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Why small business owners get demotivated? Modeling unwillingness to grow using ISM approach
Purpose: Even after establishing their business successfully, many business owners get demotivated, and it leads to unwillingness to grow. This study aims to propose a comprehensive model that represents interrelationships among various personal factors affecting unwillingness to grow. Design/methodology/approach: The personal factors for unwillingness to grow were identified by extant literature, and expert interviews were conducted to establish the contextual relationships among these factors. The interrelationships among the filtered variables have been done using interpretive structural modeling (ISM) and MICMAC analysis was done to determine the importance of each factor in influencing unwillingness to grow. Findings: In total, 30 personal attributes were identified from previous literature, out of which 15 were selected for the final study. The result identifies 7 variables having a strong impact on unwillingness to grow. These attributes are absence of strong network, lack of vision, lack of proactiveness, reluctance to involve external consultants, absence of/small founding team, lack of ambition and improper attitude. Originality/value: The research attempts to create a bricolage of all the important personal factors affecting unwillingness to grow. Previous researches have used few attributes, but with the help of ISM, a graphical modeling technique, it became possible to draw interrelationship between 15 attributes. Further, with the help of MICMAC, the importance of each attribute was determined. 2024, Emerald Publishing Limited. -
Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data
Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sources. The dataset includes trading data, news and Twitter sentiment, and technical indicators. Sentiment analysis and the Natural Language Toolkit are utilized to extract the qualitative information from social media data. Technical indicators, or quantitative characteristics, are therefore generated from trade data. The technical indicators are fused with the stock sentiment features to predict the future stock market trend. Finally, a machine learning model is employed for upward or downward stock trend predictions. The proposed model in this study incorporates XAI to interpret the results. The presented model is evaluated using five bank stocks, and the results are promising, outperforming other models by reporting a mean accuracy of 90.14%. Additionally, the proposed model is explainable, exposing the rationale behind the classifier and furnishing a complete set of interpretations for the attained outcomes. 2024, American Scientific Publishing Group (ASPG). All rights reserved. -
DISCOURSE OF DISSENT: LANGUAGING RESISTANCE AND CONSCIOUSNESS IN SUBALTERN LITERATURES DALIT AND BLACK
The paper highlights the pivotal role of language in Afro-American and Dalit movements, emphasizing identity affirmation and resistance to dominant aesthetic structures. It examines languages dynamic role in shaping subaltern experiences and fuelling revolutionary movements. While there is some analysis of the significance of literary trends and intellectual current in these parallel movements, a few scholarly inquiriesintegratethelinguisticandstylisticaspectscomprehensively. Thestudyaddresses this critical gap by comparing and contrasting the selected study of these two movements to see their convergences and divergences. We employ the theoretical framework of Subaltern Studies and Distributed Language (DL) to understand socio-political motifs of pre- and post-production of a particular kind of language. The selected poems are closely read and analysed through Critical Discourse Analysis, with close reading as a key technique. It allows for an exploration of the intricate relationship between the linguistic structure, use of lexical items, emotive use of language, connotational significations, and compositional semantics. While selected Black literature poems experimented with internal morpho-syntax and everyday language, Dalit literature bluntly presented harsh facts using multilingualism, a unique Indian linguistic trait, and everyday vocabulary. Copyright 2024 Chandan Kumar, Nivea Thomas K. -
Optical characterization of oxadiazoles analogues doped PMMA film for photonic application
In the present study, newly synthesized nitrobenzene derivatives (PBT and PBF) doped poly(methyl methacrylate) films were prepared using spin coating techniques, and their optical properties were analyzed. The absorption spectra of various weight percentages (0.02%, 0.1%, 0.2%, and 0.3%) of nitrobenzene derivative-doped polymer films were recorded using a UVvisible spectrometer. From the absorption spectra, optical properties such as refractive index, band gap energy, extinction coefficient, and dielectric constant were calculated. The effect of doping on the optical properties of PMMA was investigated, with results revealing normal dispersive behavior from the refractive index and extinction coefficient. Atomic force microscopy and scanning electron microscopy images indicated that the synthesized films have a low degree of roughness and a smooth surface. Additionally, the nonlinear optical properties of the PBF-doped polymer film were investigated, and the ? value was determined to be 7.403cm/W. Overall, the findings suggest that PBF-doped polymer films are promising candidates for photonic applications. Indian Association for the Cultivation of Science 2024. -
