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This paper aims to examine the impact of using visual grammar on learning participle adjectives by EFL (English as a Foreign Language) learners.

The study follows an experimental design in which two groups participated in the study. The experimental group used visual grammar tools in learning participle adjectives. The control group was taught the participle adjectives in a traditional way. A pre–post test was designed and presented to the participants in the two groups.

The results showed that the experimental group made statistically significant improvements in their performance in using participle adjectives due to the use of visual grammar tools.

The current study is only limited to the effect of visual images on a particular grammatical issue, that is participle adjectives. Besides, the study does not include the gender variable; there may be variation in the results depending on the variable of gender.

The present study can provide language instructors with some guidelines on how to incorporate visual grammar applications in teaching grammar aspects. Learners can also be encouraged to have a better understanding of English grammar, using the different connotations of visual images.

Using visual images in teaching grammar will increase the learners' ability to think beyond their classroom environment. They can use this experience whenever they face visual images in different societal activities.

This paper is one of the initial attempts to investigate the effect of using visual grammar on learning participle adjectives.

Customization by segmenting within human–computer interaction is an emerging phenomenon. Appealing graphical elements that cater to user needs are considered progressively important, as the way a graphic is visually represented can greatly contribute to the interaction. However, aesthetic perceptions are subjective and may differ by target group. Understanding variations in user perceptions may aid in design processes; therefore, we set out to investigate the effects of demographic differences relating to perceptions of graphical user interface (GUI) element (i.e. game app icon) aesthetics.

The authors employed a vignette experiment with random participant (n = 513) assignment to evaluate 4 icons from a total of 68 pre-selected mobile game icons using semantic differential scales. This resulted in a total of 2052 individual icon evaluations. Regression analyses were performed with the effects of age, gender and time using graphical user interfaces (i.e. app stores) and the interactions of these variables relating to perceptions of GUI element aesthetics.

The results indicate that, overall, demographic factors have relatively little effect on how icons are perceived. Significant relations suggest that experienced users, younger audiences and women are more critical in their perception of aesthetic excellence, and that perceptions change for younger women. The implications of the findings are discussed via adaptive decision-making theory.

In the context of graphical user interface element aesthetics, demographic differences have received minimal attention as moderating variables regardless of their relevance in design and development. Hence, it merits further research.

The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative entities (or places) and perceptions (or aspects) of the users.

The authors used 90,725 Instagram posts and 235,755 Twitter tweets to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers on both social media sites. The authors used several approaches for sentiment classification for English and Spanish texts, including deep learning models.

The best results in a test set were obtained using a bidirectional encoder representations from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were subsequently used to analyze the data sets. It was then possible to identify the most important entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners, travelers and tourism managers so that services could be improved and better marketing strategies formulated.

The authors propose a Spanish-Tourism-BERT model for performing sentiment classification together with a process to find places through hashtags and to reveal the important negative aspects of each place.

The study enables managers and practitioners to implement the Spanish-BERT model with our Spanish Tourism data set that the authors released for adoption in applications to find both positive and negative perceptions.

This study presents a novel approach on how to apply sentiment analysis in the tourism domain. First, the way to evaluate the different existing models and tools is presented; second, a model is trained using BERT (deep learning model); third, an approach of how to identify the acceptance of the places of a destination through hashtags is presented and, finally, the evaluation of why the users express positivity (negativity) through the identification of entities and aspects.

这项工作的主要目的是使用情感分析技术和来自 Twitter 和 Instagram 的数据来分析旅游目的地, 以便找到最具代表性的实体（或地点）和用户的感知（或方面）。

我们使用 90,725 个 Instagram 帖子和 235,755 个 Twitter 推文来分析格拉纳达（西班牙）的旅游业, 以确定旅行者在两个社交媒体网站上提到的重要地点和看法。我们使用了几种方法对英语和西班牙语文本进行情感分类, 包括深度学习模型。

测试集中的最佳结果是使用来自Transformers (BERT) 模型的双向编码器表示 (BERT) 用于西班牙语文本和Tweeteval 用于英语文本, 这些结果随后用于分析我们的数据集。然后可以确定最重要的实体和方面, 这反过来又为研究人员、从业人员、旅行者和旅游管理者提供了有趣的见解, 从而可以改进服务并制定更好的营销策略。

我们提出了一个用于执行情感分类的西班牙旅游 BERT 模型, 以及通过主题标签找到地点并揭示每个地点的重要负面方面的过程。

该研究使管理人员和从业人员能够使用我们发布的西班牙旅游数据集实施西班牙-BERT 模型, 以便在应用程序中采用该数据集, 以找到正面和负面的看法。

本研究提出了一种如何在旅游领域应用情感分析的新方法。首先, 介绍了评估不同现有模型和工具的方法; 其次, 使用 BERT（深度学习模型）训练模型; 第三, 提出了如何通过标签识别目的地地点的接受度的方法, 最后通过实体和方面的识别来评估用户表达积极性（消极性）的原因。

Using Schein’s Descriptive Index (SDI), this paper aims to first examine gender role stereotypes and requisite managerial characteristics among Irish business students over a 10-year period. Then, the paper investigates whether there have been changes in gender role stereotypes during this period and subsequently unpack the reasons behind any changes recorded.

