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The purpose of this paper is to examine how sub-criteria of the European Foundation for Quality Management (EFQM) model is structurally connected and influence each other. This paper also tries to find the underpinning logics in the EFQM model.

The study uses the empirical methodology based on assessment scores of 58 different organizations to gauge the underlying structure, develop the construct and establish interlinkages among the various sub-criteria in the EFQM model. Statistical analysis is used to find the impact on results and cross influencing of criteria at the sub-criteria level. The factorial analysis is carried out using the Doe technique to create factorial plots for result categories (customer results, people results, society results and business results). The approach is to unravel (1) the role played by each sub-criterion of the model, (2) the effects of sub-criteria on the results of the EFQM model and (3) the influence of sub-criteria on the managerial aspects of the model in an organizational context.

The EFQM sub-criteria are categorised as promoters, proponents, defenders or detractors based on their impact on the results and cross-influence on each other. The study unfolded seven sub-criteria positively impacting the results and one sub-criterion negatively impacting the results if not handled properly. Out of 32 sub-criteria, nine sub-criteria are influencing more than six other sub-criteria.

The paper investigates, for the first time: (1) the role played by each sub-criteria of the model; (2) the relationships that are produced between these sub-criteria on the EFQM results and (3) identify how such sub-criteria would influence the managerial aspects of the model in an organizational context. This research develops underlying logics in the EFQM model using Doe factorial methods for overcoming the multi-collinearity.

AN analysis of creep deformations in rotating disks of constant and variable thicknesses by experimental and theoretical methods, has been presented by Wahl and his collaborators in a series of papers. Wahl first based his theory on the Mises Criterion and found that it gave creep deformations which were too low compared to the test‐values. Later he carried out his investigations using, in the first instance, Maximum Shear Theory with the flow rule associated with Mises Criterion and in the second, the Tresca Criterion with its associated flow rule, assuming zero radial‐creep‐strain rate. He has concluded that better agreement between the test and theoretical values could be obtained in the later cases.

Purpose – In order to explore how gender and sexual politics are played out in everyday practice within both the extreme right and jihadi-Salafist movements online, this chapter analyzes the content of two women’s only forums: The Women’s Forum on Stormfront.org and Women Dawah, a Turkish language pro-IS group chat on Telegram.

Methodology – The Women’s Forum and the Women Dawah data sets were analyzed using structural topic modeling to uncover the differences and similarities in salient topics between White Nationalist and Islamic State women-only forums.

Findings – The cross-ideological and multi-linguistic thematic analysis suggests that the safety of online spaces enables women to be more active, and serves digital support network for like-minding individuals. It also highlights that religion and ideology, whilst interwoven throughout posts on both platforms, they were more explicitly discussed within Women Dawah data.

Originality/Value – This research uses a unique data set which was collected over one year to conduct a cross-ideological and multi-linguistic thematic analysis, a relatively uncommon approach.

Foundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and parts is vital for the end product quality. The complexity in foundry operations increases with the complexity in designs, patterns and geometry and the quality parameters of the casting processes need to be monitored, evaluated and controlled to achieve expected quality levels.

The literature addresses quality improvement in foundry industry primarily focusing on surface roughness, mechanical properties, dimensional accuracy and defects in the cast parts and components which are often affected by numerous process variables. Primary data are collected from the experts working in sand and investment casting processes. The authors perform machine learning analysis of the data to model the quality parameters with appropriate process variables. Further, cluster analysis using k-means clustering method is performed to develop clusters of correlated process variables for sand and investment casting processes.

The authors identified primary process variables determining each quality parameter using machine learning approach. Quality parameters such as surface roughness, defects, mechanical properties and dimensional accuracy are represented by the identified sand-casting process variables accurately up to 83%, 83%, 100% and 83% and are represented by the identified investment-casting process variables accurately up to 100%, 67%, 67% and 100% respectively. Moreover, the prioritization of process variables in influencing the quality parameters is established which further helps the practitioners to monitor and control them within acceptable levels. Further the clusters of process variables help in analyzing their combined effect on quality parameters of casting products.

