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The purpose of this paper is to study whether the presence of women directors on the corporate board influences financial performance (FP). To examine the underlying causal mechanism, the authors modeled firm-level intellectual capital efficiency (ICE) in the relationshipbetween board gender diversity (BGD) and FP.

Using a sample of 5,879 US firms, a structural model of BGD, IC and FP is conceptualized by accounting for the endogeneity issues and alternative measures of the key variables in the empirical framework. In the model, the percentage of women directors is taken as BGD measures and value-added intellectual coefficient as an IC performance measure, considering governance and corporate performance measures.

The authors find a significant impact of BGD on FP. In particular, the results suggest: BGD is linked to IC; the influence of board gender diversity on the FP is indirect; and ICE fully mediates the relationship between BGD and FP.

To the best of the author’s knowledge, no study has empirically investigated whether the firm-level IC performance explains the influence of BGD on FP. Drawing on the resource-based view and organizational learning theory of the firm, the authors empirically modeled the relationship between BGD and FP through a mediation mechanism of firm-level ICE to fill the void in the literature.

The purpose of this research study is to extract and identify named entities from Hadith literature. Named entity recognition (NER) refers to the identification of the named entities in a computer readable text having an annotation of categorization tags for information extraction. NER is an active research area in information management and information retrieval systems. NER serves as a baseline for machines to understand the context of a given content and helps in knowledge extraction. Although NER is considered as a solved task in major languages such as English, in languages such as Urdu, NER is still a challenging task. Moreover, NER depends on the language and domain of study; thus, it is gaining the attention of researchers in different domains.

This paper proposes a knowledge extraction framework using finite-state transducers (FSTs) – KEFST – to extract the named entities. KEFST consists of five steps: content extraction, tokenization, part of speech tagging, multi-word detection and NER. An extensive empirical analysis using the data corpus of Urdu translation of Sahih Al-Bukhari, a widely known hadith book, reveals that the proposed method effectively recognizes the entities to obtain better results.

The significant performance in terms of f-measure, precision and recall validates that the proposed model outperforms the existing methods for NER in the relevant literature.

This research is novel in this regard that no previous work is proposed in the Urdu language to extract named entities using FSTs and no previous work is proposed for Urdu hadith data NER.

The purpose of this paper is to explore how consumers’ socialization influences soft drink consumption behavior in Pakistan. Since consumer socialization has long been considered but it is important to understand whether the extent of consumer socialization in terms of soft drink consumption influences consumer behavior by taking into consideration consumer cohorts.

The quantitative research is based on consumer survey method by using Likert scale questionnaire. Convenience sampling technique with a sample size of 637 is used. Data are analyzed by using cronbach α, ANOVA, correlation and multiple regressions.

Overall, the findings maintain the impact of consumer socialization on soft drink consumption. Such influence of consumer socialization through social media, cultural groups and social groups encourages soft drink socialization behavior. Additionally there is also an evidence of mediating role of consumer generational behavior in soft drink consumption.

The results of this paper extend knowledge of how consumer socialization affects soft drink consumption behavior and provide important insights into how consumer cohorts should be targeted. The Chosen research approach is a limitation of the study.

The results are of value to academic researchers, soft drink industry practitioners in a way that it will help them to portray marketing and advertising activities by taking into consideration consumer cohorts behavior.

This paper addresses an untapped issue on how cohorts socialization at different social setting impact on consumer soft drink consumption behavior.

This paper fulfills a recognized need to study soft drink socialization in terms of cohort’s behavior.

This study investigates the impact of insider-ownership of publicly traded firms on their performance, cost of debt (COD) and cost of equity. We use a sample of 104 non-finance listed companies of Pakistan for the period from 2006 to 2016. Our study is conducted in Pakistan as a developing country in which insider-ownership is dominant, and a weak external corporate governance mechanism increases the payoffs from insider-ownership. We use feasible generalized least square (FGLS) regression methods to examine these hypotheses. Based on agency theory, we find that insider-ownership enhances firm performance. Furthermore, our results show that insider-ownership reduced the COD and equity. Higher ownership decreases the opportunistic behavior of insiders. It also reduces the creditor’s perception of the likelihood of default on loan payments and reduces agency issues among shareholders. The insider will invest in positive NPV projects which will help maximize shareholders’ wealth and minimize the COD. Similarly, the relationship between insider-ownership and cost of equity is significant but negative. Supporting the convergence of interest increase in ownership helps in aligning the goals of managers and stakeholders whereby the insider will focus on value creation by minimizing equity cost.

