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The purpose of this paper is to present a systematic literature review of the application of eye-tracking technology within the field of library and information science. Eye-tracking technology has now reached a level of maturity, which makes the use of the technology more accessible. Subsequently, a growing interest in employing eye tracking as a methodology within library and information science research must be anticipated.

The review follows the guidelines set in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations. Two reference databases are searched for relevant references: Library and Information Science Abstracts and Library, Information Science and Technology Abstracts. The main selection criteria are peer-reviewed literature that describes the experimental setting, including which eye-tracking equipment was used, the number of test persons and reports on the eye-tracking measures. Furthermore, this study will report which other methods were applied in combination with eye tracking.

The number of published research utilizing eye-tracking technologies within library and information science (LIS) is still limited although an increase in the use of eye-tracking technologies is observed during recent years.

To the knowledge of the author, this is the first systematic review on eye-tracking technology and application in LIS.

Business process modeling can involve multiple stakeholders, so it is natural that problems may occur during the designing and understanding processes. One way to perceive these problems is to evaluate the comprehension of business process models through the collection of data related to the readers' eye movement via an eye-tracking device. The purpose of this paper is to provide a comprehensive overview of the use of eye-trackers in understanding process models and to offer a research roadmap to challenge the community to address the identified limitations and open issues that require further investigation.

To achieve this goal, Systematic Literature Review (SLR) was performed following good practices from the Evidence-Based Software Engineering's (EBSE) field.

This study resulted in 10 primary studies selected for analysis and data extraction, from the 1,482 initially retrieved. The major findings indicate that the business process community still benefits little from the use of eye-tracking, e.g. not offering sufficient support for inexperienced designers and not having an explicit standardization in its use. These and other findings are synthesized in a research roadmap which results would benefit researchers and practitioners.

In the studies found, the methods used to explore eyes' movement in process models' comprehension analysis were presented as an advantage of the current study. Additionally, another aspect presented in this SRL as an originality is presenting a set of open questions, suggesting valuable topics for future research through a research script (research roadmap).

The study aims to pioneer an innovative approach for the evaluation of government portal websites (GPWs) by introducing an eye-tracking-based method. The research meticulously pinpoints and analyses the distinct usability issues and challenges that users encounter while navigating and interacting with GPWs.

This study devises an eye-tracking-based GPW usability evaluation approach, which focuses on the major functions (i.e. government information disclosure, government services and interactive responses) of GPWs. An Entropy Weighted Technique for Order Preference by Similarity to an Ideal Solution (EW-TOPSIS) method is employed to process eye-tracking indicator results for deriving GPW usability results.

The proposed approach is demonstrated to assess the usability of 12 GPWs in pilot smart cities in China, and it is found that most GPWs have lower-than-average usability. GPWs with low usability require more cognitive load that exhibit increased fixation and saccade. The comparisons among the GPW usability results from (1) the eye-tracking experiment, (2) questionnaire surveys and (3) the ready-made performance evaluation report validate the effectiveness of eye-tracking-based GPW usability evaluation.

The work contributes to shifting the GPW usability evaluation approach from a subjective judgment paradigm to an objective paradigm, as well as provides implications for enhancing GPW usability, including improving search function, reducing website complexity and prioritizing user needs.

To understand the meaning of a sentence, humans can focus on important words in the sentence, which reflects our eyes staying on each word in different gaze time or times. Thus, some studies utilize eye-tracking values to optimize the attention mechanism in deep learning models. But these studies lack to explain the rationality of this approach. Whether the attention mechanism possesses this feature of human reading needs to be explored.

The authors conducted experiments on a sentiment classification task. Firstly, they obtained eye-tracking values from two open-source eye-tracking corpora to describe the feature of human reading. Then, the machine attention values of each sentence were learned from a sentiment classification model. Finally, a comparison was conducted to analyze machine attention values and eye-tracking values.

Through experiments, the authors found the attention mechanism can focus on important words, such as adjectives, adverbs and sentiment words, which are valuable for judging the sentiment of sentences on the sentiment classification task. It possesses the feature of human reading, focusing on important words in sentences when reading. Due to the insufficient learning of the attention mechanism, some words are wrongly focused. The eye-tracking values can help the attention mechanism correct this error and improve the model performance.

Our research not only provides a reasonable explanation for the study of using eye-tracking values to optimize the attention mechanism but also provides new inspiration for the interpretability of attention mechanism.

The purpose of this paper is to identify attention, cognitive and affective responses towards a fashion retailer's website and the behavioural outcomes when shopping online.