In total, 1,124 students from the same business student population rated men, women and managers in general, using SDI. Data was collected first during the academic year 2008–2009 and again in 2018–2019 to determine stability or change in gender role stereotypes and requisite managerial characteristics. Intraclass correlation coefficients scores were computed to determine the relationship between gender and requisite managerial characteristics and identify differences and similarities between the two samples. To explore the content of gender stereotypes, an examination of the specific descriptive items was conducted by performing a factorial analysis using Duncan’s Multiple Range Test. Finally, the authors adapted the scales developed by Duehr and Bono (2006) to determine whether broad gender stereotypic characteristics with respect to communal and agentic, attributed to men, women and managers, differ by sample.

The overall findings indicate changes in the extent of gender role stereotyping of the managerial role among the male cohorts studied. The subsequent analysis of the descriptive items identified that the change among the male cohort is due to the levels of agency they perceive women to now possess.

The authors contribute to the literature on both gendered and managerial stereotypes by showing changes in the pro-male stereotype of the managerial role and contribute to the existing debate on a shift towards a more androgynous view of leadership.

These findings help understand the content of gender role stereotypes that recent graduates bring with them to their first job post-graduation. The observed changes in the level of agency ascribed to women by their male counterparts could prove to be an important step forward for women’s advancement to managerial positions.

The findings indicate that both male and female cohorts in Sample 2 perceived men and women in general to possess the same levels of communal and agentic traits as their managerial counterparts.

To better understand the reality of living with the diseases and conditions that its drugs and therapies are developed to treat, the Novartis leadership determined a need for more meaningful insights into patients’ lives. They sought to develop a systematic, creative methodology – informed by the psychology of insightful rather than analytical thinking – to properly integrate and deploy the research commissioned into its day-to-day business decision-making. For it is well established that better understanding of the patient reality drives both compliance and adherence “beyond the pill”. The purpose of this paper is to bring the novel methodology of creativity to a wider audience and ensure that many others – notably in patient advocacy organizations – can benefit from this approach.

A core team of Insight and Analytics and Patient Engagement leads from various therapeutic area teams worked in partnership with a psychologist and practitioner in the field of insightful thinking, to develop an effective methodology that could reliably surface and articulate genuine patient insights. This methodology – the i4i Insights Discovery™ process – was developed, piloted, refined and codified in 2020 and implemented across the company in 2021–2022. It uses a combination of convergent and divergent thinking techniques – human rather than artificial intelligence, combining diverse research outputs – to understand patients’ lives better. With enhanced understanding, the insights then shape educational and behavioral strategies to drive adherence and compliance.

At a time of tightening budgets and demands to deliver enhanced impact from research budgets, i4i Insights Discovery™ has enabled Novartis teams to turn existing research outputs into profound and useful understandings of what it means to live with specific diseases and develop evidence-based patient engagement strategies; insight-driven decision-making around the lifecycle of any compound. i4i Insights Discovery™ has been applied across Novartis’s diverse areas of expertise, from heart disease to cancer, from organ transplantation to dermatology, from food allergy to ophthalmology.

The i4i Insights Discovery™ process enables Novartis teams to gain deeper understanding of patients’ lives without the need to commission additional research; to do more with less. These insights enable cross-functional Novartis teams to develop better-informed strategies that better address the needs of patients and their care partners, of health-care professionals and health-care systems. The team creating the process is looking to make the i4i Insights Discovery™ approach a gold standard of insight discovery, both for pharma and health care and in other categories, too.

The i4i Insights Discovery™ process is a practical, novel application of well-established principles in the psychology of insightful thinking to address a clear business imperative. By repurposing and reinterpreting existing research outputs using creative verbal and visual exercises, it delivers a more human and empathetic understanding of the patient reality. It moves teams from “So what?” – this is what the data mean – to “Now what?” – this is what we should do as a result.

To identify the Transcendental Essence of Humanity, the purpose of this paper is to describe in brief what kind of research became possible when the theory of, e.g. autopoiesis, Husserl’s Transcendental Consciousness and the theory of Rhodes and Thame came together to form a “transcendental” interview methodology.

Critical conceptual implications are drawn to form a new research method to explore a de-subjectified inner domain and to search for a possible common essence of humanity.

A Transcendental Emotional Reference was found practically alien to contemporary perspectives. Still, the reference governs the emotional structure of human experience. This different perspective answers basic questions of morality, organization theory and leadership.

