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The purpose of this paper is to investigate the relative influences of technical and functional quality levels of service quality and patient satisfaction. In this context, the healthcare service quality and the factors affecting customer satisfaction were evaluated using the grey relational analysis (GRA) method.

This is a survey-based study which involves 15 patients in a dialysis center, so the GRA is applied to clarify the uncertainty on service quality level with a limited number of patients without any statistical distribution. In order to reveal whether service quality and customer satisfaction are two different structures, a GRA model is built with ten different quality factors.

Results show that each quality factor has a different effect on the quality of service. Another important finding is that service quality and customer satisfaction are different structures for customers.

The results enable healthcare managers to understand the importance of patient care and the importance of service quality if they want to facilitate their use of their expectations in related factors.

The study is the first in terms of the application of GRA models in a private health institution operating in Turkey. Successful implementation of the GRA method allows a reasonable decision to be made with a limited number of data at hand. It is considered that the method can be used successfully in other health institutions in the Turkish Health System.

This paper presents a study of the development of surface roughness model when turning the mild steel hardened up to 484 HV with mixed alumina ceramic (KY1615) and coated alumina ceramic cutting tools (KY4400). The model was developed in terms of main cutting parameters such as cutting speed, feed rate and depth of cut, using response surface methodology. The established equation indicated that the feed rate affected the surface roughness the most, but other parametres remined stable for arithmetic average height parametre (Ra). However, it decreased with increasing the cutting speed, and with the starting and finishing point of cut for ten point height parametre (Rz). The cutting speed and the depth of cut had a slight effect on surface roughness values of Ra, Rz when using KY4400 cutting tools. Furthermore, the average surface roughness value of Ra was about 0.926 um, 1.089 um for KY1615, KY4400 cutting tools, respectively. The predicted surface roughness was found to be very close to experimentally observed ones at 95% confidence level. Moreover, analysis of variance indicated that squares terms were significant but interaction terms were insignificant for both cutting tools.

Despite the fact that agile manufacturing has been frequently promoted as a means of improving business competitiveness, little empirical evidence exists in the literature validating its positive link with business performance. The purpose of this research paper is to analyse agile manufacturing in Spain and study whether it is a critical factor for success in different industries.

A conceptual model is drawn up, based on the literature and a previous case study, to relate turbulence in the environment with agile manufacturing practices and business performance. The model is tested on a large sample of Spanish manufacturers using a survey methodology to obtain information and a structural equation model to analyse the data.

The results obtained show that, in turbulent environments, the integrated use of agile manufacturing practices promotes manufacturing competitive strength, leading to better operational, market and financial performance.

This study has two main limitations. First, it is difficult to determine the most suitable unit of analysis when studying agile manufacturing. Second, single respondent bias may be considered a limitation.

Managers should consider the integrated implementation of agile manufacturing practices in order to develop manufacturing strength and to outperform competitors in turbulent business environments.

This study adopts a systematic approach to the analysis of agile manufacturing, considering various agility practices or enablers in an integrated way and relating them not only to environmental characteristics but also to business performance. This approach is especially interesting because most of the literature on agile manufacturing deals with agility strategies or techniques in an isolated way. The study also tests the suitability of agile manufacturing in real organisations – for the first time in the Spanish context.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

The purpose of this paper is to develop a performance measurement model for the entire supply chain that includes balanced set of performance measures.

The conceptual model was validated empirically in case companies through semi-structured interviews and content analysis of documents.

Performance measures are important to companies in the natural gas (NG) supply chain to assess performance against set objectives in order to identify loopholes in performance. This is important in order to remain competitive. The research found that both financial and non-financial performance measures are employed by companies to measure performance. The results also indicate that six performance criteria of the conceptual model are vital to the NG supply chain. In addition, all identified measures under each of criteria impact on performance of the supply chain with customer service and financial criteria considered as most important.

The empirical data collected from the NG supply chain in Ghana were relatively small; however, additional information was obtained from company data and relevant magazines. Also, getting through to specific target participant was a challenge due to busy work schedule but, in case companies where it proved impossible, other staff who were also involved in supply chain were interviewed instead.

This research provides a useful source of information on performance measures for practitioners in the NG industry who wish to measure performance of their supply chain. It also provides areas from which further and additional research can be carried out.

This research provides performance measures for the NG supply chain of Ghana. Typically, performance measures have been evaluated in discrete manufacturing supply chain, petroleum industry supply chain and oil industry supply chain. This research expanded on ideas from these studies and applied them in the NG industry.

From 1979 to 2023, the takaful structure has been adopted in many jurisdictions, making the documenting of its early days of establishment relatively difficult and somewhat unreliable. This is unlike conventional insurance, where the history and legislation are well documented and archived in various research (Hellwege, 2016; Marano and Siri, 2017). The purpose of this paper is to provide a chronology for the establishment and development of takaful via the takaful establishment in each jurisdiction, documenting its first takaful operator and first takaful regulation.

