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This paper constitutes the first academic study to be made of Google Discover as applied to online journalism.

This paper constitutes the first academic study to be made of Google Discover as applied to online journalism. The study involved conducting 61 semi-structured interviews with experts that are representative of a range of different professional profiles within the fields of journalism and search engine positioning (SEO) in Brazil, Spain and Greece. Based on the data collected, the authors created five semantic categories and compared the experts' perceptions in order to detect common response patterns.

This study results confirm the existence of different degrees of convergence and divergence in the opinions expressed in these three countries regarding the main dimensions of Google Discover, including specific strategies using the feed, its impact on web traffic, its impact on both quality and sensationalist content and on the degree of responsibility shown by the digital media in its use. The authors are also able to propose a set of best practices that journalists and digital media in-house web visibility teams should take into account to increase their probability of appearing in Google Discover. To this end, the authors consider strategies in the following areas of application: topics, different aspects of publication, elements of user experience, strategic analysis and diffusion and marketing.

Although research exists on the application of SEO to different areas, there have not, to date, been any studies examining Google Discover.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2022-0574

Digital entrepreneurship is a highly topical, sub-specialist and contemporary branch of entrepreneurship which is providing ever-increasing range of platforms for pursuing entrepreneurship-related career options. While the academic discourses in this area are increasingly growing, there is an equal level of scant attention paid to inquire how proclivity toward this career choice is developed and influenced. In the present study, this gap has been addressed by identifying major antecedents of digital entrepreneurship intentions under the aegis of capital theory.

Using the survey data from 287 respondents, structural equation modeling was utilized to explore the association of four antecedents: digital entrepreneurial competence, innovative cognition, social media adroitness and digital entrepreneurship role models on the goal intentions. Subsequently the association between goal intention and implementation intention to start a digital venture was also examined.

The analysis confirms the significant impact of the identified antecedents on the digital entrepreneurial volition of potential entrepreneurs. Our results demonstrate that innovativeness has the greatest impact on the goal intentions to create new digital ventures followed by the presence of role models. Digital competence is also a significant contributor in enhancing the propensity to start digital ventures while being digitally adroit has the least impact on such inclination. Lastly, our study provides empirical evidence to the linkage between goal and implementation intentions.

This paper informs practice on entrepreneurship education especially the role of skill-based education programs to enhance the information technology–related knowledge of students and incubation support for hands-on-training on the various dimensions of digital ventures. At policy-level institutions providing entrepreneurial education can design special tasks and learning activities that are focused on acquainting students with design thinking perspectives and lean start-up approaches.

The massive expansion of digital platform has been responsible for the widespread progressive engagement created amongst learners and educators. The practice of requiring student feedback on online learning services ensures that teacher education continues to advance its strategic approach to online learning. This paper aims to examine the level of accessibility and adaptability of digital technology with particular focus on Malaysia, by elaborating the value of superior learning service and practical adaptability of online learning during the pandemic era.

This study was conducted using qualitative approach of data collection, namely via structured interview. The listed respondents included 30 higher learners who participated in the study by providing feedback on the issues encountered during the research process.

The findings revealed that the strategic enhancement of digital accessibility continued with digital adaptability to sources of learning services would contribute to advancing achievement of digital learning pathway.

Increasing accessibility to digital platforms in digital learning system can help to shape the digital environment. Digital expansion can create unlimited boundaries for online knowledge acquisition.

The social implication refers to acquiring the abilities developed through online engagement with peers by actualising and exploring information together with continuous inter-connectedness of sharing pathway in online platform. The instructor would need to give a proportional gateway to make learners experience the digital environment for future education.

This study aims to assess the value of developing accessibility of digital technology for students' online learning services during the pandemic and beyond. A well-structured plan would enable digital learning capabilities and mutual accessibility amongst learners. This can allow digital abilities to be transformed into collaborative teamwork amongst learners.

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

This study aims to investigate the effectiveness of two types of social media videos used for destination image development: induced/commercial-oriented content and organic content (where content is made without commercial interest, such as vlogs classified as user-generated content).

Experimental research using “Emotive EEG” (electroencephalogram) in a controlled environment was conducted with 30 participants (20 males, 10 females), age range 18 to 26. Emotive EEG recording was performed while the participants watched both types of video clips. Test results for both groups indicate that induced content is preferred over organic content.

This study opens up future research avenues where neuromarketing’s “Marketer Friendly” EEG equipment can be applied to the customer selection process.

Marketing analysts can gauge the interest and response of customers on different types of social media video content for destination marketing based on the findings of this study.

Knowledge transfer which refers to the communication of knowledge from a source so that it is learned and applied by a recipient has long been a challenge for knowledge management. The purpose of this study is to understand influencing factors of transactive memory system (TMS) and knowledge transfer.

Drawing on the theories of communication visibility, social distance and flow, this study develops a research model. Then, data are collected from users of the social media mobile App. Partial least squares-structural equation modeling (PLS-SEM) is employed to analyze data.

