Simple Regression Method Research Articles

THE EFFECTS OF WORK ENGAGEMENT ON KALEIDOSCOPE CAREER MODELS

The Kaleidoscope Career Model has emerged as a novel framework within the realm of contemporary career approaches. Within the evolving landscape of the workforce, it offers significant analytical opportunities for individuals to manage their careers more effectively. Comprising parameters of authenticity, balance, and challenge, the Kaleidoscope Career Model reflects a dynamic interplay influenced by career stage, gender, and life circumstances, akin to the ever-changing patterns of a kaleidoscope. The aim of this study is to analyze the influence of employees' levels of work engagement on the Kaleidoscope Career Model and its parameters. Additionally, the study seeks to contribute to the literature on the Kaleidoscope Career Model. The sample for this research consists of employees working in call centers in Trabzon (n=393). Correlation analysis was employed to assess relationships, while simple regression methods were used to evaluate effects. These analyses were conducted using the SPSS package program. The findings reveal that work engagement positively and significantly influences the Kaleidoscope Career Model, balance, and challenge parameters, while negatively impacting authenticity. Another key finding indicates that in periods of high work engagement, the challenge parameter predominates, whereas during periods of low work engagement, the authenticity parameter takes precedence.

Energy landscapes of peptide-MHC binding.

Molecules of the Major Histocompatibility Complex (MHC) present short protein fragments on the cell surface, an important step in T cell immune recognition. MHC-I molecules process peptides from intracellular proteins; MHC-II molecules act in antigen-presenting cells and present peptides derived from extracellular proteins. Here we show that the sequence-dependent energy landscapes of MHC-peptide binding encode class-specific nonlinearities (epistasis). MHC-I has a smooth landscape with global epistasis; the binding energy is a simple deformation of an underlying linear trait. This form of epistasis enhances the discrimination between strong-binding peptides. In contrast, MHC-II has a rugged landscape with idiosyncratic epistasis: binding depends on detailed amino acid combinations at multiple positions of the peptide sequence. The form of epistasis affects the learning of energy landscapes from training data. For MHC-I, a low-complexity problem, we derive a simple matrix model of binding energies that outperforms current models trained by machine learning. For MHC-II, higher complexity prevents learning by simple regression methods. Epistasis also affects the energy and fitness effects of mutations in antigen-derived peptides (epitopes). In MHC-I, large-effect mutations occur predominantly in anchor positions of strong-binding epitopes. In MHC-II, large effects depend on the background epitope sequence but are broadly distributed over the epitope, generating a bigger target for escape mutations due to loss of presentation. Together, our analysis shows how an energy landscape of protein-protein binding constrains the target of escape mutations from T cell immunity, linking the complexity of the molecular interactions to the dynamics of adaptive immune response.

Impact of a pilot mHealth intervention on treatment outcomes of TB patients seeking care in the private sector using Propensity Scores Matching-Evidence collated from New Delhi, India.

Mobile health applications called Digital Adherence Technologies (DATs), are increasingly used for improving treatment adherence among Tuberculosis patients to attain cure, and/or other chronic diseases requiring long-term and complex medication regimens. These DATs are found to be useful in resource-limited settings because of their cost efficiency in reaching out to vulnerable groups (providing pill and clinic visit reminders, relevant health information, and motivational messages) or those staying in remote or rural areas. Despite their growing ubiquity, there is very limited evidence on how DATs improve healthcare outcomes. We analyzed the uptake of DATs in an urban setting (DS-DOST, powered by Connect for LifeTM, Johnson & Johnson) among different patient groups accessing TB services in New Delhi, India, and subsequently assessed its impact in improving patient engagement and treatment outcomes. This study aims to understand the uptake patterns of a digital adherence technology and its impact in improving follow-ups and treatment outcomes among TB patients. Propensity choice modelling was used to create balanced treated and untreated patient datasets, before applying simple ordinary least square and logistic regression methods to estimate the causal impact of the intervention on the number of follow-ups made with the patient and treatment outcomes. After controlling for potential confounders, it was found that patients who installed and utilized DS-DOST application received an average of 6.4 (95% C.I. [5.32 to 7.557]) additional follow-ups, relative to those who did not utilize the application. This translates to a 58% increase. They also had a 245% higher likelihood of treatment success (Odds ratio: 3.458; 95% C.I. [1.709 to 6.996]).

