This study aims to assess the success of implementing Lean Manufacturing (LM) practices in food companies in Indonesia through the mediating role of Manufacturing Agility Competencies (MAC) to improve Operational Performance (OP). The method used is survey method. The study uses a positivist paradigm to see the effect of Lean manufacturing on operational performance. Testing used Structural Equation Modeling (SEM) by the Partial Least Square (PLS) approach. Finding of this study reveals that lean manufacturing practices have no positive effect on operational performance in Indonesian food industries. Meanwhile, the mediating role of Manufacturing Agility Competencies has succeeded in increasing the effect of Lean Manufacturing on operational performance. The originality of the research lies in the process of forming Manufacturing Agility Competencies as a strategy with a long process based on the experiences and observations made in food companies in Indonesia.

The accurate and efficient classification of leukemia images is crucial for early diagnosis and effective treatment planning. Traditional methods often face challenges in handling the complexity and variability of medical images. To address these challenges, we propose a novel approach that leverages the Gray Level Co-occurrence Matrix (GLCM) and statistical feature-based segmentation techniques. In this paper, we present a comprehensive framework for the automated classification of leukemia images using advanced image processing techniques. The methodology involves six key stages: input of leukemia images, preprocessing to enhance image quality, segmentation to isolate relevant features, feature extraction using texture analysis, classification using multiple distance metrics Euclidean, Manhattan, Canberra, and Chebyshev, and performance evaluation. Our results demonstrate significant improvements in classification accuracy, sensitivity, specificity, and error rates across various metrics and feature sets. For instance, using the Chebyshev distance, we achieved an average accuracy of 82.69%, sensitivity of 85.95%, and specificity of 82.77%. The Canberra distance provided optimal performance with 65 features, yielding an accuracy of 85.18%, sensitivity of 86.39%, and specificity of 86.31%. These findings underscore the efficacy of our approach in distinguishing between healthy and leukemic cells, thereby contributing to early diagnosis and effective treatment planning for leukemia.

Recently, Lean Six Sigma (LSS) has attracted much attention from both researchers and practitioners. In fact, many published papers used a quantitative research methodology to build and test theory in the field of LSS implementation in manufacturing area. Very few studies have been reported about the critical success factors influencing of six sigma implementations. The aim of this study is to present the results, from an empirical evidence, of Six Sigma implementation in Tunisian manufacturing. Therefore, fourteen main factors have been identified through literature review and questionnaire survey. Interpretive structural modeling (ISM) has been used to develop a hierarchical structure for analyzing the interactions among the key factors. By using the MICMAC technique, the barriers were classified into four groups according to their driving power and dependence power. Our finding provides important insight to managers and practitioners about factors in the LSS implementation process.

The Kingdom of Saudi Arabia has witnessed a significant surge in online shopping in recent years, fueled by factors like growing internet penetration, smartphone adoption, and government initiatives supporting e-commerce growth. This rise in online activity has led to a corresponding increase in the utilization of online courier services, playing a crucial role in ensuring timely and efficient delivery of goods In this context, understanding public perception of online courier services becomes crucial for businesses to improve their offerings, address customer concerns, and maintain a competitive edge. Social media platforms have emerged as a valuable source of customer feedback and user-generated content, offering insights into customer experiences and opinions. This paper presents a sentiment analysis on online couriers in Saudi Arabia using natural language processing techniques combined with Decision Tree and Support Vector Machine (SVM) classifiers of machine learning. A dataset on customers’ sentiments was created by a crawling process from X social media. Both classifiers perform well, with Decision Tree classifier performs slightly better on accuracy, i.e. 95.01% compared to 93.60% of the Support Vector Machine. Other metrics support the robustness of the classification.

The rice industry policy must be comprehensive with a scope of policy from upstream to downstream. In other words, it must cover all supply chains of the rice industry consisting of five levels, such as, the level of farmers who process rice, grain traders, rice traders, rice traders in production areas and rice traders in urban markets.The purposes of this research are (1) to identify and model the current supply chain system of the rice industry; (2) analyze the simulation of the rice industry supply chain system policy; and (3) recommend rice industry policies which provide incentives for all rice industry supply chain actors. The research method used is the case study method. The aim is to understand a phenomenon in the rice industry supply chain system in depth in West Java Province – Indonesia. The results show that the rice industry supply chain system model is a closed cycle consisting of material flow feedback in the form of grain, rice, money and information flow in the form of demand that occurs in the interaction of actors from farmers, grain traders, rice milling units (RMU), rice traders in production centers to rice traders in urban wholesale markets in Bandung and Jakarta. Every businessman in the rice industry has the same goal, which is to maximize the profits. Thus, it can potentially lead to a conflict of interest which is manifested in the desire of every businessman to sell as much as they produce at the highest possible price but this will not happen because of the limited resources they have, such as capital and the market demand they receive. The strategy of increasing production is often carried out by the government at this time which it cannot be done partially without considering other rice industry supply chain instruments. The impacts of this partial policies are farmers, collectors, rice traders and rice mills gain unstable profits and the benefits received are lower than before the production strategy and policy were implemented. The recommended policy strategy is the rice industry supply chain system should be able to guarantee the availability of sufficient rice and capable of guaranting the welfare of farmers with a policy that integrates rice farming and agro-industry production strategies, financing strategies and accessible to all levels of actors in the rice industry supply chain, human resource development strategies and rice business risk management strategies, simultaneously. The integrated policy strategy needs to be carried out since rice supply chain management is a coordinating system of material flow in the form of grain, rice, money, facilities and information flow in the form of orders or requests, knowledge and innovation.