TMS is a valid second-order construct in the social media mobile app context, which is more reflected by credibility. Meanwhile, communication visibility and social distance each have positive effects on TMS which further has a positive effect on knowledge transfer. Flow has a positive effect on knowledge transfer.

Developers of the mobile App should carefully consider the role of information and communication technology (ICT) in supporting TMS and knowledge transfer. They should consider recommendation algorithm so that the benefit of communication visibility can be retained. They should design the feature to classify users based on similarity so as to stimulate users' feeling of close social distance. They should keep on improving features based on users' holistic experience.

This study incorporates the perspectives of communication visibility, social distance and flow to understand TMS and knowledge transfer, presenting a new lens for research.

This paper aims to discuss the suitability of topic modeling as a review method, identifies and compares the machine learning (ML) research trends in five primary business organization verticals.

This study presents a review framework of published research about adopting ML techniques in a business organization context. It identifies research trends and issues using topic modeling through the Latent Dirichlet allocation technique in conjunction with other text analysis techniques in five primary business verticals – human resources (HR), marketing, operations, strategy and finance.

The results identify that the ML adoption is maximum in the marketing domain and minimum in the HR domain. The operations domain witnesses the application of ML to the maximum number of distinct research areas. The results also help to identify the potential areas of ML applications in future.

This paper contributes to the existing literature by finding trends of ML applications in the business domain through the review of published research. Although there is a growth of research publications in ML in the business domain, literature review papers are scarce. Therefore, the endeavor of this study is to do a thorough review of the current status of ML applications in business by analyzing research articles published in the past ten years in various journals.

The purpose of this study is to examine the effects of banking innovations (INNs) on customer experience (EXP), satisfaction (SAT) and loyalty (LOY).

The author evaluated the data using a structural equation method-artificial neural network (SEM-ANN) method. The author’s results show the presence of relationship between INN, EXP, SAT and LOY. In this study, the node layers of ANNs add an input layer, hidden layers and an output layer. Each “node” acts as an artificial neuron that communicates with others. The ANN model takes the variables from the SEM analysis as input neurons.

The author observed the significant effects between INN, EXP, SAT and LOY using the normalised importance generated by the multilayer perceptron used in the feed-forward back propagation of the ANN methodology. In this study, the ANN model can predict LOY through service innovation, with a forecast accuracy of 77.6%.

By applying neural network modelling, this research helps us understand how service innovation affects customer behaviour. For the first time, the author examined service innovations' direct and indirect impact on loyalty through EXP and SAT. The author made a significant conceptual contribution by using a non-compensatory model of ANNs to circumvent the limitations of linear models.

The professional experiences of teachers of languages and cultures, along with the learning experiences of their students, are embedded in educational contexts, which themselves are informed, and constrained, by national language policies. This study aims to explore 51 English-as-a-foreign-language (EFL) secondary teachers’ perceptions of Web-based technology use to enhance students’ cultural awareness in Chile. Specifically, the study investigated teachers’ use of Web-based resources for cultural awareness, culture content and technology-based tasks, as well as perceived challenges in implementing technology-enhanced language and culture learning.

The study adopted a mixed-method research design combining online questionnaires and interviews as data collection tools. Results were analyzed through the use of descriptive statistics and content analysis.

The teachers in this study emphasized reflection in their classrooms but did not take a critical approach. Their approach to culture was limited to a “country-specific” view, and technology-enhanced activities accentuated differences rather than promoting meaningful intercultural exchange. Challenges to the successful implementation of technology-enhanced language and culture learning included a somewhat out-of-date theoretical approach to intercultural learning in the national curriculum, a nationwide approach to professional development that lacks a focus on critical reflection and inadequate support for effective use of technologies in schools.

The study highlights the importance of periodically revising a country’s EFL language policies, communication methods, support mechanisms and implementation factors to ensure classroom integration of language, culture and technology education.

This paper explores the tension between macro-level national policy and teachers’ perspectives on their classroom practice, including the contextualized limitations of implementing national policy at the micro level.

Acknowledging previous scholarly focus on functional attributes in understanding technology acceptance behaviour, the current study aims to offer a novel perspective by integrating eight different dimensions of utilitarian and hedonic attributes to examine their influence in delivering a holistic web-based retail shopping experience.

The research model was tested and validated through data collected from 370 online shoppers across both hedonic and utilitarian product ranges. Hypotheses were tested using covariance-based structural equation modelling with multi-group analysis to examine the moderation effect.

The findings strongly support the model confirming eight new utilitarian and hedonic dimensions that influence web-based retail shopping behaviour. The findings also confirm that hedonic attributes remain important even for utilitarian product purchasing.

The key managerial implication is the demonstrated need to balance utilitarian and hedonic attributes in web-based retail platforms, where previously, there has been an overemphasis on functional features. Web-based retailers should consider the optimal blend of utilitarian (e.g. information quality) and hedonic (e.g. aesthetic) attributes in the design of a retail shopping site, irrespective of the product category.

This study integrates multiple dimensions of utilitarian and hedonic attributes into a single model and highlights the interplay of these attributes, thus extending the technology acceptance model. This paper also advances scholarship through its identification of attribute impact across different product categories.