WOW Skin Science: strategic adaptation for responsible advertising
Learning outcomes: After completing this case study, students will be able to understand the issues firms, brands and influencers face due to sponsorship disclosure regulation and the impact of self-regulation on firms engaging in influencer marketing, explain the challenges regulators face in ensuring compliance in an emerging market, explain Advertising Standard Council of India (ASCI)s challenges in adopting influencer guidelines from emerged markets and recommend ethical theory (or theories) and strategies to firms engaged in influencer marketing. Case overview/synopsis: This case study centers on Mr Manish Chowdhary, co-founder of WOW Skin Science, who started the beauty and personal care business with his brother Karan Chowdhary in 2015 in Bangalore, India. The company successfully built its brand through influencer marketing but faced challenges after the ASCI implemented new influencer guidelines. On May 31, 2021, he expressed disagreement with ASCI guidelines during an interview with Akansha Nagar from Buzz in Content, particularly the requirement to label every product or service received by influencers as an advertisement. He expressed concern about certain rules, fearing they might harm organic content and reduce viewership and followers. Subsequently, ASCI registered noncompliance cases against the company and communicated with them about complaints regarding influencer guideline violations. In this situation, Manish needed to evaluate his decision on noncompliance with regulation and required an action plan to strategically manage its influencer marketing campaign by incorporating ASCIs guidelines. Overall, this case study highlights the journey of WOW Skin Science and its challenges with self-regulatory authorities over its influencer marketing strategy in an emerging market. Additionally, students can gain insight into the marketing communication ethics of a startup operating in an emerging market by embodying the protagonists role. Complexity academic level: This case study is suitable for postgraduate level students pursuing a Master of Business Administration program. The difficulty level ranges from moderate to complex. It fits well into integrated marketing communication and marketing strategy courses. This case study discusses marketing ethics, advertising and promotion regulation. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS 8: Marketing. 2024, Emerald Publishing Limited. -
Jesuit school teachers opinions on incorporating critical consciousness into digital citizenship education
The contemporary global landscape is undergoing swift transformations accelerated by information and digital technologies, which have given rise to a plethora of innovations that enhance human convenience, novel business models, and emerging new professional paths. However, if these technologies are used improperly, they can become dangerous to humanity. So digital citizenship is a kind of way forward to bring awareness among students and educators to use digital technologies appropriately and responsibly. But in classical digital citizenship issues, such as justice, equity, and accessibility, are not addressed. This study explores Jesuit secondary school teachers opinions on incorporating critical consciousness into digital citizenship and how that affects students attitudes towards building an equitable digital society. The researcher highlights the need to integrate critical consciousness into digital citizenship education through qualitative research study. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
An Examination of the Challenges Associated with Applying Artificial Intelligence Techniques to Specific Management Problems
Artificial intelligence (AI) holds immense promise in revolutionizing management practices across various sectors, offering solutions to complex problems and optimizing decision-making processes. However, the application of AI techniques to management problems is not without its challenges. This examination delves into the multifaceted hurdles encountered when integrating AI into management frameworks, highlighting key obstacles and potential avenues for overcoming them.AI algorithms heavily rely on large volumes of high-quality data for effective training and decision-making. Yet, many management domains grapple with disparate data sources, inconsistencies, and incomplete datasets, hindering the performance and reliability of AI systems. Furthermore, the dynamic nature of management problems poses a significant challenge to AI implementation. Management environments are characterized by evolving trends, uncertainties, and unforeseen disruptions, rendering static AI models inadequate in adapting to changing conditions. Hence, the development of agile AI systems capable of continuous learning and adaptation becomes essential for addressing the dynamic nature of management challenges. 2024, Collegium Basilea. All rights reserved. -
