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This study aims to investigate the favourable conditions that influence transgenerational value transmission (TVT), value acceptance and value similarity between generations in indigenous African business-owning families.

This study adopts a multiple case study design and draws on semi-structured face-to-face interviews to collect data from participants in seven indigenous Black business-owning families located in South Africa. The software ATLAS.ti was utilised to manage the data and reflexive thematic analysis was undertaken.

The analysis reveal four themes describing how transmission factors facilitate favourable conditions for successful TVT in IBSA business-owning families, namely, authoritarian parenting, a loving and connected family relational climate, the continuous reinforcement of autonomy during childhood development and family authenticity in the face of societies dominant values climate. Furthermore, value similarity is perceived to exist among the different family generations in the business-owning families.

This study is among the first to adopt the value acquisition model to empirically examine successful TVT and examine the extent of value similarity or dissimilarity, using the business-owning family as the unit of analysis. Novel contributions to family business literature and practices are proposing a model for TVT in an African context and studying relationships from a business-owning family perspective. The model for TVT could be used to socialise the NextGen members into value sets and behaviours that help business-owning families preserve their entrepreneurial legacy and family business longevity.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.

First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.

The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.

The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.

Numerous studies have shown that minority workers are disadvantaged in the labour market due to stereotypes and discrimination. However, published research on résumé screening has overlooked the effects of multiple social categorisations pertaining to candidates' gender, education and origin. This study addresses this gap and examines whether the gender, the level of education and the national origin cues mentioned in the résumé affect the perceived employability of candidates.

This study employs an experimental between-subjects factorial design in that 12 résumés varying in gender, education and national origin were rated by 373 Portuguese working adults.

The results documented a gender premium as women were favoured in interpersonal and job skills but not in job suitability, and an education premium, since higher educated candidates were preferred despite their gender and origin. No meaningful interactions for gender × education × national origin were observed, which suggests that ingroup favouritism and outgroup discrimination in résumé screening can be averted.

The findings endorse a multidimensional view of perceived employability by investigating candidates' skills and job suitability from the viewpoint of the decision-makers, which extends our understanding of résumé-screening discrimination. This is critical to prevent hiring discrimination at an earlier career stage, which can increase youth employment and enhance the integration in the labour market of local minorities such as women, inexperienced workers and second-generation immigrants.

This study aims to assess the technological capability of Chinese internet platforms (BAT: Baidu, Alibaba, Tencent) compared to US ones (GAFA: Google, Amazon, Facebook, Apple). More specifically, this study explores Baidu’s technological catching-up process with Google by analyzing their patent textual information.

The authors retrieved 26,383 Google patents and 6,695 Baidu patents from PATSTAT 2019 Spring version. The collected patent documents were vectorized using the Word2Vec model first, and then K-means clustering was applied to visualize the technological space of two firms. Finally, novel indicators were proposed to capture the technological catching-up process between Baidu and Google.

The results show that Baidu follows a trend of US rather than Chinese technology which suggests Baidu is aggressively seeking to catch up with US players in the process of its technological development. At the same time, the impact index of Baidu patents increases over time, reflecting its upgrading of technological competitiveness.

This study proposed a new method to analyze technology mapping and evolution based on patent text information. As both US and China are crucial players in the internet industry, it is vital for policymakers in third countries to understand the technological capacity and competitiveness of both countries to develop strategic partnerships effectively.

This paper argues that cultures with the same climate have similar handicrafts as they have similar cultivation and identical raw materials. This study focuses on how mountainous, coastal and hot regions partaking in similar crafts and cultural heritage use palm leaves and analyses the resulting handicrafts' similarities.

A review of mapping these samples establishes this similarity in the traditional industries of some civilizations' cultural heritage from countries sharing similar climates.

The handwoven crafts using palm leaves were significant patrimonial artifacts in different societies' and communities' cultural heritage. Our studies revealed that climate plays an active role in influencing all aspects of humanity’s life. It affects the construction methods and style, agriculture and lifestyles.

Traditional handwoven palm leaf product models, especially plates and baskets, are studied from South America, Africa, Gulf Countries and Asia.

Additionally, this paper focuses on preserving these treasures as an essential part of interior elements as accessories for most inhabitants of these areas.

Cultural heritage also embraces intangible aspects such as skills passed down through generations within a particular society. The tangible and intangible elements complement each other and contribute to an overall legacy.

