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This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

This chapter of the book aims to achieve sustainability and productivity in light of the interaction between managers and engineers in a lean and agile supply chain management system in today’s organizations. The main innovation of this chapter is the use of the balanced scorecard (BSC) model and fuzzy analysis network process (FANP) to create a suitable platform for the realization of this interaction between managers and engineers and to identify exactly which expert system is ideal for the main purpose. Indeed, this chapter introduces its readers to the application of strategic management tools such as the BSC accompanied by FANP in the elements of supply chain management where data analysis of lean and agile networks in supply chain management can create a competitive advantage in the organization.

This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.

This study aims to examine the relationship between public knowledge, awareness and attitudes towards post-COVID-19 infection prevention in Sarawak. At present, Sarawak is in the post-pandemic stage, marked by a gradual return to normalcy, albeit with some persistent changes caused by the pandemic.

Data were collected from various geographic areas in Sarawak through a Google Form link and QR code during a cross-sectional study, resulting in the acquisition of 1,128 responses. Data analysis was performed using SPSS 28.0 and WarpPLS 8.0.

The result revealed that out of five hypotheses, four were found to be supported, indicating a positive relationship between public knowledge, awareness and attitudes towards COVID-19 infection prevention. However, an unsupported relationship was found between public awareness and infection prevention practices.

This study is limited to the Malaysian population and has a cross-sectional design, affecting generalizability. It is recommended that future research complete an in-depth study of the knowledge, awareness and practices of COVID-19 using other data collection techniques.

Public health and policymakers can use the study to implement effective communication strategies and prioritize digitalization for economic recovery. It highlights the importance of preventive measures and the public’s role in managing future pandemics.

The originality of this research can be drawn from key findings that indicate that people overall gained knowledge on the prevention measures during the post-COVID-19 pandemic, and the accuracy of the information significantly impacts public knowledge, awareness and practices of COVID-19 infection prevention.

In Structural Equation Modelling (SEM) cases, a number of steps to ensure that the measurement of the latent constructs, variables, and indicators have been appropriate, reliable, and meet the rules of quantitative research that must be passed by researchers. One of them is the criteria for determining the presence (or not) of a discriminant validity violation, which is still a fierce debate among researchers today. The last recommended method is the Heterotrait–Monotrait ratio of correlation (HTMT) which seeks to revise the previously existing discriminant validity criteria such as the Fornell–Larcker and Cross-loadings criteria. This chapter discusses the implementation of HTMT in the case of manufacturing small and medium enterprises (SMEs) in Indonesia with the aim of providing an illustration of how the HTMT is capable of detecting the violation of discriminant validity and the solutions that can be carried out following the recommendations given by the originators.

This paper aims to investigate the key enablers and inhibitors that influence the intention to use fitness wearables using the value-based adoption model (VAM).

Data were collected using a structured online questionnaire from 323 respondents who had never used fitness wearables. A purposive sampling technique was used in this study. Smart PLS was employed to test the research framework and hypotheses using a two-step approach.

The findings support some of the hypotheses developed with R² values of 0.622 for perceived value (PV) and 0.567 for intention to use fitness wearable. Perceived enjoyment, perceived social image and perceived usefulness had a positive effect on PV. In addition, health information sensitivity (HIS) was positively related to perceived privacy risk and health information accuracy was positively related to perceived usefulness. Surprisingly, this study did not find any significant relationship between perceived fee, perceived privacy risk, perceived health increase and perceived design aesthetics with PV.

This study's findings can help designers and manufacturers design fitness wearables by considering factors that users find valuable, thus satisfying consumers' needs.

This study tries to model behavioural intention of fitness wearable usage of individual users by using the VAM with the addition of two new antecedences, HSI and health information accuracy, to better explain the behaviour.

Palm oil has consistently been a staple ingredient in the Malaysian diet. Despite various promotional efforts throughout the years, the health aspects of palm oil have often been undervalued, leading consumers to overlook its benefits. This study has two objectives: (1) to explore consumer behaviour in purchasing decisions for food products containing palm oil in an emerging market and (2) to examine consumer awareness of palm oil as an ingredient in various edible products related to health.

A quantitative methodology that utilises a self-administered questionnaire was adopted for data collection. The conceptual framework and hypotheses were tested using partial least squares (PLS) structural equation modelling (SEM) on a dataset of 342 respondents.

The findings revealed that three hypotheses – attitude, subjective norms (SNs) and perceived health benefits – positively impact the intention to purchase palm-oil-based food products. Additionally, results indicate that Malaysian consumers practice sustainable consumption when purchasing palm-oil-based food products.

There is a need for a greater understanding of the importance perceived health benefits have in influencing consumers' consumption of food products containing palm oil in an emerging market such as Malaysia. This research study addresses the gap in existing knowledge.