences of awe and place attachment as well as how these experiences influence user engagement. Using a mixed-methods approach, we first conducted in-depth qualitative interviews to explore relevant constructs and relationships, then empirically validated the model we developed from the interviews. We found focused immersion, enjoyment, and telepresence to be positively associated with awe and place attachment. In turn, awe and place attachment significantly predict user engagement, with social threat moderating the relationship between place attachment and engagement. This study contributes to the growing body of literature on the metaverse and user engagement, while also extending theoretical insights from flow theory. The findings offer practical guidance for designers and developers seeking to enhance user engagement in immersive digital environments.