Cultural heritage reflects a society’s way of life carried down through the years across lands, items, customs and aesthetic concepts. People are the gatekeepers of society, as they preserve their way of life for future generations to emulate. Tangible artistic and cultural heritage comprises artifacts. It comprises all human evidence and expressions, such as traditional handicrafts, pictures, documents, books and manuscripts.

To elaborate the picture of credibility assessment by examining how participants of online discussion evaluate the informational credibility of conspiracy theories.

Descriptive quantitative analysis and qualitative content analysis of 2,663 posts submitted to seven Reddit threads discussing a conspiracy operation, that is, the damage of the Nord Stream gas pipelines in September 2022. It was examined how the participants of online discussion assess the credibility of information constitutive of conspiracy theories speculating about (1) suspected actors responsible for the damage, (2) their motives and (3) the ways in which the damage was made. The credibility assessments focussed on diverse sources offering information about the above three factors.

The participants assessed the credibility of information by drawing on four main criteria: plausibility of arguments, honesty in argumentation, similarity to one's beliefs and provision of evidence. Most assessments were negative and indicated doubt about the informational believability of conspiracy theories about the damage. Of the information sources referred to in the discussion, the posts submitted by fellow participants, television programmes and statements provided by governmental organizations were judged most critically, due to implausible argumentation and advocacy of biased views.

As the study focuses on a sample of posts dealing with conspiracy theories about a particular event, the findings cannot be generalized to concern the informational credibility conspiracy narratives.

The study pioneers by providing an in-depth analysis of the nature of credibility assessments by focussing on information constitutive of conspiracy theories.

The main purpose of the study is to identify the differences and similarities in the communication between B2B participants in cross-cultural environments.

The research methods used in the study are two-fold: the literature analysis is complemented by primary qualitative research conducted in small- and medium-sized enterprises operating in Poland and doing business internationally. The research was focused on two culturally different markets: China and the United States. In the empirical research, the authors used one of the qualitative methods – Individual Depth Interview (IDI).

General findings showed that the strongest influence of culture was identified among older (+50 years old) business partners. The younger ones are eager to adapt and try to understand others' viewpoints. The research results may be used in creating business communication models in the countries researched for companies that plan to enter both American and Chinese markets.

The results of the study may have useful applied managerial value and be used in cooperation between SMEs' B2B business partners, not only from Poland but also from the whole region of Central and Eastern Europe and the United States and China.

The findings may help to understand and communicate with culturally different social groups such as co-workers, students, teachers, etc.

The research presented in the paper covers the gap in the literature because it relates to some new factors (like cultural heritage, age and type of industry) which determine the effectiveness of personal business communication between partners in the international marketplace.

Mouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation problems with state-of-the-art performances. This study presents a modified Mobile DeepLabV3 based technique with a comprehensive evaluation based on mouth datasets.

This paper presents a novel approach to mouth segmentation by Mobile DeepLabV3 technique with integrating decode and auxiliary heads. Extensive data augmentation, online hard example mining (OHEM) and transfer learning have been applied. CelebAMask-HQ and the mouth dataset from 15 healthy subjects in the department of rehabilitation medicine, Ramathibodi hospital, are used in validation for mouth segmentation performance.

Extensive data augmentation, OHEM and transfer learning had been performed in this study. This technique achieved better performance on CelebAMask-HQ than existing segmentation techniques with a mean Jaccard similarity coefficient (JSC), mean classification accuracy and mean Dice similarity coefficient (DSC) of 0.8640, 93.34% and 0.9267, respectively. This technique also achieved better performance on the mouth dataset with a mean JSC, mean classification accuracy and mean DSC of 0.8834, 94.87% and 0.9367, respectively. The proposed technique achieved inference time usage per image of 48.12 ms.

The modified Mobile DeepLabV3 technique was developed with extensive data augmentation, OHEM and transfer learning. This technique gained better mouth segmentation performance than existing techniques. This makes it suitable for implementation in further lip-reading applications.

This study aims to predict the effect of cultural distance, perceived risk and electronic word of mouth (eWOM) on higher education institutes' students' destination image. In addition, it examines the mediating role of destination image in relation to students' travel intentions.

An online survey was employed to collect data from 200 graduate and postgraduate students. The partial least squares was employed to analyse the hypothesised relationships.

The results of this study found support for the positive effect of cultural distance and eWOM on destination image. Additionally, the mediating effect of destination image was also supported.

This research confirms the vital role of destination image as an antecedent of students' future intention to visit the destination. Moreover, this study contributes to marketing theory by predicting the critical drivers of higher education students' destination image and discussing their applications in the education sector.