Expanding the Notion of Personal Well-Being During COVID-19 Campus Closure in India: Results from a Mixed-Methods Study with Members of Higher Education
The COVID-19 pandemic has challenged lives globally in unprecedented ways. While numerous studies have discussed the impact of this pandemic on human lives, this descriptive study examined how this pandemic affected personal well-being (PW) for members of Indian higher education in the early phase of the pandemic in 2020 when there were no vaccines and remedies available. Research participants (n = 551) were faculty members, graduate students, and non-teaching staff in Indian higher education. At the time of data collection, when all campuses were closed, all participants were functioning in their roles in the academic communities via virtual platforms. This descriptive study, based on a mixed-methods research design with concurrent triangulation strategies, collected data from all regions of India. Resulting data identified and discussed the impact of the pandemic on six domains of PW in the life of participants: (a) self-care; (b) professional growth; (c) quality of interrelationship within the family; (d) relationships with significant others outside of the family; (e) process of experiencing/facing and addressing challenges; and, (f) relationship with spirituality/transcendental dimensions. The relevance of the last domain may be unique to Indian participants socio-cultural context and ethos. The findings and discussion explain how PW is a composite of all these six domains, and the pandemic expanded the notion of PW for the members of Indian higher education. Further, the findings also provided a general orientation on how educational leadership teams and institutions can enhance at least three specific dimensions of their community members and thus increase the likelihood of improving the quality of their professional and personal life. The findings may also have relevance for academic communities worldwide and inform clinicians working with members of academic communities, educational institutions, and policymakers. Penerbit Universiti Sains Malaysia, 2024. -
The Role of Mega-events In Promoting Sustainable Behaviour: A Multi-group Analysis Using Micom
This study investigates the influence of participation in mega events, like Dubai Expo 2020, on factors related to environmentally responsible behavior. Data from 361 UAE respondents were collected, examining environmental concern, social and personal norms, ethical obligations, and perceived behavioral control. Partial Least Squares Structural Equation Modeling and Multi-Group Analysis tested the hypotheses. The results showed that environmental concern, ethical ideologies, and perceived behavioral control had stronger impacts on the intention for responsible behavior in event attendees than in non-attendees. Attendees also demonstrated a stronger positive impact on sustainable consumer behavior. These findings offer valuable insights into environmental psychology, sustainability, consumer behavior, and event management, and can inform strategies for promoting sustainable behavior in industries such as travel, tourism, and hospitality. 2024 Taylor & Francis Group, LLC. -
E-shopping orientation, trust and impulse buying in the online context a study based on female members of Generation Z in India
A large number of studies have attempted to understand consumer behaviour in the online context. One construct that has been of particular interest to marketers, retailers and researchers, is impulse buying behaviour. The number of studies attempting to understand the drivers of impulsive purchases has been on a rise. The current pandemic also saw a rise in impulsive purchases and the interest in the construct was renewed. The current study is based on the S-O-R model and evaluates the relationship between e-shopping orientation, trust and impulse buying behaviour. The findings are based on data collected from female members of Generation Z and suggest that frequent visits to e-retail stores and increased patronage can increase the level of trust in the retail partner and influence the number of impulsive purchases. The findings are particularly significant for retailers looking to drive sales through impulsive purchases. In addition, the findings provide empirical support for the application of the S-O-R model to online retail context. Copyright 2024 Inderscience Enterprises Ltd. -
Positive ageing: self-compassion as a mediator between forgiveness and psychological well-being in older adults
