Partial Least Squares Research Articles

Effects of Red Vinasse on Physicochemical Qualities of Blue Round Scad (Decapterus maruadsi) During Storage, and Shelf Life Prediction

A fish processed with red vinasse is a type of Fujian cuisine with regional characteristics. In order to monitor the effect of red vinasse on storage quality and shelf life of blue round scad (Decapterus maruadsi) during storage, the changes in fat content, thiobarbituric acid reactive substances (TBARS), composition of polyunsaturated fatty acids (PUFAs), pH value, texture, and sensory quality were studied at different storage temperatures (4 °C, 25 °C, and 37 °C). By analyzing the correlation between changes in sensory qualities and physical and chemical indexes, a first-order kinetic model and the Arrhenius equation were used to build a shelf-life prediction model for blue round scad during storage. The results showed that processing with red vinasse can significantly reduce the malondialdehyde (MDA) production and the decrease in PUFAs, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (p < 0.05) during storage. Based on partial least squares regression (PLSR), the storage temperature and time have a significant impact on the PUFA composition in blue round scad, which changed less when the samples were stored at 4 °C and 25 °C, and they had better nutritional composition of fatty acids at lower temperatures. Among the PUFAs, DHA (C22:6n-3) and EPA (C20:5n-3) had higher relative contents and significantly decreased during storage (p < 0.05). Additionally, the processing with red vinasse can slow down the increase in total volatile basic nitrogen (TVB-N) value and pH of blue round scad, maintain the appropriate hardness, elasticity, cohesion and chewability, and improve the overall sensory quality of the fish. In addition, according to the results of model prediction based on TBARS value, the storage shelf life of blue round scad with red vinasse added was 55 d, 2.7 d and 28 h at 4 °C, 25 °C and 37 °C, respectively. The accuracy of the forecast model was high, and the relative errors of the measured values and predicted values were less than 10%. Thus, it not only provided a theoretical basis for the processing and application of red vinasse to Chinese traditional food, but also provided innovative ideas for the safe storage and high-value utilization of blue round scad.

Strategic excellence and technological integration: Sustaining competitive advantage for private universities in Indonesia

The competition among private universities in Indonesia is intensifying, requiring institutions to establish a sustainable competitive advantage through the right strategies, strong vision and mission, and effective use of information technology. This study analyzes the influence of strategy, vision and mission, university rankings, and information technology on the sustainable competitive advantage of private universities. It also examines the moderating role of information technology in strengthening these relationships. Using a quantitative approach with a descriptive and causal design, data were collected from 204 respondents across private universities in Indonesia via a questionnaire. Analysis using Partial Least Squares (PLS) and SmartPLS software reveals that strategy, vision and mission, university rankings, and information technology significantly impact sustainable competitive advantage. Furthermore, information technology strengthens the influence of strategy, vision, and mission but does not moderate the relationship between university rankings and competitive advantage. These findings indicate that while information technology supports strategic implementation and the achievement of institutional vision and mission, its direct role in enhancing university rankings is limited. The study emphasizes the importance of formulating innovative, sustainable strategies and integrating information technology effectively to maintain competitiveness. Practical implications include the necessity of resource planning and investment in technology to achieve strategic objectives. This study contributes to the literature by exploring the moderating role of information technology in private universities, providing valuable insights for managers on leveraging technology to navigate competitive challenges and sustain growth.

Nondestructive Detection of Litchi Stem Borers Using Multi-Sensor Data Fusion

To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R2 of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R2 of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R2 of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection.

STARBUCKS INDONESIA'S BREWING CONTROVERSY : EXPLORING THE CONSUMER BEHAVIOUR AND PRODUCT PERCEPTION IN THE SHADOW OF THE ISRAELI-PALESTINIAN CONFLICT

