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This study aims to investigate the moderating effect of travel motivation on the relationship between perceived risks, travel constraints and visit intention of young women travelers.

A quantitative study was performed, and data were collected from 416 female university students using convenience sampling. Structural equation modeling with partial least square approach was used to test the research hypotheses.

The findings revealed that travel motivation has a moderating effect by weakening the negative relationships between physical risk, structural constraints and visit intention.

The findings of this study provide useful insights for destination managers about the influence of travel motivation on the behavioral intention of young women travelers in the case of higher perceptions of travel risks and constraints.

Literature has discussed the intervening role of travel motivations in different contexts. However, studies are scarce in examining the effect of travel motivation in weakening the negative influence of high perceptions of risks and constraints on intention to visit.

This study, a symposium, aims to explore the determinants of financial inclusion, impact of cross-country income-variations on financial inclusion, do high-income countries really uplift the financial inclusion and does the higher financial inclusion index indicate the larger economy?

This study adopts the panel data model to investigate the impact of high-income countries and low- and middle-income countries on financial inclusion. However, this study further adopts the principal component analysis rather than Sarma’s approach to calculate the financial inclusion index.

Based on the Data of World Bank, United Nations, International Monetary Fund, World Development Indicators, this study concludes that there is no nexus between income variations and financial inclusion, as the study reveals that some low- and middle-income countries have greater financial inclusion index such as Thailand (2.8538FII), Brazil (1.9526FII) and Turkey (0.8582FII). In low- and middle-income countries, the gross domestic product per capita, information technology and communication, the rule of law, age dependency ratio and urbanization have a noteworthy impact on financial inclusion that accumulatively describe the 83% of the model. Whereas, in high-income countries, merely, information technology and urbanization have a substantial influence on the growth of financial revolution and financial inclusion that describes the 70% of the total.

The biggest limitation is the availability of data from different countries.

The originality of this paper is its technique, which is used in this paper to calculate the financial inclusion index. Furthermore, this study contributes to 40 different countries based on income, which could help to boost financial inclusion, and ultimately, it leads them toward economic growth.

Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring an easier way of handling the hard work of intermittent demand forecasting by using commonly used Excel spreadsheet and also performing parameter optimization.

Smoothing parameters of the forecasting methods are optimized dynamically by Excel Solver in order to achieve the best performance. Application is done on real data of Turkish Airlines’ spare parts comprising 262 weekly periods from January 2009 to December 2013. The data set are composed of 500 stock-keeping units, so there are 131,000 data points in total.

From the results of implementation, it is shown that using the optimum parameter values yields better performance for each of the methods.

Although it is an intensive study, this research has some limitations. Since only real data are considered, this research is limited to the aviation industry.

This study guides market players by explaining the features of intermittent demand. With the help of the study, decision makers dealing with intermittent demand are capable of applying specialized intermittent demand forecasting methods.

The study brings simplicity to intermittent demand forecasting work by using commonly used spreadsheet software. The study is valuable for giving insights to market players dealing with items having intermittent demand characteristics, and it is one of the first study which is optimizing the smoothing parameters of the forecasting methods by using spreadsheet in the area of intermittent demand forecasting.

A feed-forward back-propagation neural network (NN) is proposed to model number of firefighters responding to different fire incidents. Such a predictor model can estimate number of firefighter personnel required to tackle new incidents. This a priori information at the time of dispatch can help saving unnecessary efforts in low-risk incidents while focussing on high-risk ones to reduce overall damages and injuries caused by the fire incidents.

A fully connected multilayer NN was adapted as the prediction model. The network was trained on a large number of fire incident records reported in Toronto area between 2000 and 2006 and then its performance was evaluated on another set of never seen records. Two types of prediction were done to model number of responding personnel: a rough category prediction and an exact number prediction.

Results obtained reported a very promising ability of this approach to model number of firefighters responding to a fire incident.

Such a model can significantly reduce uncertainties on the requirements needed for tackling a fire incident once it is reported.

Condition-based maintenance (CBM) has become a central maintenance approach because it performs more efficient diagnoses and prognoses based on equipment health condition compared to time-based methods. CBM models greatly inform maintenance decisions. This research examines three CBM fault prognostics models: logical analysis of data (LAD), artificial neural networks (ANNs) and proportional hazard models (PHM). A methodology, which involves data pre-processing, formulating the models and analyzing model outputs, is developed to apply and compare these models. The methodology is applied on NASA’s Turbofan Engine Degradation data set and the structural health monitoring (SHM) data set from a Nova Scotia Bridge. Results are evaluated using three metrics: error, half-life error and a cost score. This paper concludes that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably, and its predictions show much larger variance than the predictions from the other three methods. Based on these conclusions, the purpose of this paper is to provide recommendations on the appropriate situations in which to apply these three prognostics models.

LAD, ANNs and PHM methods are adopted to perform prognostics and to calculate the mean residual life (MRL) of eqipment using NASA’s Turbofan Engine Degradation data set and the SHM data set from a Nova Scotia Bridge. Statistical testing was used to evaluate the statistical differences between the approaches based on these metrics. By considering the differences in these metrics between the models, it was possible to draw conclusions about how the models perform in specific cases.

Results were evaluated using three metrics: error, half-life error and a cost score. It was concluded that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably and its predictions show much larger variance than the predictions from the other three methods. Overall the models predict failure after it has already occurred (negative error) when the residual life is large and vice versa.