Rainfall Trend Analysis in Mysuru and Bengaluru Districts of Karnataka, India

Climate change is one of the most pressing challenges facing humanity today, with temperature, precipitation, and runoff being key indicators closely linked to its impact. Understanding the spatial variability and temporal trends of rainfall is crucial for effective water resource management and agriculture. This study focuses on analyzing rainfall data from the Mysuru and Bengaluru districts, located in the Southern and Eastern dry zones of Karnataka, respectively. These regions play a pivotal role in the state's agricultural development and overall economic growth. We utilized daily precipitation records from two meteorological stations, spanning the period from 1983 to 2018, to analyze rainfall trends on monthly, seasonal, and annual scales. To assess these trends, we applied both parametric and non-parametric statistical methods, including the simple regression method, Mann-Kendall test, and Sen’s slope estimator. The Mann-Kendall test was used to detect significant trends in the precipitation time series, while Sen’s slope estimator quantified the magnitude of these trends. Bengaluru had higher average annual rainfall (928.81 mm) compared to Mysuru (681.65 mm), with excess rainfall in 2005 and a positive Pre-Monsoon trend (+3.543 mm/year), while Mysuru experienced maximum rainfall deficit in 2016 and a negative Monsoon trend (-3.238 mm/year). Neither district showed a significant trend in annual rainfall. The findings of this trend analysis are essential for informed water resource planning and management in the region.

Analysis of MSME Loans on Economic Growth in Central Sulawesi in 2015-2022

The role of small and medium enterprises in Central Sulawesi during 2015-2022 in creating added value continues to increase, thus MSMEs are considered to have a strategic role in helping reduce unemployment and poverty. The first role of MSMEs in the economy of Central Sulawesi is as a means of leveling the economic level of society, especially the lower middle class. The aim of th is research is to analyze the influence of MSME credit on economic growth in Central Sulawesi. This research uses a simple regression method. Based on research results, the increase in Gross Regional Domestic Product (GRDP) and economic growth in Central Sulawesi in 2015-2022 has a significant positive effect on the amount of MSME credit. According to statistical tests the amount of MSME credit contribute significantly by 0,000 or less than the 5% significance level. This shows that the distribution of MSME credit will help MSME players to develop their businesses, increase the number of workers, which will also increase output per business unit and per worker so that it will have an impact on economic growth.

The Influence of Credit Risk Using Derivative Financial Instruments on the Performance Commercial banks

The research aims to reveal the influence of credit risks using Derivative Financial Instruments (DFIs) on the financial performance of commercial banks in Iraq. To achieve the research aim and test its main hypothesis, a questionnaire is distributed to listed commercial banks in the Iraq Stock Exchange and that apply DFIs. The researcher used the questionnaire as the main tool in collecting data. Financial data of the variables of the study are analysed by a simple regression method, the arithmetic mean(AM) and standard deviation. The study used the statistical program (SPSS ) to analyse the data. The research sample consisted of (70) questionnaire forms that were distributed and (68) valid forms were recovered. Research results show that using DFIs reduces exposure to interest rate risks, exchange rates and price fluctuations. The financial crisis is the result of the excessive use of financial derivatives outside regulatory restrictions and securitization to collect liquidity. Financial derivatives represent the fastest-growing form of financial transactions and continue to grow even in crisis conditions. DFIs help to identify risks associated with economic losses incurred by the economic unit as a result of weaknesses in the management and operation of derivatives contracts. Accounting profit-based performance indicators, such as return on assets, return on property rights, earnings per share and cash flows per share, contribute to the evaluation of performance, as well as the use of DFIs contribute to increasing profits. The study shows that credit risks using DFIs have a significant positive influence on performances financial in commercial banks in Iraq. Paper type: Research paper.

The Role of Smartphone Addiction on Bed Procrastination and Mindful Eating Behavior in Adolescents

Excessive smartphone use causes the behavior of delaying bedtime and decreasing mindful eating in teenagers. The aim of this research is to find out and explain how much the risk of smartphone addiction plays a role in increasing bed procrastination behavior and decreasing mindful eating in adolescents. There were 192 participants involved in the research with the sampling technique used was convenience sampling. Participant characteristics include teenagers aged 18 – 21 years and have a cut off score for smartphone addiction. Each participant filled out the Smartphone Addiction Scale Short Version for Adolescents, Bedtime Procrastination Scale, and Mindful Eating Questionnaire questionnaires. Data were analyzed using a simple regression method. Based on the results of the regression test, it was found that the risk of smartphone addiction played a 6.1% role in increasing bed procrastination behavior and a 13.3% role in reducing mindful eating behavior. The results of this study also found other factors that play a role in procrastination behavior at bedtime and mindful eating in teenagers besides excessive smartphone use. Therefore, it is hoped that future research will be able to consider and look at other factors besides the risk of smartphone addiction that play a role in bed procrastination and mindful eating.

