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The purpose of this paper is to examine the relationship between attitude, subjective norm, perceived behavioural control, religiosity, knowledge and Shariah-compliance with intention to hold Shariah-compliant credit card (SCCC) amongst Muslims and non-Muslims.

Researchers used survey questionnaire to collect data and applied a purposive sampling method, then analyzing the data using descriptive statistics and also multi-group analysis of SmartPLS.

For Muslims, attitude, subjective norm, Shariah-compliance, knowledge and religiosity are positively significant. While for non-Muslims, only attitude and subjective norm are positively significant to intention to hold SCCCs.

The behavioural study only focusses on intention to hold Islamic credit cards, which is constraining the extended model of the theory of planned behaviour (TPB) without the actual performance of the behaviour, which is holding SCCCs. Secondly, the research caters for a single method, namely, quantitative without including the qualitative method to better understand and explore other factors affecting consumers’ behavioural intention. The qualitative part can be carried out by conducting interviews with practitioners, regulators and customers. Thirdly, the cultural dimensions are not combined as parts of TPBs’ antecedents for extended model that could be affecting intention, as Malaysia has diverse ethnic groups with different religious background.

In terms of managerial implications, the findings will further assist financial service providers to develop more effective marketing strategies for Islamic financial products not just to cater for the Muslims but also the non-Muslims, who are increasingly attracted to Islamic banking. As many Muslims are still holding conventional credit cards, it is timely for the Islamic financial institutions to attract them with the SCCCs.

Financial marketers are expected to be qualified and well-versed on the different Islamic product structures and also the conventional products. By having such enables them to enlighten and create awareness amongst the targeted consumers in seeking Shariah-compliant financial-related products.

The research will contribute to new theoretical knowledge of an extended behavioural model in relation to customers’ perception towards SCCCs’ acceptance.

This study aims to concentrate on quality improvement in plain yogurt production process at company A through adjusting the factors affecting the acidity of the yogurt and determining the optimal level of these factors.

Six Sigma-based framework using define-measure-analyze-improve-control (DMAIC) methodology is adopted through the application of design of experiments tool to focus on customer’s requirements to improve the quality characteristic of plain yogurt production process in dairy products manufacturing company (company A) in Iran.

The results showed that incubation time and fat percentage were significant factors on pH values of yogurt and the optimum settings for these factors were defined as 12 h for the incubation time and 1.5 per cent for the fat percentage.

This study focused solely on the plain yogurt production process in dairy products manufacturing company.

Simplicity of Six Sigma plays a leading role for enabling any dairy manufacturer to determine the problem and minimize its cause through a systematic approach.

Six Sigma has been considered to be a systematic, powerful technique to continuously improve the processes and develop the new products by using effective analytical and statistical tools and methods. This paper presents a Six Sigma-based framework using DMAIC methodology to improve the quality characteristic of plain yogurt production process in dairy products manufacturing company.

This study contributes to show a potential area in which Six Sigma DMAIC approach can promote to improve the quality of yogurt production process. This case can prompt managers of the company to apply Six Sigma method to address complicated problems in other processes, where causes particularly are not clear.

The purpose of this paper is to discuss how personal values and internal motivation interact to influence entrepreneurial orientations. Personal values and internal motivation are among personal characteristics that have an impact on entrepreneurial orientation. However, these two personal variables are studied in isolation; therefore, how these two interact to influence entrepreneurial orientation is not yet fully understood.

This study comprised a sample of Malay-owned small and medium enterprises (SMEs) located in the Klang Valley in Malaysia. A cross-sectional research design was used to examine the relationships between personal values, self-efficacy motivation and entrepreneurial orientation among small-scale Malay SMEs. To focus on SMEs, lists were sought from the Majlis Amanah Rakyat (MARA). Malay was chosen for this study because Malaysia has a majority of the Malay population compared to other races. Data were gathered based on mailed and personally administered questionnaires.

The findings indicate that self-efficacy of Malay SMEs in the Shah Alam area acted as a mediator in the relationship between personal values and entrepreneurial orientation. Malay SMEs were found to have high self-efficacy and entrepreneurial orientation.

An important implication of this research is that the interesting findings provide some insight to management consultants for focusing on improving the self-efficacy of Malay SMEs, in their training, as this would improve their entrepreneurial orientations.