It was concluded that a good CBM prognostics model for practical implications can be determined based on three main considerations: accuracy, run time and data type. When accuracy is a main concern, as in the case where impacts of failure are large, LAD and feedforward neural network are preferred. The preference changes when run time is considered. If data can be easily collected and updating the model is performed often, the ANNs and LAD are preferred. On the other hand, if CM data are not easily obtainable and existing data are not representative of the population’s behavior, data type comes into play. In this case, PHM is preferred.

Previous research in the literature performed reviews of multiple independent studies on CBM techniques performed on different data sets. They concluded that it is typically harder to implement artificial intelligence models, because of difficulties in data procurement, but these approaches offer improved performance as compared to more traditional model-based and statistical approaches. In this research, the authors further investigate and compare the performance and results from two major artificial intelligence models, namely, ANNs and LAD, and one pioneer statistical model, PHM over the same two real life prognostics data sets. Such in-depth comparison and review of major CBM techniques was missing in current literature of CBM field.

Islamic finance is an alternative approach of financial intermediation based on risk-sharing and asset-backed operations, which evolved substantially in recent years in academic research raising the need for quantitative studies to address the intellectual development and scientific performance of this field. This study aims to provide quantitative statistics and comprehensive review of the key influential and intellectual structure of Islamic finance literature.

The authors apply the trending and cutting-edge quali-quantitative approach of bibliometric citation analysis. This study reviews 1,940 English studies and review papers published in scientific journals indexed by the Scopus database from 1983 to 2019. RStudio, VOSviewer and Excel’s software are used to analyze the collected data and apply the bibliometric tests.

The results identify the leading academic authors, journals, institutions and countries with relation to Islamic finance. The authors also propose six main research themes in this field, which are as follows: Islamic finance – fundamentals, growth and legitimacy; customer’s attitude and perception toward Islamic finance; accounting and social reporting of Islamic finance; performance and risk management of Islamic finance; Islamic financial markets; and efficiency of Islamic financial institutions. Lastly, the authors identify research gaps in the existing Islamic finance literature and present 24 future research directions.

The data in this study is confined only to the Scopus database of English papers and reviews. It also considers papers directly related to the field of Islamic finance.

To the best of the authors’ knowledge, this paper is one of the first to address the literature of Islamic finance from a bibliometric aspect. The results of this study along with future research questions will help researchers and practitioners to further explore and stand on firm quantitative bases regarding the scientific development of Islamic finance.

This study intends to explore the influence of emotional intelligence on employee self-efficacy in Pakistan's telecom industry. Besides, it explores the mediating effect of emotional labor (surface acting and deep acting) between them. This study also tests the relationship between emotional labor (surface acting and deep acting) and self-efficacy in the customer care of Pakistan's telecom division.

The study leads forward with a positivist approach to obtain data in two different waves as a time lag study from the big five telecom companies operating in Pakistan. The data was collected from 270 employees working in Customer Services in the Telecom sector.

The results reveal that there exists a positive relationship between emotional intelligence and self-efficacy in customer care employees in Pakistan's telecommunication division sector. Moreover, emotional labor (deep acting) partially mediates the relationship between emotional intelligence and self-efficacy, and surface acting could not mediate the relationship among the employees of customer care in Pakistan's telecom division.

Management of emotions at the workplace has been an immensely vital area in managing the performance of employees, especially in customer-centric jobs, where dealing with customers is the prime focus and achieving customer satisfaction is the utmost outcome. There is limited evidence of the relationship between emotional intelligence and self-efficacy specifically in the customer care of the Telecom sector.

Knowledge management (KM) has become a key concern for companies which nowadays are constantly looking for better ways to assure knowledge sharing between their employees. However, companies encounter several challenges arising from the fact that several generations share the same workplace and a big portion of today's employees are close to retirement. This article aims to focus on knowledge sharing between generations.

The article reviews the “generation” concept and its limitations, and introduces a new view on generations as “communities of knowledge”.

Companies have to find ways not only to assure knowledge transfer between generations, but also knowledge retention of the workers that are retiring. This requires a deeper understanding of the generations and their differentiated knowledge. Yet, today's dominant descriptions of generations (“baby‐boomers”, “generation X”, “generation Y”), do not appear to adequately take into account cultural, socio‐professional and individual factors.

The proposed change of paradigm allows a deeper comprehension of nuances that may exist within the same age group. In doing so, the article makes a contribution to the understanding of knowledge sharing in organizations.

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

In this chapter, I uncover the jail diaries of a revolutionary woman of the 20th century Pakistan, Akhtar Baloch. Although feminism in Pakistan has oscillated between liberal and postcolonial camps, through reading Akhtar's diaries, compiled as Prison Narratives (2017), I center Akhtar's own struggles for Sindh, along with the resistance of the women she met in the prison convicted for the murders of their husbands, to better theorize Marxist Feminism in Pakistan that overturns the structures that commodify women through love and revolution. My article will show the commodification of women's bodies; the “sale” of women through marriage as the goal of this commodification; the lovelessness and alienation women experience in commodified marriages; the unexpected fall in love with someone whom it is subversive for the commodified wife to love; the subversion of this unexpected event that leads to the attempted resolution of this tension through murder; the separation of the lovers through the incarceration of the woman by the capitalist-patriarchal state; and finally, the unexpected outcome (albeit the most common one) that the male lover abandons his female lover once she's jailed, but the defiantly brave female lover finds platonic love in jail through close female friendships with other women who are similarly brave in both love and in revolution. Through this exposition, I show that Akhtar's diaries provide a way for us to build on Marxist Feminist theory through a theory of love and revolution from a Sindhi feminist perspective.