Purpose: Positive aging aims to promote the physical health and psychological well-being of older adults for them to age successfully. Under the domain of positive aging, this study aims to explore the mediating role of self-compassion between forgiveness and psychological well-being in older adults. Design/methodology/approach: It was based on a quantitative research design, with a sample of 250 individuals within the age group of 6075 years. Data was collected using Self-compassion Scale (2003), Heartland Forgiveness Scale (2005) and Psychological Well-being Scale. Analysis was performed using Pearsons correlation, linear regression, followed by the generalised linear model of mediation. Findings: The results revealed a significant (p ? 0.001), high and positive correlation between self-compassion and forgiveness (r = 0.821), forgiveness and psychological well-being (r = 0.852) and self-compassion and psychological well-being (r = 0.802). Linear regression suggested that self-compassion and forgiveness are significant (p ? 0.001) predictors of psychological well-being, causing a variance of 75.6%. Mediation revealed significant (p ? 0.001) direct, indirect and total effect between the variables, showing that self-compassion partially mediates the relationship between forgiveness and psychological well-being. Research limitations/implications: The findings provide valuable insights on how fostering self-compassion along with forgiveness can improve psychological well-being among the elderly, however, research on additional variables, drawing comparisons between gender, economic status and clinical populations can be further explored. Nevertheless, this study can be used to develop interventions and therapeutic techniques to enhance self-compassion and forgiveness to improve psychological well-being among older adults. Originality/value: As per the best knowledge of the researcher, this work is original as it is a primary research and no data has been collected of a similar nature from the participants. 2024, Emerald Publishing Limited. -
Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network
In past decade, the android malware threats have been rapidly increasing with the widespread usage of internet applications. In respect of security purpose, there are several machine learning techniques attempted to detect the malwares effectively, but failed to achieve the accurate detection due to increasing number of features, more time consumption decreases in detection efficiency. To overcome these limitations, in this research work an innovative Hybrid dual path Bidirectional long short-term memory Kepler dynamic graph Convolutional Network (HBKCN) is proposed to analyze and detect android malwares effectively. First, the augmented abstract syntax tree is applied for pre-processing and extracts the string function from each malware. Second, the adaptive aphid ant optimization is utilized to choose the most appropriate features and remove irrelevant features. Finally, the proposed HBKCN classifies benign and malware apps based on their specifications. Four benchmark datasets, namely Drebin, VirusShare, Malgenome -215, and MaMaDroid datasets, are employed to estimate the effectiveness of the technique. The result demonstrates that the HBKCN technique achieved excellent performance with respect to a few important metrics compared to existing methods. Moreover, detection accuracies of 99.2%, 99.1%,99.8% and 99.8% are achieved for the considered datasets, respectively. Also, the computation time is greatly reduced, illustrating the efficiency of the proposed model in identifying android malwares. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Exploring the potential application of Cr2AlC MAX phase as an emerging electrocatalyst for overall water splitting
A three-dimensional (3D) chromium carbide ceramic type, H-phase (211) categorized as Cr2AlC, MAX phase has garnered enormous attention in recent times due to its unique structure and bonding, surface area, thermal stability, and thermo-electrical conductivity, and hydrophilicity. A simple synthesis approach is proposed for obtaining layered Cr2AlC, MAX phase, with X-ray diffraction data and SEM morphology confirming the formation of the H-phase. The electrocatalyst Cr2AlC is being utilized for electrocatalytic water splitting application. The Cr2AlC is observed to exhibit an overpotential and Tafel slopes of 215 mV/88.3 mV dec? 1 for the hydrogen evolution reaction (HER) and 376 mV/96.5 mV dec? 1 for the oxygen evolution reaction (OER), respectively, demonstrating good stability for up to 7200s. This study establishes a straightforward method for producing emergent material, Cr2AlC MAX phase, and highlights its promising applications in water electrolysis, hydrogen evolution, and oxygen evolution reactions. Qatar University and Springer Nature Switzerland AG 2024. -
The Role of Imposter Phenomenon on Self-Handicapping and Psychological Distress among Young Adults
The Imposter Phenomenon (IP), characterized by persistent self-doubt and a fear of being exposed as a fraud despite objective success, is a growing concern, particularly among young adults. This study explores the intricate relationships between the Imposter Phenomenon, Self-handicapping, and Psychological Distress in a sample of 242 young adults aged 1825. The data is analysed using descriptive statistics, correlation, and regression. Findings from a comprehensive survey, utilizing the Clance Impostor Phenomenon Scale, the Self-Handicapping Scale, and the Mental Health Inventory reveal a significant positive correlation and prediction between the Imposter Phenomenon and self-handicapping and a positive relationship between the Imposter phenomenon and psychological distress. These findings contribute to a deeper understanding of how the Imposter Phenomenon influences self-handicapping behaviours in young adults, shedding light on the psychological distress associated with these experiences. The study underscores the need for targeted interventions to address imposter feelings and their potential consequences on mental well-being in this vulnerable population, ultimately aiming to foster a healthier and more resilient generation. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