The findings of the new research open a new and transparent perspective answering Grey’s question: “What is it to be human?” (Grey, p. 47, 2014.) A perspective shedding new light on the humanities. A research limitation is the number of respondents. Still, being transcendental the findings are theoretically valid for all.

The paper is based on a unique research enabling 32+ (ongoing research) respondents to explore their own and universally shared Transcendental domain.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

This paper is a corpus-driven study of written electronic texts, particularly the “About Us” sections in the university websites of 41 members of the Asian Association of Open Universities (AAOU). This inquiry is important because it will provide an insight as to how AAOU members describe and portray themselves in the World Wide Web, a platform which is highly utilized in the field of distance education. This will also lead to the understanding of conventional knowledge among AAOU members during the period of study. The paper aims to discuss these issues.

Guided by Swales’ (1990) seminal work on genre analysis, the researchers conducted a three-part genre analysis which involved identification and analysis of moves, keywords, and concordance lines.

Results of the keyword analysis were conducted using the AntConc application. Anthony (2017) found that the top 100 keywords with positive keyness exemplified inherent characteristics of open and distance learning institutions as well as characteristics common to higher education institutions and their thrusts. The analysis also found that the use of adjectives and verbs with positive denotations is common in the “About Us” sections. Concordance for several keywords related to the overarching theme of the AAOU 2017 Conference then revealed that the AAOU members are active in the discourse about accessibility, assessment, and quality, while there is not much discourse on openness, inclusivity, and justice. Meanwhile, subscribing to equality and equity could still be a point of discussion among the AAOU members as the concordance analysis revealed more discourse on equality than equity as a goal and principle. Overall, results of the genre analysis resonated with previous studies of the academic genre as the “About Us” sections are promotional in both authoritarian and inclusive ways.

The inquiry will provide the members of the AAOU with an overview of their common communicative purposes, overused or underused keywords, and their usage of these words which they may opt to work on in the future.

This study aims to explore how corporate discourses enact legitimation strategies aimed at repairing pragmatic, moral and cognitive legitimacy types (Suchman, 1995) after a scandal involving sustainability, namely, the Volkswagen’s 2015 diesel scandal.

By drawing on the discursive nature of legitimacy, this study conducts a critical discourse analysis to identify how the scandal is depicted and which semantic, grammatical and lexical features characterise discourses. It then relates discourses and their features to legitimation strategies that help repair diverse types of legitimacy.

To repair pragmatic legitimacy, discourses on a few actors and processes enact strategies of creating monitors and avoiding panic. Such discourses include grammatical features only. Discourses on the event, actors, processes and topics of apology, trust and integrity aim to repair moral legitimacy. Enriched with grammatical and lexical features, they mobilise disassociation, excuse, justify and restructure strategies. Discourses on the event, actors, processes and topics of corporate qualities, history and future strategy help repair cognitive legitimacy by enacting an avoiding panic strategy. Grammatical, lexical and semantic features characterise such discourses.

The study reveals the potentials of critical discourse analysis to bring out from texts practical modes of communicating, and specifically those discourses and features of discourses that serve legitimacy purposes.

This study offers insights into the connection among discourses, relegitimation strategies and legitimacy types by combining the discursive nature of legitimacy with critical discourse analysis. It also contributes to the growing literature on how organisations face the legitimacy challenges raised by scandals involving sustainability.

Wine packaged tours as a specific aspect of wine tourism have so far been neglected in research, for this reason, the purpose of this study is to study the key elements for the success of the wine tour in Tuscany (Italy), evaluating the points of strength and weakness.

The study combines approaches of text mining, sentiment analysis and natural language processing, drawing on data from the TripAdvisor platform, obtaining through an automatic procedure 9,616 reviews from 600 tours in the years 2010–2020.

The authors identified six elements of successful wine tours expressed by research subjects: tour guide; logistical aspects; the quality of the wine; the quality of the food; complementary tourist and recreational activities; the landscape and historic villages. The key strength associated with success was the integration of the leading wine product with food, landscape and historic villages, while the main criticisms were concerned with the organization and planning of the tour. Furthermore, the tour guide also plays a fundamental role in consumer satisfaction.

The limitations of the method were linked to the origin of the data used. The main one is that TripAdvisor does not allow you to have social and personal information about the tourist who wrote the review; therefore, the methods are substantially complementary to the traditional survey through questionnaires.

The proposed model can be used both by professionals to improve the quality of their products and by policymakers to promote the territorial development of quality wine-growing areas.

The proposed model can be useful for policymakers to promote the territorial development of quality wine-growing areas.

The methodology we tested is easily transferable to many countries and to the authors’ knowledge, for the first time attempts to combine multidimensional scaling, sentiment analysis and natural language processing approaches.