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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 aim of the study is to assess the effect of employer branding dimension of training and development on the retention of employees through the mediation of organizational identification (OI).

The study is based on data collected from the Indian IT employees. Regression and PROCESS Macro were used to test the hypotheses.

The regression results revealed that employer branding attribute of training and development influence employee retention (ER). Second, training and development influences OI and ER. Third, the relationship between training and development and ER is mediated by OI.

The research provides a new direction, strategizing a brand around development value is effective to enhance OI and their intent to stay. The major limitation is that the data is collected from a single source that may result in the issue of common method biasness.

The paper focuses on measuring the effects of training and development on the retention of employees through the mediation of OI, for which studies are very limited.

Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes.

The authors collected 1,206,587 reviews from 25,369 consumers of 46,613 restaurants from Yelp.com. Using these data, the authors generated a consumer preference vector by combining consumer identity and online consumer reviews. Thereafter, the authors combined the restaurant identity and food categories to generate a restaurant information vector. Finally, the nonlinear interaction between the consumer preference and restaurant information vectors was learned by considering the restaurant attribute vector.

This study found that the proposed recommendation model exhibited excellent performance compared with state-of-the-art models, suggesting that combining various textual information on consumers and restaurants is a fundamental factor in determining consumer preference predictions.

To the best of the authors’ knowledge, this is the first study to develop a personalized restaurant recommendation model using textual information from real-world online restaurant platforms. This study also presents deep learning mechanisms that outperform the recommendation performance of state-of-the-art models. The results of this study can reduce the cost of exploring consumers and support effective purchasing decisions.

关于餐厅的文本信息, 如在线评论和食品分类, 对于消费者的购买决策产生至关重要。然而, 先前的餐厅推荐研究未能有效利这些文本信息去预测消费者喜好。本研究提出了一种新颖的餐厅推荐模型, 以有效估计消费者对多个餐厅属性的评估行为。

我们从 Yelp.com 收集了来自25,369名消费者对 46,613 家餐厅的 1,206,587 条评论。利用这些数据, 我们通过结合消费者身份和在线消费者评论生成了消费者偏好向量。然后, 我们结合了餐厅身份和食品分类来生成餐厅信息向量。最后, 考虑到餐厅属性向量, 本研究调查了消费者偏好和餐厅信息向量之间的非线性交互关系。

我们发现, 所提出的推荐模型相比于之前最先进的模型表现出更优秀的性能, 这表明结合消费者和餐厅的各种文本信息是预测消费者喜好的基本因素。

据我们所知, 这是第一项利用来自真实在线餐厅平台的文本信息开发个性化餐厅推荐模型的研究。本研究还提出了胜过最先进模型的深度学习机制。本研究的结果可以降低探索消费者行为的成本并支持有效的购买决策。

The significance of digital literacy in online social capital accumulation and surviving the contemporary society is widely recognised. Despite that the current generation is regarded as “digital natives”, their levels and nature of digital literacy vary. To generate educational insights, this study investigates the type(s) of digital literacy which are mostly related to the online social capital accumulation, and how one’s socio-economic background affects the connection between digital literacy and online social capital.

A total of 1,747 participants aged 13–30 were invited to take part in a quantitative study. Spearman's rank correlation analysis, hierarchical regression analysis and mediation analyses were performed to investigate the relationship between participants' demographic background, engagement in the online platforms, digital literacy and online social capital.

The results showed that the creative dimension of digital literacy was mostly significantly predictive of online social capital accumulation. Also, education significantly affected the relationship between the creative dimension of digital literacy and online social capital more than demographic background.

Results suggest that education helps enhance digital literacy, offsetting the influence of socio-demographic background. The author examines the implications of how to implement training programmes in youth settings to enhance students' digital literacy and benefit those who are marginalised.

The purpose of this paper is to analyze the life history of the Spanish Generation X over the last five decades.

Considering that the generational cohort concept can be identified from the marketing side as a market segment, this paper proposes to analyze the socio-economic and cultural context that has marked the different life stages of Generation X, and how they have related to brands according to their needs, desires and aspirations.

The results show that the customer journey can be considered a circular concept. The customer’s relationship with the brand can begin in childhood and continue into adulthood, such that the emotional relationship established with the brand as a child influences purchase decisions in adulthood.

Although limited to the analysis of Generation X and its relationship with brands, this paper shows the importance of knowing the socio-economic, legal and cultural context of a generation.

As a business implication, the importance of remarketing is evident as a business strategy that reinforces the emotional connection between the brand and the different generations.

From a social point of view, this paper shows the power of brands as an element of self-expression of the needs, tastes or preferences of individuals is evident.

This paper offers a different and innovative vision of the customer journey, taking into account the individual’s life cycle, and the way in which at each stage of life, he/she relates to brands in a different way.

Multi-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images.

The authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance.

The authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation.

Most of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image.

Brand reputation (BR) is one of the most important factors that affect the consumer–brand relationship and give businesses a competitive advantage. Businesses with a strong BR can increase their market shares and product market prices, in addition to gaining a competitive advantage. In order for businesses to have these advantages, they need to know and analyze their consumers. This study aimed to develop an alternative analysis method by using classification algorithms and regression analysis to measure and evaluate the effect of consumers' BR perceptions on their willingness to pay premium prices (WPP).