The findings are original and unique and are based on established theories from the literature on Malay-based SMEs in Malaysia. The results are based on a sample of Malay-owned SMEs in the Klang Valley in Malaysia. The research findings are useful to academics and policymakers interested in fostering SMEs in Malaysia.

The purpose of this paper is to examine the effect of religiosity on Muslim consumer behaviour and on purchasing decision.

Survey comprised a sample of 232 Muslims from the middle‐ and upper‐income groups who work in Shah Alam and Bangi in the Selangor state of Malaysia.

The findings indicate that religious Muslims in Shah Alam and Bangi area consider Islam as their source of reference and they spend moderately, as commanded by Allah in the Quran. This study confirms that religiosity acts as a full mediating role in the relationship between relative and contextual variables, and purchase behaviour of Muslim consumers.

The paper's interesting findings serve to remind entrepreneurs that they cannot neglect the element of religion in their marketing activities, particularly in the development of products.

This study aims to present an overview of leading research trends in the lean six sigma domain published in the International Journal of Lean Six Sigma (IJLSS) since its inception.

The study analyses articles published between 2010 and 2019 in IJLSS using the bibliometric technique. The results of data analysis identify the most prolific authors, their affiliation, citation trends and highly cited articles from the journal. Further, a graphical analysis involving bibliographic coupling and co-citation analysis of the corpus enriches the investigation.

The results of the bibliometric analysis suggest that the number of IJLSS’s publications and citations grew markedly over time (from 4 citations in 2010 to nearly 1,324 in 2019). The organizational diversity and collaboration among authors publishing in IJLSS are trending upwards. Case study and focus group are the two most common research designs in publications. In the study, three major themes emerged: implementation of lean on business, integration of lean and six sigma and the effects of lean six sigma on businesses.

The study finding informs and educates practitioners and scholars about various qualitative research tools, applications and methods of implementing lean six sigma tools in different industry sectors.

The study uses bibliometric analysis to propose a novel categorization of research published in IJLSS and to report on the utilization of various lean tools in the journal. The study provides guidance for new future research besides offering a thorough introspection of the lean and six sigma domains.

This study aims to investigate differences between Islamic and conventional banks in Pakistan with respect to their operational efficiency, liquidity risk and asset quality. Importantly, in addition to full-fledged Islamic and conventional banks, this study also investigates a more recently emerged breed of hybrid banks, i.e. Islamic divisions of conventional banks.

Data for the period 2011–2020 was collected from financial reports of all full-fledged Islamic banks (5), Islamic banking divisions of conventional banks (8) and conventional banks (20) in Pakistan. Logistic regressions were designed to test the proposed hypotheses.

The findings suggest that full-fledged Islamic banks are operationally less efficient and experience higher liquidity risk than conventional banks. However, the asset quality of Islamic banks is better than that of conventional banks. Next, in the robustness analysis, the authors extended the sample size by adding the Islamic divisions (window) of the conventional banks; they found almost the same result except for efficiency which turned out to be non-significantly related to bank type.

The findings are beneficial for investors, depositors, consumers and bank management in understanding the financial features of such as efficiency, liquidity and liquidity risk that separate Islamic banks from conventional banks.

The findings of this study present a clear picture to bankers and practitioners about some financial features of banking systems and depict that Islamic banks are in need to improve their liquidity risk management practices to compete with conventional banks.

The purpose of this paper is to examine the determinants of capital structure using a dataset of firms in Malaysia.

This paper carries out a panel data analysis of 8,270 observations from 827 listed non-financial firms on the Malaysia stock market over the period 2008–2017. To estimate the model and analyse the data collected from the DataStream and World Bank databases, the authors use static panel estimation techniques as well as two-step difference and system dynamic GMM estimator.

The results show that profitability, growth opportunity, tax-shield, liquidity and cash flow volatility have a negative and significant impact on debt measures. However, the effects of collateral, non-debt tax and earnings volatility on measures of debt are positive and significant. In addition, firm size, firm age, inflation rate and interest rate are important determinants of the present value of debt. The results also show a significant inverse U-shaped relationship between the firm's age and its capital structure. In general, the results support the proposition advocated by the pecking order and trade-off theories.