The main objective of this study is to examine how the role and influence of religiosity, consumer animosity and consumer ethnocentrism on the assessment of Starbucks Indonesia products that affect consumer purchasing behavior after the Israeli-Palestinian conflict broke out again in October 2023. Considering Indonesia is the biggest country with Muslim population in the world, how will it react to such a global brand, the biggest coffee corporation in the world and what drives them. The research using quantitative method with validated questionnaires derived from literature is used for data collection. Data were collected from 250 respondents Starbucks Indonesia consumers who are Muslims. This research using the Structural Equation Modeling (SEM) method with a focus on Partial Least Squares (PLS) for framework analysis. The study attempts to add value and give comprehensive details to the existing literature on consumer behavior, especially the role of religiosity, animosity, and ethnocentrism among consumers in Indonesia. The research emphasizes the importance of understanding consumer behavior in diverse cultural contexts and aims to guide brand managers in developing targeted strategies to navigate potential challenges arising from religiously motivated consumer sentiments in the aftermath of the Israeli-Palestinian conflict. The practical implications suggest that brand managers should adopt nuanced marketing strategies in conflict-affected regions, recognizing the diverse consumer sentiments and tailoring campaigns accordingly. Effective communication and CSR initiatives are crucial for maintaining brand loyalty and trust, with a focus on community engagement and interfaith projects. Additionally, ongoing research and feedback mechanisms are essential to monitor and adapt to the changing consumer sentiments in these regions.

Determination of Sugar Concentrations in Aqueous Solution Using Multivariate Predictions Based on 1H‐NMR Spectroscopy

AbstractRenewable chemicals from carbohydrate‐rich wastes, like furfural and 5‐hydroxymethylfurfural (HMF), are gaining prominence as alternatives to petroleum‐based resources. Assessing the suitability of biomass as feedstock for furfural and HMF production requires knowledge of its composition. This study focuses on developing and validating predictive models for individual sugar concentrations in hydrolysates using quantitative 1H NMR data. Utilizing partial least square (PLS) regression, the dataset includes 137 NMR spectra of multi‐component sugar standards (arabinose, fructose, galactose, glucose, mannose, maltose, sucrose, and xylose). The best‐performing model achieved an R2 of 0.987–0.999 and RMSECV of 0.37–1.56 mM and is based on the non‐overlapping area of the NMR spectrum. Real‐world samples were used for validation, resulting in predicted sugar concentrations with a mean standard deviation of 0.5 mM. This high accuracy and streamlined analysis process make these models practical for quantifying large sample sets, showcasing the reliability and accessibility of extracting statistical information from H‐NMR data.

Prediction of soil organic carbon and total nitrogen affected by mine using Vis-NIR spectroscopy coupled with machine learning algorithms in calcareous soils.

The utilization of visible-near infrared (Vis-NIR) spectroscopy presents a nondestructive, fast, reliable and cost-effective approach to predicting total nitrogen (TN) and organic carbon (OC) levels. This study employed a combination of Vis-NIR spectroscopy, partial least-squares regression (PLSR), and support vector machine (SVM) models to investigate the effects of mining on TN and OC stocks in both the topsoil (0-10 cm) and subsoil (10-40 cm). 105 soil samples were collected from agricultural areas near an iron mine, polluted, moderately-polluted, and non-polluted sites. Results indicated that soils at the non-polluted site had the highest of soil OC stocks (7.5 kg m-2) and total nitrogen (2.5 kg m-2), followed by the moderately-polluted site. Furthermore, it was observed that soils from the polluted site displayed the highest spectral reflectance. The spectral bands in the range of 500-700 nm showed the strongest correlation with soil organic carbon content. Notably, the SVM method utilizing Vis-NIR spectroscopy provided superior predictions for both subsoil and topsoil organic carbon and total nitrogen compared to the PLSR methods. Additionally, SVM demonstrated better performance in predicting topsoil soil organic carbon (R2 = 0.87, RMSE = 0.13%, and RPD = 2.8) and total nitrogen (R2 = 0.91, RMSE = 0.13%, and RPD = 2.4) compared to the subsoil, owing to the larger OM content in the topsoils.

The Impact of Store Atmosphere, Service Quality, and Social Media Marketing on Purchase Decisions: Insight from the Coffee Shop Market (Case Study at Kedai Kopi Distrik Metropolitan Surabaya)

This research aims to identify the influence of store atmosphere, service quality, and social media marketing on purchasing decisions at the Surabaya Metropolitan District Coffee Shop. Using quantitative methods with non-probability sampling techniques through purposive sampling, this research involved 115 respondents aged over 17 years who had bought and visited shops and had social media. Data was collected via Google Form and analyzed using Partial Least Square (PLS). The research results show that store atmosphere, service quality, and social media marketing have a positive influence on purchasing decisions. Consumers consider the design at Kedai Distrik Metropolitan to be quite unique by combining Vintage Chinatown-Sumatra themes. With a unique place and cheap product prices, consumers are happy to visit Kedai Kopi Distrik Metropolitan Surabaya, especially among teenagers.

