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Understanding the fluid flow through rock masses, which commonly consist of rock matrix and fractures, is a fundamental issue in many application areas of rock engineering. As the equivalent porous medium approach is the dominant approach for engineering applications, it is of great significance to estimate the equivalent permeability tensor of rock masses. This study aims to develop a novel numerical approach to estimate the equivalent permeability tensor for fractured porous rock masses.

The radial point interpolation method (RPIM) and finite element method (FEM) are coupled to simulate the seepage flow in fractured porous rock masses. The rock matrix is modeled by the RPIM, and the fractures are modeled explicitly by the FEM. A procedure for numerical experiments is then designed to determinate the equivalent permeability tensor directly on the basis of Darcy’s law.

The coupled RPIM-FEM method is a reliable numerical method to analyze the seepage flow in fractured porous rock masses, which can consider simultaneously the influences of fractures and rock matrix. As the meshes of rock matrix and fracture network are generated separately without considering the topology relationship between them, the mesh generation process can be greatly facilitated. Using the proposed procedure for numerical experiments, which is designed directly on the basis of Darcy’s law, the representative elementary volume and equivalent permeability tensor of fractured porous rock masses can be identified conveniently.

A novel numerical approach to estimate the equivalent permeability tensor for fractured porous rock masses is proposed. In the approach, the RPIM and FEM are coupled to simulate the seepage flow in fractured porous rock masses, and then a numerical experiment procedure directly based on Darcy’s law is introduced to estimate the equivalent permeability tensor.

Even though the Indian luxury market is predicted to grow as much as the Chinese one over the coming years, limited research has been conducted on luxury consumer behavior. The purpose of this study is to examine the purchasing behavior for luxury fashion goods using the framework of the theory of planned behavior.

A total of 257 respondents were included after distributing a structured questionnaire by surveying real luxury consumers in Delhi. Data were analyzed using structural equation modeling.

The results of the study indicated that subjective norm was the most important determinant of the purchasing intentions for luxury fashion goods, followed by attitude. Perceived behavioral control was not found to have a significant relationship with purchasing intentions, but it showed a strong positive relationship with actual purchasing behavior.

This study provides new theoretical insights regarding luxury consumer behavior in India. It explains the motivating factors behind purchasing intentions for luxury goods among Indian consumers. The findings of the study will provide great help to global luxury companies in formulating their penetration and expansion strategies in the Indian market.

Collaboration, communication, critical thinking and creativity are the most essential Cs of education. However, at present, these Cs are interlinked with technology to make it more effective and reliable. Educational technology infuses higher education, many people use it on a daily basis. Students are eager to adopt such technologies that help them in academia. Hence, this study aims to investigate how cloud computing adoption influences the academic performance of students by incorporating innovative, knowledge, economic and technological factors in the model.

The data are collected by using the survey method and the five-point Likert scale is used for this purpose. The statistical techniques applied to the data set were confirmatory factor analysis and partial least square structural equation modeling.

All dimensions have been observed to have a positive association with perceived ease of use and perceived usefulness. On the other hand, the innovative factors which include relative advantage and complexity have a negative impact on perceived ease of use and perceived usefulness except for compatibility. Moreover, economic factors, all have a negative relationship. Finally, research shows that perceived ease of use and perceived usefulness have a direct and significant relationship with cloud computing adoption among students, which ultimately predicts their academic performance.

Present research makes the following vital contributions; first, focus on the role of innovative factors, economical, technological and knowledge factors together that were previously largely ignored. Second, it extends the model of technology acceptance model for analyzing the cloud computing adoption pattern among university students. Finally, this study uses PLS-SEM for analyzing the relationship.

User-generated content (UGC) refers to semantic and behavioral traces created by users on various social media platforms. While several waves of platforms have come and gone, the long-term sustainability of UGC activities has become a critical question that bears significance for theoretical understanding and social media practices.

Based on a large and lengthy dataset of both blogging and microblogging activities of the same set of users, a multistate survival analysis was applied to explore the patterns of users' staying, switching and multiplatforming behaviors, as well as the underlying driving factors.