Augmented reality (AR) has garnered considerable interest for its potential to motivate engagement and advance customer-brand interactions. This study explored the impact of reality congruence (RC) of AR-menus on brand information sharing (BIS) in fast-food restaurants, particularly among Generation Z (Gen Z), as well as the mediating effects of usefulness of AR menu (UAR) and brand positivity (BP) on this relationship. Media Richness Theory (MRT) was employed as the theoretical umbrella for developing the study model. To validate the research model, we employed structural equation modeling (PLS-SEM) with a sample of 209 respondents. The results demonstrate that reality congruence of AR menus is a relevant predictor of Gen Z members’ behavior in sharing information about the brand. Furthermore, this relationship was mediated by the UAR and BP. The findings also demonstrated that UAR and BP had a sequential mediating effect on the relationship between the RC of AR-menu and BIS. This revolutionary study revealed that RC of AR-menu in restaurants fosters positive behaviors in fast food settings. By highlighting AR's potential to create engaging dining experiences for Gen Z members, this study offers valuable insights for service businesses, marketing managers, and the hospitality industry. Addressing this gap in existing research emphasizes the importance of adopting innovative technologies to enhance Gen Z's customer experience and engagement in the restaurant industry.

This study aimed to determine the impact of digital transformation (DT) and digital accounting (DA) on the financial reports quality (FRQ) in companies listed in the Saudi capital market. Data were collected using a questionnaire distributed via email. The research sample consisted of 116 individuals, including accountants and executive directors in companies listed. The study adopted the smart PLS method to test hypotheses. The study found that there is an incomplete positive relationship between digital transformation and the quality of financial reports through the positive impact on the characteristics of relevance and understandability, as well as the existence of a complete positive relationship between digital accounting and the FRQ through the impact on all characteristics of accounting information.

Sentiment analysis of students’ feedback using machine learning algorithms has emerged as a valuable tool for understanding students’ sentiments and improving educational outcomes. Currently, existing systems use frequency-based methods for feature selection (e.g., Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW)) not to capture the subtleties of emotions expressed in student feedback and do not provide insights into the specific concerns of students via topics or themes. In this study, we propose the Student Sentiment from Feedback (SSF) framework, which includes four main procedures: pre-processing, feature selection, classification, and theme finding. The SSF framework classifies student sentiments and subsequently groups feedback into themes using semantic networks based on word co-occurrence. Our innovative feature selection approach combines TF-IDF with sentiment-based features derived from SentiWordNet and intensifiers, creating a robust feature vector that enhances the dataset’s richness and improves classification accuracy and robustness. In the experiments, we utilize a public dataset from Kaggle, applying our proposed method and various machine learning models (e.g., k-nearest neighbor, decision tree, random forest, multilayer perceptron, support vector machine, gradient boosting, and extreme gradient boosting). The experimental results show that our concatenated features achieve the highest accuracy across all machine learning models (greater than 0.82). Our study demonstrates the efficacy of this hybrid feature selection method, contributing to better understanding and decision-making in educational settings.

Management practitioners often assume that raises in the contractual absolute maximum bandwidth of Internet connections increase the attitudinal bonds of customers with their access provider. The present study challenges this perspective: In a sample of up to 752 German-speaking Internet users with a wireline Internet access at home, it explores two relative speed characteristics of broadband connections (customer quality experiences in terms of delivered transmission speeds (1) relative to the maximum down- and upload bandwidths as per customer contract, and (2) relative to speeds emphasized in advertisements of a consumer’s access provider) as potential determinants of customer affinity with an access provider. Furthermore, correlations are analyzed between the frequency with which customers use speed tests to check the quality of their wireline Internet connection and their attitudinal bonds with their access provider. Multivariate regression analysis reveals that the impression that advertised wireline access bandwidths are exaggerated compared to the speeds actually delivered and – to a lesser extent – the perception that the contractual maximum speed is reached only in a low share of a customer’s Internet sessions significantly decrease customer satis-faction and significantly increase customer inclination to churn. The effects of the two speed assessments are not independent of one another: Perceived speed exaggerations in a provider’s ads impair customer bonds less (more) strongly if the experienced share of Internet sessions with speeds below the contractual maximum bandwidth is high (low). More frequent speed testing reduces customer bonding, especially in case that advertised speeds of a customer’s provider are experienced as overblown.

The purpose of this study is to examine the influences of self-identity, subjective norm and attitude on the intention to purchase luxury fashion goods. It also demonstrates how purchase intention influences on customers' intention to pay premium prices for luxury fashion goods. A theoretical model is established and assessed in this study on a sample of luxury fashion consumers in Malaysia. By means of a self-structured questionnaire, 240 valid submissions are included for data analysis with the Smart-PLS software 3.2.0. The results support all hypotheses on the correlation of self-identity, subjective norm and attitude with purchase intention and price premium, except the correlation between self-identity and purchase intention. This is the first research paper investigating self-identity as an antecedent of purchase intention, and price premium as a consequence of purchase intention. It also justifies the behavior of luxury fashion consumers with regards to luxury fashion products. Findings from this study may help marketers develop effective strategies which would yield them a competitive advantage within the luxury fashion market.