Comparison between consumer-oriented and laboratory benchtop near-infrared spectrometers for total soluble solids measurement in grape

Portable near-infrared (NIR) spectrometers are gaining interest as a non-destructive tool for fruit quality determination in the decade. In this study, the performance of the portable NIR spectrometer for TSS prediction of grapes was evaluated and compared to the benchtop spectrometer. A total of 105 table grapes were individually measured NIR spectra at short-wavelength (740 – 1070 nm) and long-wavelength NIR region (1000 – 2500 nm) using SCiO and NIRFlex N-500 spectrometers, respectively. Partial least square (PLS) regression analysis was performed on berry spectra from both devices afterward significant wavelengths were identified and used to develop multiple linear regression (MLR) models. The best PLS prediction model was obtained from SNV pretreating spectra for both devices and spectral data acquired from SCiO showed better prediction performance (= 0.854, SEP = 0.452°Brix) than NIRFlex N-500 spectra (= 0.667, SEP = 0.675°Brix). Low predicting ability was gained for the MLR calibration model for both device with an RPD of about 1.70. From the results, a pocket-size spectrometer has the potential to be a non-destructive sorting and screening tool in fruit industries.

The Cultural Roots of Green Stays: Understanding Touristic Accommodation Choices Through the Lens of the Theory of Planned Behavior

Based on the Theory of Planned Behavior (TPB), this study presents and tests an integrated model to investigate how individual cultural values influence tourists’ decisions when selecting sustainable accommodation. This study aims to examine the cultural impact on sustainable accommodation choices from the perspectives of tourists in three culturally distinct countries. Data were gathered from 1855 participants in Spain, Norway, and Lithuania using a questionnaire survey method. The data was analyzed using the Partial Least Squares (PLS) method, with statistical analysis based on Structural Equation Modeling (SEM). This study found that uncertainty avoidance and long-term orientation significantly influence attitude. Additionally, collectivism and power distance notably impact subjective norms, while masculinity affects perceived behavioral control. Tourists’ intentions regarding sustainable accommodation choices were determined to be influenced by attitude, subjective norms, and perceived behavioral control. These findings contribute to the theoretical discussion of sustainable purchasing by emphasizing the intricate role of individual cultural values and provide practical insights for developing marketing strategies that resonate with these values.

The Influence of Budget Planning, Bureaucratic Environment and Budget Evaluation on Budget Absorption with Management Commitment as a Moderator at the Directorate of Yogyakarta State University

This research is research with a quantitative approach carried out at the Directorate of Yogyakarta State University. This research aims to determine whether or not there is an influence of budget planning, bureaucratic environment, budget evaluation on budget absorption with management commitment as a moderator. The data collection method in this research is a questionnaire. The sampling method used was the purposive sampling method. The population that was the object of research was the financial planning and management team at the Directorate of Yogyakarta State University. The sample in this study was 100 members of the Directorate of Yogyakarta State University. Hypothesis testing was carried out with Partial Least Square (PLS) through the SmartPLS 4.0 program. Based on the research results with a significance level of 5%, the conclusions obtained are: (1) There is a positive and significant influence of Budget Planning on Budget Absorption with at statistic of 3.255 and a significant value of 0.001; (2) There is a positive and significant influence of the Bureaucratic Environment on Budget Absorption with at statistic of 2.410 and a significant value of 0.008; (3) There is a positive and significant influence of Budget Evaluation on Budget Absorption with at statistic of 1.688 and a significant value of 0.046; (4) Management Commitment strengthens the positive influence of Budget Planning on Budget Absorption with at statistic of 2.071 and a significant value of 0.019; (5) Management Commitment does not strengthen the positive influence of the Bureaucratic Environment on Budget Absorption with at statistic of 0.534 and a significant value of 0.297; (6) Management Commitment strengthens the positive influence of Budget Evaluation on Budget Absorption with at statistic of 1.685 and a significant value of 0.046.