This study identified potential process variables and collected data from experts, researchers and practitioners on the effect of these on the quality aspects of cast products. While most of the previous studies focus on a very limited process variables for enhancing the quality characteristics of cast parts and components, this study represents each quality parameter as the function of influencing process variables which will enable the quality managers in Indian foundries to maintain capability and stability of casting processes. The models hence developed for both sand and investment casting for each quality parameter are validated with real life applications. Such studies are scarcely reported in the literature.

The purpose of this paper is to use three-dimensional printers to fabricate functionally graded porous (FGP) beams, carry out impact loading and provide innovative equations.

In this study, using the three-dimensional printer, polylactic acid beams containing internal holes with distribution two-dimensional FGP beams of type X are fabricated. A simple tensile test and also a drop-weight impact test are performed to determine the mechanical and low-velocity impact properties of the beams. Inverse-tangent shear–strain function theory, together with the energy method, is used to derive the motion equations of low-velocity impact on the beam. A range of impact energies from 1.5 to 12 J and holes with diameters of 2 and 3 mm are used in the drop-weight impact testing machine.

As the hole diameter enlarges, there is a decrease in contact force and energy absorption levels. The larger hole diameter results in an extended contact duration, increased maximum impactor displacement, and a higher remaining impactor velocity. The innovative linear equations with a maximum error of 5.18% and nonlinear equations with a maximum error of 0.01% provide an acceptable result for the maximum contact force. For predicting the absorbed energy, the linear equation with a maximum error of 90.69% does provide an unacceptable result, and the nonlinear equation with a maximum error of 4.51% provides a reasonable result.

The originality of this research is in fabricating FGP beams with a three-dimensional printer to conduct impact tests and generate creative equations.

This paper comes in the context of the European International Business Academy (EIBA) History project, and the long period analysed was broken down in six time windows (1975–1981, 1982–1988, 1989–1995, 1996–2002, 2003–2012; and 2013–2020), in line with the periodisation followed in writing such history. The main purpose was to identify the key themes of the papers presented at EIBA conferences between 1975 and 2020.

The analysis was carried out drawing on topic modelling, a machine-learning statistical solution that is capable of processing large volumes of text data.

A set of 5,296 Competitive and Workshop papers was processed with the use of topic modelling. The method enabled to identify 24 underlying research topics. These were then grouped into nine higher-level categories. The results show a consistent growth in the number of papers presented, especially in the periods going from 1989–2012. This is a sign of an increasing attractiveness, openness and attendance in EIBA’s annual conferences. Overall, the topics with the highest probabilities were topic 22 (Measuring and Assessing IB performance), followed ex-aequo by topics 13 (Evolutionary Approaches, Matrix Structures and Managerial Challenges) and 20 (Comparative Management Education and Behaviour) and then by topic 4 (Born Globals and International New Ventures). A key finding was the change in methodological approaches over time, with a significant increase in the use of sound quantitative and qualitative methods, instead of broad narratives mostly based on descriptive statistics.

Latent Dirichlet Allocation (LDA), as a quantitative approach to analyse text data, has some limitations. LDA, along with other distributional models, may identify degrees of semantic relations between words, but is not able by itself to specify the kind of relation, entailing a possible loss of contextual information which might have been able to further assist in the study. Another limitation stems from the use of very old paper proceedings, whose quality was sometimes low, making reading difficult.

This research provides a longitudinal perspective of the evolution of the key research topics in international business over about 45 years. Its findings are very important for all those who are interested on the evolution of the IB field.

The research provides an interesting perspective of the development of a scientific field as well as of a scientific community.

The main contributions of this research are fourfold. Firstly, to the best of the authors' knowledge, it provides the most complete longitudinal analysis about the evolution of IB research topics published so far. Secondly, it extracts relevant information about the evolution of the IB research issues addressed at EIBA’s annual conferences, enabling a 46-year longitudinal perspective on research interests as they emerged. Thirdly, it provides a successful application of topic modelling for the analysis of large volumes of textual data. Fourthly, it addresses the entirety of the text documents, as opposed to specific sections or keywords only, ensuring increased analytical depth.

