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This research delves into the dynamic realm of app design by examining the impact of app icon familiarity and authority on image fit, influencing users' app usage intention. Focusing on the distinctive circumstances of Chinese and Korean customers, the study aims to provide insightful information about how application user behavior changes.

Utilizing structural equation modeling, the study employs data from 293 Korean and Chinese consumers. The research design incorporates a thoughtful approach, including parallel translation methods, focus group interviews, and pre-experimental testing to ensure survey accuracy and validity. The study strategically selects stimuli from the Apple App Store rankings, emphasizing icon features and type considerations.

The results provide important new information about the connections between usage intention, image fit, authority, and familiarity with app icons. Notably, app icon familiarity and authority positively influence image fit. Furthermore, app icon image fit emerges as a positive predictor of usage intention, mediating the complex interplay between familiarity, authority, and intention. The study also identifies moderating effects, shedding light on the nuanced role of app icon features and types.

Originating from a comprehensive exploration of icons, this study significantly contributes to the field by exploring icon differences and uncovering the intricate mechanisms guiding users' decisions. The findings offer valuable insights for app designers, marketers, and researchers seeking a deeper understanding of user behavior in diverse cultural contexts, thereby enhancing the theoretical and practical foundations in app usability and consumer behavior.

The purpose of this study was to explore potential witnesses' memories for a day that was experienced an unremarkable. There may be instances in an investigation in which all leads have been exhausted, and investigators use a broad appeal for witnesses who may have witnessed something important. Investigators can benefit from knowing the types of information that may be recalled in such circumstances, as well as identifying specific methods that are effective in eliciting useful information.

The present study explored how the delay to recall and recall method influenced the recollection of a seemingly unremarkable day that later became important. Participants were asked to recall an experienced event that occurred either recently (a few weeks prior) or in the distant past (a year prior). Participants recalled via either a written method, in-person individual-spoken or collaborative-spoken interviews.

Results suggest an independent benefit for individual-spoken in-person recall (compared to written or collaborative-spoken recall) and recall undertaken closely after an event (compared to delayed recall). Both individual-spoken interviews as well as more recent recollection resulted in a greater number of overall details recalled. The authors further examined the types of details recalled that might be important to progressing an investigation (e.g. other witnesses and records).

The present work provides important implications for interviewing witnesses about a seemingly unremarkable event that later became important.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

The impact of various factors on how TV sports audiences perceive sport event sponsors’ billboards around sports facilities has been the subject of extensive research. Despite numerous factors that impact the effectiveness of sponsor signage at sporting events, there has been a lack of research regarding the language used for such signage around sports facilities’ billboards. Therefore, this study aims to investigate the effects of billboard advertisement language on TV sports audiences’ recognition, recall and search intention to sponsor signage.

This study employed an online experimental design. Participants (n = 925) were recruited from two linguistically different regions: Chinese and English. Participants were randomly assigned to one of two conditions: watching tennis video matches with billboard advertisements presented in either the Roman alphabet exclusively or in a combination of the Roman alphabet and Chinese characters.

This study revealed that although language cannot significantly impact audiences’ unaided recall of a brand, it does have a discernible effect on brand recognition and search intention among audiences. Additionally, people are more likely to search for brands in their native language. Participants from various regions tend to have different recognition rates and search intentions for sport sponsors.

This is the first manuscript examining the use of different languages in relation to audiences’ recognition and recall of sports sponsorship. It provides empirical evidence of the importance of carefully considering the language used in sponsor signage around stadium billboards to optimize the efficacy of sponsorships at sports events.

This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.

This paper aims to explore the opinions of business owners in an industrial area of Abu Dhabi that could be potentially turned into an art tourism destination.

By mobilizing the concept of “gentrification aesthetics,” the authors use a recall technique to explore support toward art from business owners, regression analyses to understand how the type and content of art predicts gentrification support and chi-square to research the differences between respondents who support the area to become a creative place and those who do not.

A model that explains the connection between gentrification aesthetics and art tourism is presented.