Mediating effect of self-control on financial literacy and savings behaviour of rural women entrepreneurs

Background: Savings behaviour is crucial for any economic and investment activity. The ability of rural enterprises to save is difficult because of their ignorance of the benefits of savings, low-income levels and inability to commit personal savings, which falls to certain factors that include financial literacy (financial knowledge, financial attitude) and self-control.Aim: The article aims to examine the mediating effect of self-control on financial literacy (financial knowledge, financial attitude) and savings behaviour of rural women entrepreneurs in Mokwa Local Government, Niger State, Nigeria.Setting: The population comprised 340 rural women trained by the Department of Commerce on financial literacy applications in the 11 wards of Mokwa Local Government. A total of 175 respondents were identified using the Krejcie and Morgan sample size table.Methods: Primary quantitative data were collected using structured questionnaires and analysed using partial least square (PLS) path modelling.Results: The empirical results revealed that financial knowledge and financial attitude exerted a positive effect on the savings behaviour of rural women entrepreneurs.Conclusion: The conclusion drawn from the study is that the financial literacy of rural women entrepreneurs is empirically related to how they save.Contribution: Based on the research findings, the study recommends that government agencies supervising and coordinating the activities of entrepreneurs like SMEDAN and also financial institutions like Microfinance Banks should facilitate flexible, understandable and acceptable programmes that will enlighten rural entrepreneurs on the importance of savings for investment and the nation at large.

Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology

Color is a key indicator for evaluating the quality of tea during processing; various processing procedures can significantly affect the content of fat-soluble pigments of tea, which in turn affects the color and quality of finished tea. Therefore, there is an urgent demand for the fast, non-destructive detection of pigments of stacked tea during processing. This paper presents the use of hyperspectral imaging technology (HSI), combined with machine learning algorithms, to detect chlorophyll a, chlorophyll b, and carotenoids in stacked matcha tea during processing. Firstly, a quantitative relationship between HSI data of tea and their pigment contents was developed based on regression analysis, and the results showed that exceptional prediction performance was achieved by the partial least squares regression (PLSR) algorithm combined with the feature band algorithm of competitive adaptive reweighting (CARS), and the Rp2 values of detection models of chlorophyll a, chlorophyll b and carotenoids were 0.90465, 0.92068 and 0.62666, respectively. Then, these quantitative detection models were extended to each pixel in hyperspectral images, achieving point-by-point prediction of pigment components, so the distribution of pigments of stacked tea leaves during processing procedures was successfully visualized on the processing line in situ. By integrating a hyperspectral imaging system into the real-world environment, operators can monitor pigment levels in real time and thus dynamically adjust processing parameters based on real-time data. This study enhances pigment detection efficiency in tea processing, supports process optimization, and aids in quality control.

FACTORS INFLUENCING INVESTMENT DECISION-MAKING IN GENERATION Z

This study aims to provide empirical evidence about the factors that affect investment decision-making in generation Z. The sampling method used is a non-probability purposive sampling method with generation Z students as a research sample. The age range included in the category of generation Z students is those aged 18-26 years. The analysis method used is Partial Least Square (PLS). The results of the study show that financial literacy, attitude towards investment, and risk perception have a significant influence on investment decision-making among Generation Z students. Financial literacy and attitude towards investment have a positive effect, while risk perception has a negative effect on investment decisions. Although herding does not have a direct effect on investment decision-making, it does affect attitudes towards investment, which then influences investment decisions. This shows that the investment trends and investment decisions of others can shape students' investment attitudes, which ultimately influences their decision to invest. Technological advances and broad access to information, particularly through social media, seem to reduce the direct influence of herding but increase the role of influencers in shaping investment attitudes.

Shaping Patient Loyalty through Quality Care: Analyzing Patient Satisfaction in Public Hospitals in Ghana

Public hospitals in Ghana face challenges such as limited resources and inconsistent care delivery, which impact patient satisfaction and loyalty. In this context, enhancing service quality is crucial for retaining patients and improving healthcare outcomes. This study investigates the role of service quality in shaping patient loyalty and explores patient satisfaction as a mediator in selected public hospitals in Ghana using the SERVQUAL model. A survey was conducted among 200 outpatients from five public health facilities in Kumasi, Ghana, between January and February 25, 2024. A quantitative methodology was applied to evaluate how patients' perceptions of service quality influence their loyalty toward public healthcare providers. In order to analyze the data, SPSS and the smart partial least square (PLS) software version 3.0 were used for structural equation modeling (SEM). The findings revealed that communication, the physical environment, and responsiveness significantly affect patient loyalty, whereas factors such as privacy, safety, and a customer-friendly atmosphere do not. Additionally, this study confirmed that patient satisfaction mediates the relationship between service quality and patient loyalty, underscoring the importance of service quality in enhancing both satisfaction and loyalty in health care. Based on these insights, this study recommends that health care institutions improve their service quality to strengthen public healthcare systems. The significance of this paper extends beyond regression analysis, as it also addresses the delayed effects of service quality on patient loyalty, considering both direct and indirect influences in the regression models.