A Deep Ensemble Framework for DDoS Attack Recognition and Mitigation in Cloud SDN Environment
Much research has been done in the recent past on the absolute shift of Internet infrastructure in order to make it more significantly programmable, configurable and make it more conveniently feasible. Software Defined Networking (SDN) forms the basis for this absolute shift in Internet infrastructure. When you look at the benefits of an SDN-based cloud environment they are monumental. Namely, network traffic control and elastic resource management. The SDN-based cloud environment becomes susceptible to cyber threats, especially like that of Distributed Denial of Service (DDoS) attacks and other cyber-attacks that perturb the SDN-based cloud environment. Hence, automated Machine Learning (ML) models are an efficient way to protect against these cyber-attacks. This research will develop a deep learning-based ensemble model for DDoS attack detection and classification (DLEM-DDoS) in a cloud environment. Long Short-Term Memory (LSTM), 1-D Convolutional Neural Networks (1D-CNN) and Gated Recurrent Unit (GRU) are the three DL models integrated into an ensemble model that classifies the incoming packet by majority voting classifiers. Network traffic data including source and destination IP addresses, packet and byte counts, packet and byte rates, flow duration, protocol types and port numbers are fed into the DLEM-DDoS model. This model preprocesses this data by converting categorical values (like protocol types) into numerical values and removing any missing values. Once collected and preprocessed, the data is fed into deep learning models (LSTM, 1D-CNN, GRU) within the framework for analysis. Finally, in this research using the DLEM-DDoS technique an efficient DDoS attack mitigation scheme in an SDN-based cloud environment is demonstrated. The report shows comprehensive stimulations as well as a superiority into the current approaches in terms of several measures. 2024 S. Annie Christila and R. Sivakumar. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
International Journal of Operational Research: a retrospective overview between 2005 and 2020
The study presents a retrospective analysis of the International Journal of Operational Research (IJOR) across its 16 years of publication, 2005 to 2020. IJOR is a journal of international repute that publishes original and peer-reviewed research in the management sciences, decision sciences, and operation research domain. The journal reached its 17th year of publishing in 2021. This study provides a comprehensive overview of 1,023 publications using the bibliometric data analysis technique. The study focuses on the contribution of IJOR to the knowledge domain through publishing trends, authorship patterns, dominant authors, prominent articles, nature of studies, and thematic analysis. Co-occurrence analysis of all keywords, co-authorship, citation and co-citation analysis of authors, countries, and institutions is performed through VOSviewer software. The findings of the study emphasise the relationship of IJOR to different fields. 2024 Inderscience Enterprises Ltd. -
UNDERGRADUATE STUDENTS (UG) MOTIVATION TO FIT LOOKS AND ITS EFFECT ON ACADEMIC PERFORMANCE MEDIATED BY PHYSICAL EXERCISE, SLEEP PATTERN, AND MALADAPTIVE EATING; [MOTIVAO DOS ALUNOS DE GRADUAO (UG) PARA ADEQUAR A APARCIA E SEU EFEITO NO DESEMPENHO ACADICO MEDIADO POR EXERCIO FICO, PADR DE SONO E ALIMENTAO MAL ADAPTATIVA]
India is one of the largest youth-populated countries in the world. Technological advancements during and post-COVID have gifted most youth with personal computers/smartphones and internet connectivity at an affordable price. Thus, binge-watching social media has become the order of the day. Youths are excessively interested in building fit-looks for their psychosocial well-being. However, it has adversely changed their life routine and academic performance. Thus, the present study attempted to measure the relationship between their motivation to fit looks and academic performance in the presence of mediating variables like physical exercise, sleeping, and eating patterns. The study surveyed 300 UG students attending physical exercise or gym classes. The study employed mediation analysis to find the relationship. Results revealed a total and indirect effect between motivation to fit looks and academic performance in the presence of mediating variables, explaining the full mediation effect. The study warrants future researchers to explore the reasons for these relationships with a qualitative approach and identify plausible suggestions to improve youths psychosocial well-being. 2024 Universidade Estadual de Maringa. All rights reserved. -
Corporate social initiatives and wealth creation for firms-an empirical examination