The authors’ proposed model results from testing the possibilities for expanding art tourism specifically and may not apply to other types of tourism. Future research is needed to understand whether and how the model can be applied to other forms of tourist consumption.

The current research presents a case study on how tourism can be strategically expanded into more rural places in a city.

The authors found significant differences between respondents who would like to see Mussafah becoming a creative place in five years and those who believe Mussafah needs to be(come) something else.

While work on tourism gentrification has been conducted, the nexus between gentrification aesthetics and art tourism cannot be found. Their relation can help to expend (art) tourism from busy cultural attractions to industrial areas. The present research fills this gap.

Analyses of cultural landscapes need to combine natural and social-cultural components to promote discussions on landscape planning and heritage management. This qualitative research explores the integrated case study of ten municipalities in the “Vineyard Landscape of Piedmont: Langhe-Roero and Monferrato”, Italy, a UNESCO World Heritage cultural landscape. The research aims to raise awareness of its aesthetic-perceptive features, the importance of effective identification of visual impacts and to promote mitigation strategies/actions for updating the current Management Plan.

Two rounds of interviews and focus groups with mayors were performed in 2015 and 2020 to identify trends and drivers of change affecting the territories. Potential mitigation strategies and actions were voted on and selected in response to five critical themes that emerged from the survey, mainly related to real estate and its supplies.

The results suggest tools and policies in the fields of landscape architecture and landscape design that could benefit planning and management at different levels. They support the design of sustainable scenarios, improving mayors' understanding of the significance of cultural landscapes and promoting them as heritage managers. Furthermore, they intend to preserve the authenticity of the landscape by supporting its attributes for long-term conservation.

The research makes an original contribution on the visual implications of anthropogenic landscape transformations in ten municipalities constituting this serial property, six years after its UNESCO nomination (2014).

The use of brand slogans that represent brand concepts on app launch pages can improve user brand impressions. The purpose of this paper is to investigate the impact of using animated or static spokes-characters with brand slogans on app launch pages.

Using the theory of attention selection, the authors conducted two experiments to study the boundary and mediation path of the influence of the motion attributes of spokes-characters (static vs animated) on brand memory based on app launch time (3 s vs 5 s), user engagement with spokes-characters and the level of attention to brand slogans.

Study 1 explores the effect of the interaction between launch time and the motion attributes of spokes-characters on brand memory. The results show that when the launch time of the app is 3 s, the advertisement memory effect of using a static spokes-character is better than that of using an animated spokes-character; when the launch time of the app is 5 s, the advertisement memory effect of using an animated spokes-character is better than that of using a static spokes-character. Study 2 shows that user engagement with spokes-characters and the level of attention given to brand slogans play a continuous mediating role in the effect of the interaction between launch time and the motion attributes of spokes-characters on brand memory.

This paper contributes to the marketing literature by expanding the knowledge of spokes-characters and animated visual images, providing new insights for future research.

Good abandonment behavior refers to users obtaining direct answers via search engine results pages (SERPs) without clicking any search result, which occurs commonly in mobile search. This study aims to better understand users' good abandonment behavior and perception, and then construct a good abandonment prediction model for mobile search with improved performance.

In this study, an in situ user mobile search experiment (N = 43) and a crowdsourcing survey (N = 1,379) were conducted. Good abandonment behavior was analyzed from a quantitative perspective, exploring users' search behavior characteristics from four aspects: session and query, SERPs, gestures and eye-tracking data.

Users show less engagement with SERPs in good abandonment, spending less time and using fewer gestures, and they pay more visual attention to answer-like results. It was also found that good abandonment behavior is often related to users' perceived difficulty of the searching tasks and trustworthiness in the search engine. A good abandonment prediction model in mobile search was constructed with a high accuracy (97.14%).

This study is the first to explore eye-tracking characteristics of users' good abandonment behavior in mobile search, and to explore users' perception of their good abandonment behavior. Visual attention features are introduced into good abandonment prediction in mobile search for the first time and proved to be important predictors in the proposed model.