Digital competence of Vietnamese citizens: An application of digcomp framework and the role of individual factors

AbstractAssessment of digital competence for citizens could support the Education Action plan at a national level, a regional level, or even at a global level to enhance the digital skills and competence gap for work and life for digital transformation. This study applies the DigComp framework through a self-administered online survey to 723 citizens in Vietnam, using a sequential exploratory mixed methods research design. Data were analysed using factor analysis, standard descriptive statistics, and simple regression methods, followed by qualitative content analysis from interviews with ten respondents and a focus group discussion with eight stakeholders on digital competence. The results confirmed the validity of the DigComp framework for measuring the digital competence of Vietnamese citizens. Overall, the proficiency level of the 723 surveyed Vietnamese citizens falls between basic and intermediate levels, being more advanced in areas in information and data literacy and communication and collaboration, compared to digital content creation, safety, and problem solving. The results further showed that male citizens with a higher education level, working in the public sector, or living in province with high provincial digital transformation index rankings perceived themselves to be more digital competent than females with a lower educational level, working in the private sector, or living in the province with low provincial digital transformation index rankings. To improve citizens’ digital competence, training should be designed that considers not only citizens’ digital competence profiles but also their individual characteristics.

Deducing solute differential heat capacity from experimental solubilities. An exemplified treatment of ascorbic acid to improve solubility prediction

ContextSolubility prediction based on the general solubility equation (GSE) rests on reliable values for the isobaric heat capacity difference ΔCp,1 of the solid solute. Usually, this value is estimated with either zero or the melting entropy ΔS1(Tm,1) or, in few cases, is extrapolated from data of thermally stable melts of the solute. This causes uncertainties in the prediction. ObjectiveTo improve prediction accuracy a simple regression method is proposed that determines ΔCp,1 from measured solubilities. Materials and methodsPublished experimental solubilities in neat organic solvents at 298 K of a model compound (L-(+)-ascorbic acid (LAA)) have been regressed using the GSE together with the Hansen parameter model for the activity coefficient. Results and discussionRegression yielded ΔCp,1 = 238 J∙mol−1∙K−1 which agrees well with cross-validation results and is consistent with estimates from various group contribution methods. It was found that prediction accuracy improved in the order of increasing ΔCp,1, that is, from 0, via 91 (=ΔS1(Tm,1)) to 238 J∙mol−1∙K−1. It could be shown that mole fraction solubility of LAA can be forecast this way with an accuracy within current inter-laboratory variation. ConclusionThe proposed method shows a general way to improve prediction accuracy of activity coefficient based solubility models by determining ΔCp,1 without resorting to common assumptions. The method is universally applicable and easy to implement.

Employee Benefits and Employee Retention in Commercial Banks in Akwa Ibom State, Nigeria

This study investigated employee benefits and employee retention in commercial banks in Akwa Ibom State, Nigeria. The survey research design was adopted and this involved administration of copies of questionnaire on employees of case banks who served as respondents. The study’s population was 497 while the sample size was 384 determined using Cochran’s sample size determination formula. The sampling method employed in the study was random sampling. The questionnaire was used in data collection. Data obtained in the study were analyzed using simple regression method. The outcome of this research showed that employee benefits (Beta =0.184, t=2.921, p<0.05) has a significant influence on employee retention in commercial banks in Akwa Ibom State. Employee benefits can determine employee retention in commercial banks in Akwa Ibom State.The study makes contribution in the area of employing employee benefits in retaining the services of high performing and experienced employees in banks. The limitation of this study is that only one state was considered in the survey

Training and Development and Employee Retention in Commercial Banks in Akwa Ibom State, Nigeria

This study focused on training and development and employee retention in commercial banks in Akwa Ibom State, Nigeria. Primarily, the study was conducted to investigate the influence of training and development on employee retention in select banks. The survey research design was adopted and this involved administration of copies of questionnaire on employees of case banks who served as respondents. The study’s population was 497 while the sample size was 384 determined using Cochran’s sample size determination formula. The sampling method employed in the study was random sampling. The questionnaire was used in data collection. Data obtained in the study were analyzed using simple regression method. Result of the analysis indicated that training and development (Beta =0.093, t=2.5833, p<0.05) has a significant influence on employee retention in commercial banks in Akwa Ibom State. It was concluded that training and development can make significant contributions to employee retention in commercial banks in Akwa Ibom State. It was recommended that commercial banks in the state should prioritize employee training and development in order to motivate their employees’ retention.

