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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 study aims to examine the visual effects of cause-related marketing (CM) posts on Instagram, with a focus on image resolution and consumer engagement.

Three studies were conducted through an experimental design. Study 1 (N = 155) uncovered the mediation underlying the effects of image quality (low and high image resolution). Study 2 (N = 160) replicated the findings of the first study and extended the investigation by examining the mediator (fluency) and moderator (visual sensitivity). Study 3 (N = 291) further extended the effects of image resolution by demonstrating its interactive effects with the visual complexity of an Instagram post design in a 2 × 2 factorial experiment.

The serial mediation analysis demonstrated that high image resolution CM posts yielded more favorable evaluations in terms of brand credibility and information costs saved, subsequently leading to positive brand attitudes, purchase intentions and increased Instagram engagement. Processing fluency mediated image effects on brand credibility, while individual differences in visual sensitivity moderated the image effects. The image resolution effects were greater for visually complex CM posts compared to simple ones.

To one's best knowledge, little to no research has examined the image quality of Instagram posts in the context of CM and the extent to which such visual cues can affect consumers' brand evaluations and engagement on the platform.

Despite its practical significance, there exists a notable gap in understanding the specific role of CM posts on Instagram and the impact of visual elements on consumer behaviors. The current research findings aim to bridge the research gap.

Fashion merchandising hold a paramount position within the realm of retail marketing. Currently, the purpose of this article is that the assessment of display effectiveness predominantly relies on the subjective judgment of merchandisers due to the absence of an effective evaluation method. Although eye-tracking devices have found extensive used in tracking the gaze trajectory of subject, they exhibit limitations in terms of stability when applied to the evaluation of various scenes. This underscores the need for a dependable, user-friendly and objective assessment method.

To develop a cost-effective and convenient evaluation method, the authors introduced an image processing framework for the assessment of variations in the impact of store furnishings. An optimized visual saliency methodology that leverages a multiscale pyramid model, incorporating color, brightness and orientation features, to construct a visual saliency heatmap. Additionally, the authors have established two pivotal evaluation indices aimed at quantifying attention coverage and dispersion. Specifically, bottom features are extract from 9 distinct scale images which are down sampled from merchandising photographs. Subsequently, these extracted features are amalgamated to form a heatmap, serving as the focal point of the evaluation process. The authors have proposed evaluation indices dedicated to measuring visual focus and dispersion, facilitating a precise quantification of attention distribution within the observed scenes.

In comparison to conventional saliency algorithm, the optimization method yields more intuitive feedback regarding scene contrast. Moreover, the optimized approach results in a more concentrated focus within the central region of the visual field, a pattern in alignment with physiological research findings. The results affirm that the two defined indicators prove highly effective in discerning variations in visual attention across diverse brand store displays.

The study introduces an intelligent and cost-effective objective evaluate method founded upon visual saliency. This pioneering approach not only effectively discerns the efficacy of merchandising efforts but also holds the potential for extension to the assessment of fashion advertisements, home design and website aesthetics.

The purpose of this study is to investigate the effects of visual and verbal information presentations on mental imagery, perceived informativeness and purchase intention. The study assesses two types of product-related information: (1) visual information: static product images and augmented reality (AR) and (2) verbal information: abstract and concrete product reviews.

A total of 320 mobile consumers participated in the experiment. To increase external validity, this study was conducted in an existing digital shopping environment.

The results suggest that AR has a greater effect on consumers' shopping outcomes than static images. The findings further reveal that concrete product reviews are important in increasing mental imagery, perceived informativeness and purchase intention when visual information does not provide an AR function.

This study contributes to the current literature by providing empirical support for AR effects and concrete reviews on consumer responses. The results further provide an important perspective for retailers seeking ways to develop effective information presentations in digital retailing.

The purpose of this study is to evaluate student visual literacy skills using the newly designed visual literacy framework and visual literacy (VL) scale.

It includes a newly designed framework, a self-reporting questionnaire and a scale to evaluate an individual's VL skills and overall competency. The self-reporting questionnaire consists of 13 items with a five-point Likert scale.

The newly developed VL skill scale assessed the Fiji students’ competency (i.e. identify, understand, evaluate and communicate using visuals). The mean for the 13 items on VL skills showed average results, but 46.33% recorded high visual literacy competencies. The multiple linear regression analysis outcomes showed all 13 skills demonstrated significant contributions to becoming visually literate.

The limitation of this study is that the questionnaire is self-reporting, so the evaluation can be highly rated. The implications are that relevant stakeholders will be able to devise strategies and content to improve visual literacy in Fiji.

Images are playing an important role today, especially after COVID-19, which forced the education system to go online. Online learning involves a lot of visuals, and as such, visual literacy is important to students so that they can successfully learn online. This paper brings out the important aspects of visual literacy, which needs to be understood by the students.