The terrorist attacks on the United States on September 11, 2001 were a very traumatic event for the entire nation. This was especially true for law enforcement. Many law enforcement officers and other first responders lost their lives in the initial response to the attack while attempting to save the lives of the citizens they were sworn to protect. As a result of the 9/11 attacks, many changes have occurred in the missions, operations and tactics of local law enforcement agencies in the United States.

This chapter attempts to examine the changes that were forced upon law enforcement by the events of 9/11 and to look at what the future might hold for law enforcement in an enhanced homeland security environment.

Terrorism presents additional duties for law enforcement. Traditional police missions have not been lessened, but new threats to the public have arisen.

The research empirically examines the role of artificial intelligence (AI) integrated with social customer relationship management (SCRM) in multinational enterprises (MNEs) towards international relationship management under social distancing conditions due to the COVID-19 pandemic.

The study initially undertakes pertinently focused theoretical research in the fields of international marketing, knowledge management, and customer relationship management. And, utilizing the theories of resource-based view (RBV) and dynamic capability view (DCV) theory, the study develops a theoretical model that is subsequently empirically validated through a survey and structural equation modeling.

The study highlights the importance and means of adopting AI-integrated social CRM by MNEs, in the context of international relationship management, under the Covid-19 social distancing conditions. The study more specifically elucidates the role and significance of MNE leadership approach and support towards the adoption of AI-integrated social CRM systems and, ultimately, performance improvement of MNEs under such conditions.

The study presents insights and prescriptive explications on a topic at the heart of state-of the-art technology-based international marketing in the explicit context of the primary business-defining environment of the Covid-19 pandemic. The research provides practicable suggestions to MNEs' leadership towards the adoption of an AI-integrated social CRM system. And the study presents a unique model for international relationship management under social distancing conditions, potentially applicable during other crises.

The research is original and on a ‘fresh’ topic that combines the latest technological advancements in business (AI-integrated CRM) with the present critical business context (pandemic). The research develops a tested theoretical model that (a) is unique in its field; (b) provides a solid foundation for further research; (c) bears generic value and application during other-than-Covid-19 conditions; and (d) enhances the understanding of important fields of international marketing, including international customer relationship management and global knowledge management.

The purpose of this paper is to investigate the heat transportation rate by using Cattaneo–Christov heat flux model. Furthermore, homogeneous-heterogeneous reaction is also deliberated in the modeling of concentration expression.

The nonlinear PDEs are reduced to ODEs via implementation of applicable transformations. Numerical scheme bvp4c is used to obtain convergent solutions.

The main findings are to characterize the generalized Fourier’s heat flux and homogeneous-heterogeneous reactions in 3D flow of non-Newtonian cross fluid.

It is to certify that this paper is neither published earlier nor submitted elsewhere.

This study aims to investigate the determinants of Islamic stock returns listed on Jakarta Islamic Index (Indonesia) between 2008 and 2018.

This study uses a quantile bounded autoregressive distributed lag (QBARDL) model to uncover relevant relationships.

This study finds that the Dow Jones Islamic Market Index, gold returns, world oil prices and exchange rates are the determinants of the Indonesia’s Islamic stock returns. However, the relationship is time varying developing intra-/inter-quantile bounded.

Integration of the Islamic stock returns with the real economic indicators changes over time. The findings have important implications for the policymakers, the fund managers and the investors to anticipate consequences when considering the macroeconomic conditions before participating in the Indonesian Islamic stock market.

Using a QBARDL, this study finds that the Islamic stock returns have on net and “time-varying intra-/inter-quantile developing” relationship with its determinants as data quantiles progressed from 25% to 75%.

This paper aims to develop a mathematical model featuring Brownian motion and thermophoresis. The idea of curved stretching subjected to time-dependent non-Newtonian (Sisko) fluid flow is introduced.

Shooting scheme is implemented to compute nonlinear systems.

Velocity profile of Sisko magnetonanofluid enhances for augmented values of curvature parameter.

To the best of the authors’ knowledge, no such analysis has yet been reported.