This paper has used a qualitative method in the form of reviewing literature and available data such as journals, books and official resources. The data is thoroughly analysed in order to build the chronology for takaful. It adopted an exploratory research design, which is deemed suitable in situations where few works of literature have examined the subject (Neuman, 2014). The paper explores the establishment and non-establishment of takaful in 57 countries. The paper categorises the countries into seven regions starting with the GCC, Levant, Asia, Central Asia, Africa, Europe and Others.

The takaful chronology presented in this paper shows that takaful operations exist in 47 jurisdictions, starting from Sudan and the UAE in 1979, with the most recent adopters being Morocco and Iran in December 2021. It is found that 22 jurisdictions do not have takaful regulations, and the Takaful Act 1984, issued in Malaysia, is considered the first takaful regulation that sets the basis for other regulations that follow.

The paper contributes to the literature by providing a comprehensive chronology of takaful, especially as the few existing timelines have been found to be incomplete and consist of contradictory information.

Solar photovoltaic (PV) is commonly used as a renewable energy source to provide electrical power to customers. This research establishes a method for testing the performance reliability of large grid-connected PV power systems. Solar PV can turn unrestricted amounts of sunlight into energy without releasing carbon dioxide or other contaminants into the atmosphere. Because of these advantages, large-scale solar PV generation has been increasingly incorporated into power grids to meet energy demand. The capability of the installation and the position of the PV are the most important considerations for a utility company when installing solar PV generation in their system. Because of the unpredictability of sunlight, the amount of solar penetration in a device is generally restricted by reliability constraints. PV power systems are made up of five PV modules, with three of them needing to be operational at the same time. In other words, three out of five. Then there is a charge controller and a battery bank with three batteries, two of which must be consecutively be in operation. i.e. two out of three. Inverter and two distributors, all of which were involved at the same time. i.e. two out of two. In order to evaluate real-world grid-connected PV networks, state enumeration is used. To measure the reliability of PV systems, a collection of reliability indices has been created. Furthermore, detailed sensitivity tests are carried out to examine the effect of various factors on the efficiency of PV power systems. Every module's test results on a realistic 10-kW PV system. To see how the model works in practice, many scenarios are considered. Tables and graphs are used to show the findings.

The system of first-order differential equations is formulated and solved using Laplace transforms using regenerative point techniques. Several scenarios were examined to determine the impact of the model under consideration. The calculations were done with Maple 13 software.

The authors get availability, reliability, mean time to failure (MTTF), MTTF sensitivity and gain feature in this research. To measure the reliability of PV systems, a collection of reliability indices has been created. Furthermore, detailed sensitivity tests are carried out to examine the effect of various factors on the efficiency of PV power systems.

This is the authors' original copy of the paper. Because of the importance of the study, the references are well-cited. Nothing from any previously published articles or textbooks has been withdrawn.

The purpose of this study is to investigate the relationship between socioeconomic factors and entrepreneurial orientation (EO) across genders. The study also highlights the impact of cognitive and motivational factors on the EO in Saudi Arabia.

This is a quantitative analysis based on the Adult Population Survey of 2016 conducted by the Global Entrepreneurship Monitor among a sample of 4,053 in the Kingdom of Saudi Arabia.

The results showed significant differences in most EO dimensions between genders. It was found that EO is positively related to gender, with more women intending to start a venture than men in the Kingdom of Saudi Arabia. Self-efficacy, motivation and social welfare motivational factors for EO are positively associated with the gender of the respondents.

This paper illustrates the urgency to establish programs that support self-efficacy, motivation and social welfare to promote entrepreneurship among women in the Kingdom.

The study highlights the impact of cognitive and motivational factors on EO among females in Saudi Arabia.

This study utilised Mehrabian and Russel's stimulus-organism-response (SOR) model to investigate the inter-relationship between perceived social media information qualities (IQ) of online UGC, destination image, perceived travel risk and behavioural intentions.

A total of 255 responses from international tourists were collected. The data collection via an online survey was performed from October 2020 to February 2021. The study model and hypotheses were examined using partial least squares-structural equation modelling (PLS-SEM).

The result indicated that social media intrinsic, representational, and social IQ significantly influenced tourists perceived cognitive image, while only contextual and social IQ significantly predicted the perceived affective image. This study also confirms the significant effect of cognitive image on the affective image. In addition, the authors found that both destination image components, cognitive and affective, significantly affect tourists' behavioural intentions. However, surprisingly, tourists' perceived travel risk did not moderate the effect of destination image components on behavioural intentions.

This study contributes to the body of knowledge by extending Mehrabian and Russel's SOR model in the tourism behaviour context.

From a practical perspective, this study proves that UGC significantly affects destination image and plays an integral part in luring tourists to visit a destination.

Previous research in this area is limited, making this study particularly novel. This study represents one of the initial attempts to evaluate the dimensions of information quality in UGC on social media and online review platforms, particularly within the field of tourism. Treating online travel UGC seriously could assist organisations in leveraging tourist behaviour and enhancing destination image.