Religion and Poverty Alleviation in South Sulawesi: Analysis at the Village Level

The influence of religion on poverty reduction has become an exciting study in various countries. Several studies have proven no relationship between religious observance and poverty alleviation. However, on the contrary, other studies have proven that religion affects poverty alleviation. In fact, religion should be able to help overcome poverty. Based on this phenomenon, this study will use raw data of Village Potential (Podes) data issued by BPS in 2018 for South Sulawesi Province, with around 3000 villages. The dependent variable of this study is the number of poor people in each village. Poverty data is taken from the number of poverty certificates issued by the village. This data explains the willingness of people to be considered poor, so that it can show voluntary poverty. Meanwhile, the independent variables include the number of religious places of worship, the number of educational institutions, the number of health institutions, the number of electricity customers, and the number of small-scale industries in the village. This study will use a simple regression method to find the relationship between the variables studied. Even though econometrically many variable relationships are not statistically significant which may be due to the completeness and abnormal distribution of the data, there are interesting variable relationships from this study. The relationship between religion and poverty is negative, indicating that religion can reduce poverty. Community-based health services (posyandu) can also reduce poverty. Markets that show economic aspects will also have an impact on reducing poverty. The same thing is technology proxied with electricity customers will also reduce poverty. Meanwhile, the geographic variable proxied by the distance between the village and the sub-district is not significant and the direction is not as expected.
 Keywords: religion, poverty, South Sulawesi

Assessing Safety Culture Metrics Influencing Organisation Risk Scorecard among Selected Manufacturing Firms in Lagos, Nigeria

This study evaluated the effects of safety culture metrics on risk scorecard among manufacturing firms in Lagos State. The study employed a descriptive survey research design; cum multiphase sampling techniques comprised of judgmental and convenience. Thus, a structured questionnaire of two hundred and eighty-two (282) were gathered from selected manufacturing firms in Lagos State. The statistical techniques adopted were simple frequency percentage, Friedman rank test, and simple regression method. While rank order tests were conducted for both safety culture and risk score metrics, positively low relationship was established between safety culture and risk scorecard among selected manufacturing firms in Lagos State. On recommendations, firms should endeavour to scale and underwrite in promoting vigorous safety cultures that permit for safety performance. The integration of safety culture and risk scorecard metrics should be yearned for to enhance organisational capabilities and ensure survival. Ensuring safety participation, priority, control, engagement, and communication will help organisation in building robust capacities to advance operational efficiency and quality.

Assessment of Bohme surface abrasion resistance of natural stones by simple and nonlinear multiple regression methods

Natural stones are widely used in the construction sector and the physico-mechanical features are very important for them in terms of areas of use. Abrasion is an important phenomenon for causes to decrease in aesthetical and mechanical properties. The Bohme surface abrasion (BSA) test is the widely used test method to determine abrasion resistance, but it is time-consuming, very difficult, and expensive to apply. Because of such difficulties, this study has been conducted to indirectly determine the BSA value of natural stones by some practical methods. For this purpose, BSA, Point load index (Is50), Shore Schlerescope hardness (SSH), Brinell hardness (HB), Rock impact hardness number (RIHN), Coefficient of rock strength (CRS), Crushability index (CI), Impact strength index (ISI), Water absorption (WA), Unit weight (UW), and Porosity (Pg) properties of a total of up 119 rocks, mainly of Turkish rocks, were determined. The main objective of this article is to develop some estimation models for the BSA of different rocks. Two different methods have been utilized in the modeling of data, including simple regression analysis (SRA), and nonlinear multiple regression analysis (NMRA). In addition to the SRA performed using all data sets, the SRA analysis was deepened by classification according to origin, porosity, and strength. A large number of estimation equations were obtained by NMRA performed by applying three different models. The actual BSA values obtained from the laboratory study and the BSA values obtained from the models analyzed with SPSS software were compared using ANOVA analysis and various graphics. The most successful SRA equations derived using the entire dataset were obtained with HB, Is50, and SSH parameters, respectively. In general, there is a significant correlation between various rock properties and BSA, except CRS and CI. For NMRA models constructed with at least three independent variables, the highest predictive power belongs to the equations using (Is50 / HB / UW), (Is50 / HB / CRS), and (Is50 / UW / WA / Pg) parameters, respectively. Rock abrasion has a complex nature. The results of this study strongly support that it is an accurate approach to seek a solution to this complex problem by using parameters such as hardness, strength, and physical properties in the same model.