52 eye-tracking tests and 52 qualitative semi-structured interviews were conducted.

Consumer attention and behaviour differ across web pages throughout the shopping journey depending on its content, function and consumers' goal. Top-down attention is more dominant than bottom-up attention when consumers are shopping online for fashion items. The product listings page was the most frequented and had the most time spent on it. Consumers enjoy browsing for products and adding them to their basket to evaluate them together later. Customisation and personalisation features are the most valued due to their ability to make the experience more convenient and enjoyable.

This article contributes novel findings that the content and design of the website affects attention in different ways. It demonstrates that research cannot simplify viewing patterns for fashion shopping online. The study extends the SOR framework, showing that top-down attention, when provided with personalisation and customisation features, results in approach behaviour. A lack of personalisation or customisation features results in avoidance behaviour. The complex nature of consumer attention and behaviour during their holistic shopping journey advocates the need for eye-tracking research to be conducted on a live website for ecological validity, providing a methodological contribution which can be used for future research.

Graphic health warnings (GHWs) on tobacco product packaging constitute one component within a multifaceted set of tobacco control measures. This study aims to understand whether consumers’ attention to GHWs will be associated with recall and quit intentions, using Australia as the case for this study.

Using the 14 GHWs currently in market as visual stimuli, non-probability intercept sampling was conducted, eye tracking and post-survey datasets were collected from a total of 419 respondents across three Australian cities.

Results show the front graphic image areas draw initial attention and the Quitline message area holds the longest attention duration. Attention is highly correlated with better quality of recall of health warning information, emotive responses, believability ratings among smokers and smokers’ perception of health risks and quit intentions. Associations are also noted with perceived health risk and quitting intentions.

To the best of the authors’ knowledge, this is the first study that has objectively tested the effectiveness of in-market tobacco GHWs in Australia and highlights eye tracking as a valid measurement approach that can enhance and drive new insights to evaluate consumer behaviour towards visual stimuli. This study extends new knowledge around the physiological relationships between viewing behaviours, health vulnerability perceptions and intentions to quit smoking, which has theoretical implications for the extended parallel process model which underpins this research.

Construction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.

The study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.

The classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.

The findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.

The classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.

This study analyzed the factors affecting visual attention toward sugar-reduction information (SRI) on sugar-reduced beverages (SRBs) and identified the most optimal SRI type and location using eye-tracking. The eye-tracking results were compared with those of a self-reported questionnaire.

An eye-tracking experiment was conducted on 50 Korean people in their 20s and 30s to analyze implicit responses. Subsequently, a self-reported questionnaire was administered to analyze explicit responses, facilitating the investigation of perceptions, attitudes, preferences, intentions to purchase SRBs, and preferred SRI types and positions.

The results were as follows. First, personal trait-, state-, and product-related factors were found to affect eye movement in relation to SRI. Second, eye-tracking revealed that SRI types and locations that drew long-lasting fixation and attracted considerable attention were similar to those preferred in the self-reported questionnaire. Therefore, to efficiently convey information on SRBs, SRI should be combined with a graphic, and not merely a word, and placed in the upper-right corner, exhibiting consistency with the results of two previous experiments.

This study specifically focused on considering personal and product-related traits while conducting an eye-tracking experiment to investigate the factors that attract consumers' attention. Furthermore, this study is the first to investigate the use of SRI labels to promote SRB selection. What is significant is that both explicit and implicit responses were assessed and compared via a self-reported survey and eye-tracking experiments for various SRB categories.

This study applied eye-tracking techniques and questionnaires within the framework of the Stimulus-Organism-Response Model (SOR) and Technology Acceptance Model (TAM), to investigate the influencing factors of the public acceptance of 5G base stations.

This study used a combination of eye-tracking experiments and questionnaires. The data were analyzed using partial least squares structural equation modeling (PLS-SEM).

(1) The Technology Acceptance Model (TAM) could be used to explain the effects on public acceptance of 5G base stations in the context of the COVID-19 pandemic. The public's perceived usefulness and ease of use of 5G base stations positively affects public acceptance of 5G base stations. (2) The public's perceived risk of 5G base stations has a negative influence on the public acceptance of 5G base stations. (3) The public's visual attention to the different valence information about 5G base stations positively impacts the perceived ease of use while having negative impacts on perceived risk. (4) Visual attention to various valence information of 5G base stations can indirectly influence public acceptance through the perceived risk.

Applying the SOR and TAM to data obtained from eye-tracking experiments and questionnaires, this study analyzed the factors and mechanisms influencing public acceptance of 5G base stations in the context of the COVID-19 pandemic.