The Impact of Entrepreneurial Leadership, Business Environment, Digital Literacy, and Religiosity on Msme Performance: The Mediating Role of Innovative Behavior in Pekanbaru

This study examines the influence of entrepreneurial leadership, business environment, digital literacy, and religiosity on the performance of micro, small, and medium enterprises (MSMEs) in Pekanbaru, with innovative behavior as a mediating variable. Using a sample of 300 MSME entrepreneurs and analyzed through Partial Least Squares (PLS) Structural Equation Modeling (SEM), the research finds significant direct effects of entrepreneurial leadership (β = -0.204, p = 0.047), business environment (β = 0.225, p = 0.007), digital literacy (β = 0.139, p = 0.022), and religiosity (β = 0.271, p = 0.011) on business performance. The mediating role of innovative behavior is only significant in the relationship between religiosity and business performance (β = 0.162, p = 0.012). The findings emphasize the critical role of religiosity and innovative behavior in improving MSME performance. This research contributes to the growing literature on MSMEs by demonstrating how leadership, environment, digital skills, and religiosity shape performance outcomes.

Impact of Drying on Phytonutritional Compounds, In Vitro Antioxidant Activity and Cytotoxicity of Spiny Saltbush (Rhagodia spinescens)

The Spiny saltbush (Rhagodia spinscens) is a halophyte species with the potential to provide natural ingredients used in food and pharmaceutical industries. In food and pharmaceutical applications, drying is necessary to maintain shelf-life, which reduces phytonutrient content. In this study, changes in the nutritional composition, phenolic and carotenoid profiles of radical antioxidant scavenging activity [(2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)(ABTS)], antioxidant power [ferric reducing antioxidant ability assay (FRAP)], and cytotoxicity of freeze- and oven-dried (55 °C for 24 h) spiny saltbush were determined. Sodium (4.72 g/100 g dry weight (DW), potassium (6.86 g/100 g DW), calcium (4.06 g/100 g DW), zinc (372 mg/kg DW) and protein content were higher in oven-dried samples than freeze-dried samples. Ultra-performance liquid chromatography–mass spectrometry analysis detected 18 metabolites in saltbush extracts. Partial Least-Squares Discriminant Analysis, Hierarchical Cluster Analysis, and Variable Importance in Projection discriminated between freeze-dried and oven-dried samples. Freeze-dried samples retained more individual metabolites than oven-dried samples, while oven-dried samples had higher antioxidant activity (ABTS and FRAP), lutein, trans-β carotene, and cis-β-carotene. Correlation analysis identified potential antioxidant candidates between phenolic and carotenoid compounds. Neither freeze-dried nor oven-dried spiny saltbush samples showed cytotoxicity. The study uncovered changes in phytonutritional compounds after the oven and freeze-drying spiny saltbush, a potential salt alternative and functional ingredient for the food industry.

Analyzing the Impact of the Knowledge Management Process on the Banking Sector Performance: By Using the Partial Least Square Method

ABSTRACTThe essence of this investigation was to elucidate the impact of knowledge management practices (KMP)—namely knowledge storage (KS), knowledge creation (KC), and knowledge application (KA)—on the performance dynamics of the banking sector in Yemen. Leveraging the partial least squares (PLS) method for analysis, this study scrutinized responses from 380 employees across Yemeni banks, revealing a significant and positive correlation between these knowledge management dimensions and the sector's performance metrics. Key to this study's findings is the assertion that KMP serves as a cornerstone for bolstering efficiency and competitiveness within Yemen's banking institutions. Notably, this research brings to light the pivotal roles played by both human capital and technological advancements in optimizing knowledge management processes. These factors collectively facilitate the sector's adaptability and responsiveness to the ever‐evolving financial landscape.