UGC activities are generally unsustainable in the long run, and natural attrition is the primary reason, rather than competitive switching to new platforms. The availability of leisure time, expected gratification and previous experiences drive users' sustainability.

The authors adopted actual behavioral data from two generations of platforms instead of survey data on users' switching intentions. Four types of users are defined: loyal, switcher, multiplatformer and dropout. As measured by the transitions among the four states, the different sustainability behaviors are thereby studied via an integrated framework. These two originalities bridge gaps in the literature and offer new insights into exploring user sustainability in social media.

Although knowledge based on business intelligence (BI) is crucial, few studies have explored the core of BI knowledge; this study explores this topic.

The authors collected 1,306 articles and 54,020 references from the Web of Science (WoS) database and performed co-citation analysis to explore the core knowledge of BI; 52 highly cited articles were identified. The authors also performed factor and cluster analyses to organize this core knowledge and compared the results of these analyses.

The factor analysis based on the co-citation matrix revealed seven key factors of the core knowledge of BI: big data analytics, BI benefits and success, organizational capabilities and performance, information technology (IT) acceptance and measurement, information and business analytics, social media text analytics, and the development of BI. The cluster analysis revealed six categories: IT acceptance and measurement, BI success and measurement, organizational capabilities and performance, big data-enabled business value, social media text analytics, and BI system (BIS) and analytics. These results suggest that numerous research topics related to big data are emerging.

The core knowledge of BI revealed in this study can help researchers understand BI, save time, and explore new problems. The study has three limitations that researchers should consider: the time lag of co-citation analysis, the difference between two analytical methods, and the changing nature of research over time. Researchers should consider these limitations in future studies.

This study systematically explores the extent to which scholars of business have researched and understand BI. To the best of the authors’ knowledge, this is one of the first studies to outline the core knowledge of BI and identify emerging opportunities for research in the field.

The study challenges the assumption of independence among Technological, Organizational and Environmental (TOE) factors and investigates the influence of TOE factors on Big Data Analytics (BDA) adoption among Small and Medium Enterprises (SMEs). Top management support was proposed as a mediator between technological and organizational factors and BDA adoption. Furthermore, the moderating effect of environmental factors on the association between relative advantage, compatibility, competitiveness, organizational readiness and BDA adoption was evaluated.

Data were collected from 171 SME manufacturing firms and analyzed using the partial least squares technique.

The findings confirmed the interrelationships among the TOE factors. The effects of compatibility, competitiveness and organizational readiness on BDA adoption were mediated by top management support. Furthermore, environmental factors moderate the influences of compatibility and organizational readiness on top management support.

The findings contribute to the TOE model by challenging the assumption of independence among TOE factors, and future studies should use this model with more caution and consider the potential relationships between TOE factors.

This paper aims to investigate the effect of parent–adolescent conflict in step versus biological families on family communication patterns (FCPs) and the conflict resolution strategy adopted by adolescents during family destinations or holiday planning (where to visit?).

The literature on family conflict (i.e. parent–adolescent conflict) and the different types of families (step vs nuclear) supported the proposed framework. The survey was conducted in the Indian subcontinent with a sample size of 437 adolescents. SPSS 22.0 was used for factor analysis (exploratory and confirmatory factor analysis) and structural equation modelling was used through AMOS 26.0 for data analysis.

Significant relationship was observed between the types of families (step and biological), FCP and the resolution strategy chosen by Indian adolescents. Adopting a resolution strategy by adolescents in both families depends on the type of FCP in the family. Adolescents in stepfamilies have socio-oriented FCP and use “positive problem-solving” and “conflict withdrawal” as a resolution strategy. In contrast, adolescents in biological families have concept-oriented families and use “conflict enhancement” as a resolution strategy. It has also been found that adolescents who fall into high-stress categories used conflict enhancement strategies. In contrast, those who fall under low-stress categories used positive problem-solving and withdrawal strategies.

This study will add a new chapter to adolescents’ decision-making literature in line with the previous research. It has practical implications for tourism marketers, academicians/researchers and policymakers. Marketers can segment adolescents into step versus biological families, and the choice of resolution strategies may introduce efficient and competent marketing strategies and promotional campaigns.