The results of this study necessitate formulation of various policy measures that can counter the effects of debt on firms.

The present study is among the earliest to use both the book and market value measures of capital structure. It also uses three proxies for each: total debt, long-term debt and short-term debt. It incorporates earning volatility and cash flow volatility as new independent variables in the model. These variables have not previously been used together with both book and market value measures of capital structure. The study also examines the non-monotonic relationship between firm's age and capital structure using a quadratic regression method. It applies both static panel techniques and dynamic GMM estimation techniques to analyse the data.

This research aims to enhance the operational excellence and continuous improvement of the retail supply chain in the Saudi Thobe Factory through an integrated approach of Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) with artificial intelligence (AI).

The study identified the tailoring department as the department with maximum defects by using voice of customer and critical to quality tools. An AI-integrated Six Sigma approach was applied to identify and eliminate nonproductive stages, and a new facility layout was designed to enhance productivity and customer satisfaction.

The use of the factor rating method and simulation using Arena software led to an improved sigma level from 1.597 to 2.237, representing an increment of about 40%. Additionally, the defects per million opportunities reduced from 461,538 to 230,769. The study can help production industry management to optimize facility layouts and improve overall production line efficiency.

This study addresses the lack of published research on the use of an integrated approach of Six Sigma DMAIC with AI in the retail and distribution sector of Saudi Arabia, particularly for small and medium-sized enterprises (SMEs). The study demonstrates how this approach may significantly boost SMEs’ performance and provides a basis for future research in this area.

This study provides a practical example of how an integrated approach of Six Sigma DMAIC with AI can be used in the retail and distribution sector of Saudi Arabia to enhance operational excellence and continuous improvement. The study highlights the potential benefits of this approach for SMEs in the region and provides a framework for future research.

In recent years, halal food industries are facing a high level of competition. The growing demand for halal food means firms are working hard to improve quality and reduce halal food defects. The purpose of this study is to propose a halal-based six sigma (SS) framework that could be used to reduce halal food defects and improve compliance.

The proposed halal six sigma (HSS) framework has five phases based on the define-measure-analyse-improve-control (DMAIC) approach. The proposed framework also integrates halal assurance system requirements into HSS to ensure compliance. Tools used in the proposed model include SIPOC tools, Pareto diagram, root cause analysis and the improvement process flows. A case study in a chicken meat company is used to test and validate the proposed framework. In case of study research, brainstorming was used to review an initial proposed framework and focus group discussions were used to determine feasible improvement actions.

The results showed that the proposed HSS framework could be effective to detect and reduce the halal defects and lower the halal defect costs. By adopting the framework, the case firm was able to lower halal defect costs significantly and increase the SS level.

SS approach can be designed and adapted to specific food industry. HSS framework could provide a systematic approach to reduce halal food defects and sustain the improvement efforts. The proposed HSS framework is also easy to use to understand halal critical points and improve halal compliance.

This study proposed a HSS framework that can be adopted to reduce halal food defects and costs. This proposed framework will benefit the halal food industry intending to realize zero halal food defects and lower production costs. The limited number of publications in the research theme of halal food defects show that there is a significant gap in the existing body of knowledge.

The purpose of this paper is to predict the concrete pouring production rate by considering both construction and supply parameters, and by using a more stable learning method.

Unlike similar approaches, this paper considers not only construction site parameters, but also supply chain parameters. Machine learner fusion-regression (MLF-R) is used to predict the production rate of concrete pouring tasks.

MLF-R is used on a field database including 2,600 deliveries to 507 different locations. The proposed data set and the results are compared with ANN-Gaussian, ANN-Sigmoid and Adaboost.R2 (ANN-Gaussian). The results show better performance of MLF-R obtaining the least root mean square error (RMSE) compared with other methods. Moreover, the RMSEs derived from the predictions by MLF-R in some trials had the least standard deviation, indicating the stability of this approach among similar used approaches.

The size of the database used in this study is much larger than the size of databases used in previous studies. It helps authors draw their conclusions more confidently and introduce more generalised models that can be used in the ready-mixed concrete industry.

Introducing a more stable learning method for predicting the concrete pouring production rate helps not only construction parameters, but also traffic and supply chain parameters.