The research data were collected from 483 participants by the online survey method due to the COVID-19 pandemic. The data were first analyzed with regression analysis, and the effect of BR on WPP was found to be significant. Then, using artificial intelligence (AI) methods that were not used in previous studies, consumers' perceptions of BR and WPP were clustered and classified.

The results revealed the highest and lowest customer groups with BR and WPP and empirically demonstrated that highly accurate practical classification models can be applied to determine strategies in line with these findings.

The model proposed in this study offers an integrated approach by using AI and regression analysis together and tries to fill the gap in the literature in this field. Therefore, the novelty of this study is to quantitatively reveal and evaluate the relationship between BR and WPP by using AI classification algorithms and regression analysis together.

This research examined the effects of information literacy on lifelong learning, creativity, and work performance among journalists in Pakistan.

Survey research design was applied to conduct this research. The participants were recruited through a stratified convenient sampling process from the press clubs of four provinces (e.g. Punjab, Sindh, Khyber Pakhtunkhwa, and Baluchistan) and the federal capital Islamabad with the consent of relevant authorities for data collection. An online questionnaire was distributed among these journalists and a total of 1,089 responses were received. The data were analyzed by applying descriptive and inferential statistics in SPSS.

The results revealed that these journalists perceived themselves as information literate. The information literacy (IL) skills of journalists appeared to have a direct and positive effect on their lifelong learning, creativity, and work performance. In other words, the lifelong learning, creativity, and work performance of journalists increase as their levels of IL skills increase.

These results generated useful insights for academicians and organizations about the importance of IL in the workplace and its influence on organizational effectiveness and performance in gaining a sustainable competitive advantage. This knowledge might be crucial for media employers to initiate training programs for journalists to impart IL education.

This research would be a worthwhile contribution to the existing research on workplace IL, particularly in the context of journalists' workplace as no such comprehensive study using these variables appeared so far.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2022-0345.

Despite the significant growth in Islamic economies and the increasing number of Muslim youths inclining digital services, empirical-based research addressing the adoption of digital Islamic services is still limited. ZakaTech is a new phrase that has recently emerged as a modern term describing novel technologies adopted by zakat institutions; yet, it has been largely neglected in the literature. Therefore, this study aims to provide an integrated model that scrutinizes the determinants of acceptance and use of technology (UTAUT) of ZakaTech, combined with social cognitive theory (SCT), in the midst of the COVID-19 crisis where social distancing is the norm in conducting economic activities.

Based on cross-national evidence from two Muslim-majority countries, a total of 1,006 valid responses were collected from zakat payer users in Saudi Arabia and Egypt using a Web-based survey. To validate the research model and draw significant insights, SmartPLS structural equation modeling was used.

By analyzing both Saudi and Egyptian samples, the authors found that all UTAUT constructs are statistically significant, except for effort expectancy in Egypt. The effects of self-efficacy and social isolation on ZakaTech adoption are supported across both countries. Trust in technology reduces users’ inherent risks and increases their likelihood of adopting ZakaTech services in Saudi Arabia, while this is not the case in Egypt. However, the study revealed that trust in electronic-zakat systems (EZSs) is a vital predictor for mitigating perceived risk among Egyptian users of ZakaTech, but it is not the case in Saudi Arabia. Moreover, significant gender differences were found between males and females in the adoption of such digital services in both countries, particularly regarding self-efficacy, trust in EZSs, social isolation and social influence.

The results provide meaningful insights for policymakers to find ways to develop strategies to escalate the adoption of technology in zakat administration and also to create awareness among the users of ZakaTech in a gender-balanced manner that will include zakat payers and recipients as well in the hope that the digital gender divide will be bridged. Bridging the digital gender divide in this regard is imperative for sustainable inclusive development of zakat. Further, strategies need to be developed to provide incentives to zakat authorities and zakat organizations that adopt technology in managing zakat. This is important to escalate the process of fusing technology with zakat, which is an important social finance tool to eradicate poverty in the world.

This research serves as a building block for literature by empirically testing an integrated model of UTAUT-SCT within a modern and unique related context like ZakaTech. Also, it adds value by testing gender disparities in ZakaTech adoption among Muslims.

This paper aims to explore the impact of a digital growth mindset on the academic performance of business students in China as well as the role of gender in this relationship. The study provides feasible ways to foster such a mindset to ensure quality in business education.

The paper employs a survey to examine the associations among digital growth mindset, gender and the academic performance of business students in China within the context of online learning. The authors collected data from 533 students who were enrolled in two online business courses at a Chinese university. The data were paired and analyzed through regression analysis.

The empirical results show that digital growth mindset has a positive influence on the academic performance of business students in China. The relationship is stronger for male business students than for their female counterparts.

This study is one of the first to examine the concept of digital growth mindset and its significance in Chinese business education. Through the analysis of paired data on digital growth mindset and academic performance, this study makes theoretical contributions to the literature on growth mindset, gender differences and academic success. Additionally, it has practical implications for quality assurance in business education in developing countries by offering feasible approaches to cultivate a growth mindset among students.