This study favours that family type is a robust construct to predict adolescents’ choice of resolution strategy. So, it is one of the most influential variables in adolescents’ resolution strategy adoption.

The purpose of this paper is to understand the factors that enable and inhibit WhatsApp users' discontinuance intention (DI) following the change in WhatsApp's privacy policy.

Using the enabler-inhibitor model as a framework, a research model consisting of discontinuation enabler distrust (DT) and the DT's antecedents [(negative electronic word of mouth (NEWOM), negative offline word of mouth (NOWOM) and privacy invasion (PI)], discontinuation inhibitor inertia (INR) and INR's antecedents (affective commitment, switching cost and use habit) and moderator structural assurance was proposed and tested with data from 624 WhatsApp users using partial least square structure equational modeling (PLS-SEM).

The results show that DT created due to NEWOM and a sense of PI significantly impact DI. However, INR has no significant impact on DI. Structural assurance significantly moderates the relationship between DT and DI.

The paper collected data when many WhatsApp users switched to other platforms due to the change in WhatsApp's terms of service. The timing of data collection allowed for collecting the real impact of the sense of PI compared to other studies where the effect is hypothetically induced. Further, the authors acknowledge social media providers' efforts to address privacy criticism and regain users’ trust, an area that has received little attention in prior literature.

Accurately estimating the severity of derailment is a crucial step in quantifying train derailment consequences and, thereby, mitigating its impacts. The purpose of this paper is to propose a simplified approach aimed at addressing this research gap by developing a physics-informed 1-D model. The model is used to simulate train dynamics through a time-stepping algorithm, incorporating derailment data after the point of derailment.

In this study, a simplified approach is adopted that applies a 1-D kinematic analysis with data obtained from various derailments. These include the length and weight of the rail cars behind the point of derailment, the train braking effects, derailment blockage forces, the grade of the track and the train rolling and aerodynamic resistance. Since train braking/blockage effects and derailment blockage forces are not always available for historical or potential train derailment, it is also necessary to fit the historical data and find optimal parameters to estimate these two variables. Using these fitted parameters, a detailed comparison can be performed between the physics-informed 1-D model and previous statistical models to predict the derailment severity.

The results show that the proposed model outperforms the Truncated Geometric model (the latest statistical model used in prior research) in estimating derailment severity. The proposed model contributes to the understanding and prevention of train derailments and hazmat release consequences, offering improved accuracy for certain scenarios and train types

This paper presents a simplified physics-informed 1-D model, which could help understand the derailment mechanism and, thus, is expected to estimate train derailment severity more accurately for certain scenarios and train types compared with the latest statistical model. The performance of the braking response and the 1-D model is verified by comparing known ride-down profiles with estimated ones. This validation process ensures that both the braking response and the 1-D model accurately represent the expected behavior.

The dynamic and evolving character of the fashion market, affected by globalisation and technology development, has resulted in complex supply chains. In order to keep costs down, fashion companies have relocated their production facilities to developing countries. At the same time, easing trade restrictions and reducing tariffs have encouraged fashion companies to offer their products all around the world. Accordingly, fashion supply chains have become geographically dispersed, with an increasing number of members, and decreasing traceability and visibility of the chains. As a consequence of that, they face uncertainties and some risks, from stock-outs, late deliveries, over-stocks, to counterfeits etc. This chapter sheds light on counterfeiting as the making of a product that so closely imitates the appearance of the product of another as to mislead consumers that the product is an original. Counterfeiting presents the biggest threat to the fashion industry due to its growing popularity among consumers who were not aware of buying fakes or knowingly bought fake fashion items. This chapter aims to examine the pros and cons of purchasing counterfeit fashion products (CFPs) by Gen Y and Z consumers, as they are more likely to purchase them. The results of the study on a sample of young Croatian consumers show that they prefer CFPs due to functional benefits of price and accessibility, and overestimated originals. The main reasons for young consumers not purchasing counterfeits are the perception of having poor quality relative compared to authentic ones as well as the ethical and legal dilemmas involved.