Feasibility Study of Label-Free Raman Spectroscopy forParathyroid Gland Identification.

We aim to evaluate the feasibility of Raman spectroscopy for parathyroid gland (PG) identification during thyroidectomy. Using a novel side-viewing handheld Raman probe, a total of 324 Raman spectra of four tissue types (i.e., thyroid, lymph node, PG, and lipid) commonly encountered during thyroidectomy were rapidly (< 3 s) acquired from 80 tissue sites (thyroid [n = 10], lymph node [n = 10], PG [n = 40], lipid [n = 20]) of 10 euthanized Wistar rats. Two partial least-squares (PLS)-discriminant analysis (DA) detection models were developed, differentiating the lipid and nonlipid (i.e., thyroid, lymph node, and PG) tissues with an accuracy of 100%, and PG, lymph node, and thyroid could be detected with an accuracy of 98.4%, 93.9%, and 95.4% respectively. This work demonstrates the feasibility of Raman spectroscopy technique for PG identification and protection during thyroidectomy at the molecular level.

Early Modeling of the Upcoming Landsat Next Constellation for Soybean Yield Prediction Under Varying Levels of Water Availability

The upcoming Landsat Next will provide more frequent land surface observations at higher spatial and spectral resolutions that will greatly benefit the agricultural sector. Early modeling of the upcoming Landsat Next products for soybean yield prediction is essential for long-term satellite monitoring strategies. In this context, this article evaluates the contribution of Landsat Next’s improved spectral resolution for soybean yield prediction under varying levels of water availability. Ground-based hyperspectral data collected over five cropping seasons at the Brazilian Agricultural Research Corporation were resampled to Landsat Next spectral resolution. The spectral dataset (n = 384) was divided into calibration and external validation datasets and investigated using three strategies for soybean yield prediction: (1) using the reflectance from each spectral band; (2) using existing and new vegetation indices developed based on three general equations: Normalized Difference Vegetation Index (NDVI-like), Band Ratio Vegetation Index (RVI-like), and Band Difference Vegetation Index (DVI-like), replacing the traditional spectral bands by all possible combinations between two bands for index calculation; and (3) using a partial least squares regression (PLSR) model composed of all Landsat Next spectral bands, in comparison to PLSR models using Landsat OLI and Sentienel-2 MSI bands. The results show the distribution of the new spectral bands over the most prominent changes in leaf reflectance due to water deficit, particularly in the visible and shortwave infrared spectrum. (1) Band 18 (centered at 1610 nm) had the highest correlation with yield (R2 = 0.34). (2) A new vegetation index, called Normalized Difference Shortwave Vegetation Index (NDSWVI), is proposed and calculated from bands 19 and 20 (centered at 2028 and 2108 nm). NDSWVI showed the best performance (R2 = 0.37) compared to traditional existing and new vegetation indices. (3) The PLSR model gave the best results (R2 = 0.65), outperforming the Landsat OLI and Sentinel-2 MSI sensors. The improved spectral resolution of Landsat Next is expected to contribute to improved crop monitoring, especially for soybean crops in Brazil, increasing the sustainability of the production systems and strengthening food security in Brazil and globally.

Gen-Z Entrepreneurial Intentions: Exploring the Impact of Risk, Achievement Needs, and Social Media Engagement

This research explores the relationship between risk-taking propensity, need for achievement, social media engagement, attitudes toward entrepreneurship, and entrepreneurial intention among Gen-Z entrepreneurs in Banyumas Purwokerto. Gen-Z, known for their digital fluency and innovative outlook, presents unique insights into entrepreneurial intention, making them a compelling focus for this study. Using Structural Equation Modeling (SEM) with the Partial Least Square (PLS) approach, selected for its ability to handle complex models with mediating effects, the study surveyed 150 participants. Findings reveal that risk-taking propensity, need for achievement, and social media engagement significantly and positively influence entrepreneurial intention. Additionally, attitudes toward entrepreneurship mediate these effects, highlighting the role of mindset in translating individual characteristics into entrepreneurial ambitions. Practical implications suggest that educators, policymakers, and business mentors can enhance entrepreneurial intention by fostering supportive environments that nurture these traits and positively shape attitudes toward entrepreneurship among Gen-Z.