Leading Risk Factors for Diseases of the Circulatory and Musculoskeletal Systems in Metallurgical Workers

Introduction: It is important to assess health impact of the entire set of heterogeneous risk factors and identify the key ones in order to develop personalized measures for prevention of non-occupational diseases in workers. Objective: To establish the major risk factors for circulatory and musculoskeletal disorders in employees of a metallurgical enterprise. Materials and methods: We used anonymized data from a special assessment of working conditions and production control of the forging departments of the forging-rod and forging-press shops of a metallurgical enterprise producing titanium alloys for the years 2016–2020. To determine the presence of personal biological and behavioral risk factors for diseases of the circulatory and musculoskeletal systems, we examined anonymized data of the periodic medical check-up conducted in 2020 of 146 male blacksmiths working with hammers and presses. The mean age of the workers was 35.9 ± 8.8 years (range: 21 to 57 years) and their mean length of current employment was 18.9 ± 9.7 years (range: 2 to 41 years). The method of simple and multiple logistic regression was used to build models for predicting the likelihood of a disease; odds ratios were calculated with a 95 % confidence interval. The quality of the models was assessed using the maximum likelihood estimation. Results: We determined the factors allowing prediction of the disease likelihood. The prevalence of circulatory diseases correlated with age, length of employment, and the body mass index, whole-body and hand-arm vibration, noise exposure, high ambient temperature, thermal radiation, and heavy physical work. The prevalence of musculoskeletal diseases, in its turn, had a somewhat weaker statistically significant association with the same risk factors. Models predicting the likelihood of circulatory and musculoskeletal diseases have been built. The best predictive model for circulatory diseases included the combined effect of the body mass index and heavy physical work while that for musculoskeletal disorders – of the body mass index and hand-arm vibration. Conclusion: Multiple logistic regression used to analyze statistical relationships between work-related risk factors and disease prevalence in employees in specific occupations allowed identification of the leading factors contributing the most to the disease development and can be recommended for solving practical problems in occupational medicine.

New methods of coalbed methane production analysis based on the generalized gamma distribution and field applications

Decline curve analysis is a simple and fast regression method of estimating recoverable reserves and future performance, but it is challenged when applied to unconventional reservoirs. Based on a generalized Gamma distribution in the real number field, a theoretical forecasting model describing the whole-process change of coalbed methane well production was constructed, that is, the gas production can be estimated by the early rising section data and a few declining section data, or even just the early rising section data. Furthermore, a generalized production decline analysis model was proposed, which can match all flow regimes in coalbed methane reservoirs as well as in shale gas and tight gas reservoirs. Particularly, the Arps exponential decline model, Duong model, and SEPD model are some of its special forms. By analyzing the production of typical wells in the Zhengzhuang-Fanzhuang block of China, it has demonstrated that the generalized production forecasting model performs significantly better than the Duong model in terms of data fitting accuracy and prediction accuracy, and in comparison to the Arps model, PLE model, SEPD model and Duong model, the generalized production decline model has also demonstrated greater scientific validity and field applicability for predicting coalbed methane production. The MAPE value between the predicted and actual gas production ranges from 4.46% to 8.82%, with an average of 5.75%, and the RRMSE between them ranges from 6.24% to 9.74%, with an average of 7.53%, which are both significantly lower than those provided by the Arps model, PLE model, SEPD model, and Duong model.

Remote estimates of suspended particulate matter in global lakes using machine learning models