This study investigates consumer perspectives on cultural appropriation in the fashion industry by analysing user-generated content on Douyin.

Using text-mining techniques, including topic modeling and sentiment analysis, 6,584 comments related to the Dior midi-length skirt controversy were analyzed to identify key themes and sentiments.

The study reveals that cultural appropriation elicits polarized responses. Positive themes, such as cultural appreciation, innovation and global exposure, highlight consumer approval of respectful and creative integration of cultural elements. Negative themes, including cultural theft, historical inaccuracy and commercial exploitation, underscore concerns about commodification and misuse of cultural symbols.

Fashion brands should prioritize cultural sensitivity by collaborating with cultural communities, ensuring historical accuracy and promoting ethical engagement with cultural symbols. Proactively addressing these issues through real-time consumer feedback can help mitigate backlash, foster trust and enhance brand equity.

This study advances the discourse on cultural appropriation by leveraging real-time user-generated content to provide actionable insights for ethical practices in the fashion industry.

The aim of this research is to gain insight into the emotional impact and cognitive evaluations that these applications have on users from their own perspective.

Sentiment analysis methodology is used to analyze users’ comments and ratings, assess perceived benefits and determine the emotional impact these applications have on them.

The results suggest a benefit for happiness scholars, professionals, users and organizations focused on employee well-being and provide valuable insights into the evolving landscape of happiness applications. While the study relies on publicly available user data, it complements, but does not replace, market research or product evaluation conducted by app developers and companies.

Among the myriad channels disseminating the assumption that the good life is attainable through personal means and psychological know-how, smartphone applications have emerged as accessible, cost-effective and attractive tools to help users become happier and alleviate feelings of distress and discomfort.

This study aims to identify the impact of affective components on behavioral intentions applying electronic word of mouth (eWOM) and is based on the “cognitive–affective–conative” model. EWOM allows researchers to get new insights about consumers’ behavior and explores new patterns of consumers’ decision-making processes.

This study is based on the theory of planned behavior (Ajzen, 1991), doubled with “cognitive–affective–conative” model (Oliver, 2014). This study applies structural topic modeling to examine the impact of satisfaction dimensions from all the Tripadvisor reviews on consumer behavioral intentions. The research sample covers all restaurants located in St. Petersburg (n = 10,424) and all consumers’ reviews (n = 286,642).

In this study, the dimensions of the affective component were identified. The results demonstrate that dimensions of the affective component (food quality, service quality, atmosphere and cost fairness) affect behavioral intention (willingness to share positive emotions). In total, 20 topics, forming these dimensions, have been indicated. Consumers tend to pay more attention toward food quality and restaurant staffs’ work when they are willing to share positive emotions and tend to point out auxiliary service when they have less willingness to share positive emotions. Random restaurant visits tend to increase the willingness to share positive emotions.

Research originality lies in a new methodological approach which is based on text mining techniques. To the best of the authors’ knowledge, this study is the first attempt to examine consumer behavior through the lens of the “cognitive–affective–conative” model based on eWOM and covers all businesses in the specific economic sphere. This has allowed the researchers to reveal new dimensions of consumer behavior and brought more insights into the consumers’ decision-making process.

This study aims to offer an overview of the models of clinical care of the patients with dual disorders in India.

All the members of the Dual Diagnosis India Network (DDIN) who shared the clinical care delivery at their center were invited to share the details of their model. In addition, an invite was also sent to those members who could not attend the online session but were interested in contributing the required information about their model. The information shared by the respondents was collated. The different models were then categorized based on their features.

Following the categorization of the clinical care services organization across different settings, five different models emerged. These were specialized dual diagnosis clinic; services for dual disorders offered as substance use disorder (SUD) treatment services within general psychiatry care; services for dual disorders in general psychiatry care; services for dual disorders offered as SUD treatment services separated from general psychiatry care; and services for dual disorders offered in general psychiatry services combined with exclusive SUD treatment services.

Currently, there is limited literature on models of dual disorders from the low- and middle-income countries. The authors believe that the documentation of these models from India shall be of help while setting up services for dual disorders in other health-care settings. This study can be a valuable resource for making informed choices while setting up new services.