Purpose: This study aims to examine whether social initiatives adopted by firms lead to improved financial performance. The authors analyse the impact of different elements of social initiatives on wealth creation for firms in terms of operating and market performance. Design/methodology/approach: The study is based on the social initiative scores of over 4,500 firms collected from Thomson Reuters' ESG database. The study uses two-stage least squares (2SLS) to analyse the relationship between social initiatives and firm performance. Findings: Profitable, mature, capital intensive and firms with high sales growth rate tend to invest more in social initiatives. Firms with high agency costs invest in social initiatives for workforce efficiency, maintaining human rights and product responsibility. The study documents evidence that social investments are value creating mechanism for firms which leads to improved financial performance in terms of operating and stock market performance. Firms with high dividend intensity invest in social initiatives for workforce welfare and human rights initiatives. Investment in employee well-being and community initiatives results in intangible benefits such as improved stock market valuation. Practical implications: The research model has not considered the impact of intervening variables to understand the relationship between corporate social performance and corporate financial performance. Social implications: Firms ought to recognize that social investment is beneficial in terms of value creation of firms as stock market perceive such investments favourably. Firms must focus more on community development initiatives and workforce initiatives for the value creation of firms compared to investments directed towards human rights initiatives and product responsibility initiatives. Originality/value: This study focusses exclusively on the social dimension of the CSR activities. The authors examine the impact of social welfare scores on firm performance by analysing the valuation effects on scores representing workforce, human rights, community and product responsibility. Moreover, the paper also examines the impact of a new dimension of product responsibility on firm performance. They also focus on both aspects of financial performance in terms of operating performance (proxied by ROE) and the joint impact of both operating and market performance (proxied by Tobins Q). This paper contributes to the research on the linkage of social performance to financial performance by observing that firms with high agency cost characteristics tend to invest in social initiatives for work force efficiency, maintaining human rights and product responsibility. 2024, Emerald Publishing Limited. -
Elastic circuit de-constructor: a pattern to enhance resiliency in microservices
Cloud-based workloads have proliferated with the deep penetration of the internet. Microservices based handling of high volume transactions and data have become extremely popular owing to their scalability and elasticity. The major challenge that cloud-based microservice patterns face is predicting dynamic load and failure patterns, which affect resiliency and uptime. Existing Circuit breaker patterns are biased toward denying incoming requests to maintain acceptable latency values, at the cost of availability. This paper proposes the Elastic Circuit De-Constructor (ECD) pattern to address these gaps. The proposed ECD pattern addresses this challenge by dynamically adapting to changing workloads and adjusting circuit-breaking thresholds based on real-time performance metrics. The proposed ECD pattern introduces a novel De-constructed state, that allows the ECD to identify alternate paths pre-defined by the application, ensuring user requests continue to be routed to the microservice. By leveraging Availability, Latency and Error rate as performance metrics, the ECD pattern is able to balance the fault tolerance and resiliency imperatives in the cloud-based microservices environment. The performance of the proposed ECD pattern has been verified against both no Circuit Breaker and a default Circuit Breaker setting. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Forced Labour, Global Supply Chain and TNCs: Recent Trends and Practices
The abolition of forced labour is a fundamental element of contemporary international human rights law, but the idea has undergone a protracted and complex history, and the scope of the various international mechanisms that handle different aspects of it is not always precisely defined. Slavery, forced labour, and related practices are strictly prohibited under international law. Forced labour is a longstanding and complex obstacle in global supply chains, frequently associated with the desire for inexpensive products and the outsourcing of manufacturing processes to nations with lax labour regulations. The growing power of transnational corporations (TNCs) poses significant challenges to workers at the bottom of supply chains. However, disagreements have made it unclear how to deal with new forms of forced labour, or modern forms of slavery. This confusion highlights the need for a comprehensive approach to combating these issues. Efforts to stop or restrict forced labour will be made easier with a clear legal definition at both the national and international levels, particularly with an emphasis on the human rights perspective. 2024 Kluwer Law International BV, The Netherlands