In society, everything involves visuals. This paper introduces a visual literacy scale and a visual literacy tool to measure the visual competencies of individuals. If people understand the components of visual literacy, then visual competencies of the people will also improve.

To the best of the authors’ knowledge, this paper is the first one on evaluating visual literacy competencies in Fiji and also in the South Pacific. The visual literacy tool is also new to the world.

The study seeks to contribute to a deeper understanding of the relationship between remediations and participation in new media. By lending some transparency, the analysis hopes to contribute toward generating a critical optics aware of the potentials and pitfalls of emergent media.

The methodology is visual semiotic analysis. The author make no claim for one, true interpretation or critical judgment about the images.

In demonstrating some shortfalls of Instagram affordances, the analysis shows how social media sites can develop tools that encourage users to engage in civic consciousness and respectful political debate. The study makes clear that new media tools can hamper or aid participatory logics.

To author’s knowledge, no other study that has analyzed remediated images related to the controversial confirmation of Brett Kavanaugh to the U.S. Supreme Court. It is also important to place these images in the contexts of “iconicity” in emergent media (a concept increasingly being eroded in new media environment).

While prior studies have explored the relationship between visual appeal and purchasing decisions, the role of bookmarking has largely been underemphasized. This research aims to address this gap by focusing on the impact of bookmarking on consumer behavior, guided by the cognitive load theory and dual-system theory.

The authors executed a controlled experiment and analyzed the results using a two-stage regression method that linked visual appeal, bookmarking and purchase intent. Further empirical analysis was conducted to authenticate the authors' proposed model, utilizing real-world mobile commerce data from a clothing company.

This study's findings suggest that visual appeal influences purchase intent primarily through the full mediation of bookmarking, rather than exerting a direct influence. Furthermore, an increase in colorfulness corresponds positively with visual appeal, while visual complexity exhibits an inverted U-shaped relationship with it.

This study provides novel insights into the choice-set formation process through the theoretical lens of dual-system theory. Additionally, the authors employed an image processing technique to quantify a product's visual appeal as depicted in a photograph. This study also incorporates a comprehensive econometric analysis to connect the objective aspects of visual appeal with subjective responses.

Through examination of the Library Reference Model (LRM) specifications for nomen and the potential challenges visual nomen might present for their description and use in information systems, the purpose of this study was to investigate two questions: (1) how do nonlinguistic or nonalphanumeric signs or symbols act as nomen to identify entities? and (2) what details or attributes are relevant to describe and classify such nomen to integrate them into information systems?

This research was built on an exploratory, qualitative instrumental case study design using multiple (or comparative) cases. Using the International Federation of Library Associations and Institutions LRM conceptualization of nomen as the basis, this research explored the similarities and differences between the LRM definition, its attributes and the use of nonlinguistic and nonalphanumeric “strings” for visual nomen to represent a res, moving iteratively between the LRM documentation, visual nomen identified in previous research and additional examples. This study used a constant comparative method to conduct a structured, focused comparison across different cases found in the source survey.

A close review of the history of the development of the nomen entity was made to understand the semiotic relationship between entities and their symbolic representation, how those symbols are then reified to be further classified and described and how such definitions in the LRM offer a path forward for better understanding the role and function of visual nomen. Based on the foundation of the nomen entity and its attributes established in the LRM, this research then looked at visual representations of concepts and entities to suggest a nascent framework for describing aspects of visual nomen which may be relevant to their use and application

This exploratory study of the use of supralinguistic ways of referencing entities delineates novel insights into a potential framework for describing and using visual nomen as a way of labeling or naming entities represented in information systems. By examining the specifications of the nomen entity and its attributes as delineated by the LRM, this study reinforces the applicability of LRM-defined attributes in the use of visual nomen in addition to offering other attributes or dimensions.

Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.

This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.

The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.

This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

Images and caption descriptions serve as important visual stimuli that influence consumer preferences; therefore, the current study focuses on property images and captions illustrated on the home pages of accommodation-sharing platforms. Specifically, this study investigates the relative importance of hue, brightness and saturation of a property image and caption description styles on potential consumers’ preferences.

A mixed-method approach was used, and a total of 293 valid responses were collected through a discrete choice experiment approach. Interviews were conducted for additional analyses to explore the detailed explanations.

The utility model demonstrated that the image’s saturation was the most critical attribute perceived by the respondents, followed by caption description style, hue and brightness.

This is one of the first studies to investigate the display of attributes on a digital accommodation platform by exploring potential customers’ stated preferences. This study focuses explicitly on images and captions illustrated on the home page of an accommodation booking platform. Detailed image investigation is also a new research area in sharing economy-related research.