Suspended particulate matter (SPM) in lakes exerts strong impact on light propagation, aquatic ecosystem productivity, which co-varies with nutrients, heavy metal and micro-pollutant in waters. In lakes, SPM exerts strong absorption and backscattering, ultimately affects water leaving signals that can be detected by satellite sensors. Simple regression models based on specific band or hand ratios have been widely used for SPM estimate in the past with moderate accuracy. There are still rooms for model accuracy improvements, and machine learning models may solve the non-linear relationships between spectral variable and SPM in waters. We assembled more than 16,400 in situ measured SPM in lakes from six continents (excluding the Antarctica continent), of which 9640 samples were matched with Landsat overpasses within ±7 days. Seven machine learning algorithms and two simple regression methods (linear and partial least squares models) were used to estimate SPM in lakes and the performance were compared. To overcome the problem of imbalance datasets in regression, a Synthetic Minority Over-Sampling technique for regression with Gaussian Noise (SMOGN) was adopted in this study. Through comparison, we found that gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) models demonstrated good spatiotemporal transferability with SMOGN processed dataset, and has potential to map SPM at different year with good quality of Landsat land surface reflectance images. In all the tested modeling approaches, the GBDT model has accurate calibration (n = 6428, R2 = 0.95, MAPE = 29.8%) from SPM collected in 2235 lakes across the world, and the validation (n = 3214, R2 = 0.84, MAPE = 38.8%) also exhibited stable performance. Further, the good performances were also exhibited by RF model with calibration (R2 = 0.93) and validation (R2 = 0.86, MAPE = 24.2%) datasets. We applied GBDT and RF models to map SPM of typical lakes, and satisfactory result was obtained. In addition, the GBDT model was evaluated by historical SPM measurements coincident with different Landsat sensors (L5-TM, L7-ETM+, and L8-OLI), thus the model has the potential to map SPM of lakes for monitoring temporal variations, and tracks lake water SPM dynamics in approximately the past four decades (1984–2021) since Landsat-5/TM was launched in 1984.

The impact of job security on entrepreneurial activities: The case of Sultanate of Oman

Purpose — The main goal of this study is to determine the relationship between job security and entrepreneurial activities.Method — This study used the quantitative method and secondary data in this investigation. The questionnaire was collected from 96 entrepreneurs for the sample size, where the collected data is from the Global Entrepreneurship Monitor (GEM) project database in Oman. The Smart-PLS technique’s simple regression method was employed to analyze data.Result — The empirical result found that there is a significant link between job security and entrepreneurial activity. In addition, job security makes the entrepreneur develop long-term strategic plans. In general, the government orientations in Oman 2040 vision support entrepreneurship and support openness, while Oman 2040 vision wants to reduce the contribution of oil revenues and raise the contribution of non-oil revenues.Contribution — This research contributes to the existing literature by improving the understanding of the significance of job security in improving entrepreneurial activity practices for entrepreneurs' projects. Furthermore, the finding provides empirical evidence of the ability of job security to be a valuable instrument for sustainable entrepreneurial activity.

THE INFLUENCE OF INSTAGRAM CELEBRITY ENDORSEMENT ON PURCHASE INTEREST IN STUDENTS OF THE TRISAKTI TOURISM INSTITUTE

This study aims to determine the effect of celebrity endorsements on Instagram on the buying interest of Trisakti Tourism Institute students. This research is a qualitative research that is quantified through a simple regression method. The population in this study were all Trisakti STP students, while the sample consisted of 100 respondents. The sampling technique used is accidental sampling. Data collection techniques by distributing questionnaires with a Likert scale. The results showed that celebrity endorsements on Instagram had a significant effect on the buying interest of Trisakti Tourism Institute students. Instagram celebrity endorsements significantly affect purchase intention among Trisakti Tourism Institute students because of their positive perceptions of Instagram celebrities as endorsers, their reputation and popularity, and the ease with which celebrities promote products or services on social media.

Pengaruh Perceived Organizational Support, Contigency Reward Behaviour terhadap Work Engagement

Purpose: This study aimed to analyze and explore the presence of perceived organizational support and contingent reward behavior in work engagement case studies of employees in PT Pelabuhan Indonesia. Methodology: The sample used in this study was purposive by distributing questionnaires online to 70 employees at PT Pelabuhan, Indonesia. The data analysis method was a simple regression method using SPSS version 25. Results: The results showed that perceived organizational support and contingent reward behavior had a positive and significant effect on work engagement in a case study of employees in PT Pelabuhan Indonesia. Limitations: This study involved only 70 employees at PT Pelabuhan Indonesia as respondents. In this study, only contingency reward behavior and perceived organizational support were used as independent variables. Work engagement is the dependent variable. Contribution: The findings of this study are useful in providing and becoming a source of information about the influence of perceived organizational support and contingent reward behavior, and can be applied by PT Pelabuhan Indonesia so that they can pay more attention to employees by implementing contingent reward behavior and perceived organizational support so that workers will feel valued in working at PT. Pelabuhan Indonesia feels that the workforce is prioritized, gets motivated, and can be rewarded according to performance to achieve company goals. This will have an impact on increasing work engagement in PT Pelabuhan, Indonesia.