Information Resource Management Research Articles

Design and Implementation of Dancing Information Management System Based on Visual Processing

With the advent of the information age and the continuous updating of modern educational methods, the learning form of schools has developed into a diversified structure, and the status of network teaching in the modern educational mechanism has also been continuously improved, achieving irreplaceable results in modern teaching. The advantages of network teaching in its breadth and ubiquity have been recognized and now accepted by students. At present, most of the schools relax students’ study areas and increase students’ interest in self-directed learning. A good online teaching method is an educational method formed by comprehensive use of modern educational technology. To sum up, it is imperative to develop a system that is suitable for schools to manage many dance resource websites. The dance resource information management platform organically integrates related digital resources around a certain theme to form a resource combination covering text, pictures, audio, video, and other types of materials, specifically related to content associations and subject indexing. This dance digital resource information can be revealed from multiple dimensions through the information system. It is convenient for users to retrieve and browse various information. This method facilitates the interaction between teachers and students, thereby greatly improving the utilization rate of digital information resources. The three-dimensional teaching of dance classroom is realized. Through the platform, a wealth of characteristic dance resource information can be collected from all over the world. Through the system’s reorganization of dance information, value-added services of characteristic dance resources are realized, making the dance material novel and professional. The system has strong operability and practicability to achieve the integration of teaching design and diversification of learning applications.

Management Information System Tools and Human Resource Management Practices in Selected Commercial Banks in Akwa Ibom State

This research was conducted to examine the relationship between Management Information System Tools and Human Resource Management Practices in Selected Commercial Banks in Akwa Ibom State. Four objectives and four Research Questions were developed. For the objective of the study to be achieved, four hypotheses were formulated. A survey research design was adopted for the study and a sample size of 181 respondents was drawn from the population of 330. The major instrument for data collection was a structured questionnaire administered to the respondents. Random Sampling Technique was used to pick the size from the population. Data collected were analyzed using simple percentage and Pearson Product Moment Correlation was used in testing the hypotheses. Results showed that there is a significant correlation between system software, Supervisor support and employee network design, and Human Resource Management. Based on the findings of the analysis, it was concluded that Software design, supervisor support, employee network design, business and process knowledge are significant positive determinants of Human resource management. Commercial Banks that consider the application of management information system dimensions are likely to record a better HRM Key Performance Indicators (KPI); Recruitment & Selection, Training & Development, Performance Management and Appraisal Processes, and Compensation and Benefits. Although all four (4) management information system dimensions were strongly correlated, the coefficient for business and process knowledge was seen as the dimension that had the highest significant influence at 0.874 on the human resource. Consequently, it is recommended that Commercial Banks in Akwa Ibom State should help in the development of human resources management information system to measuring goals through training programs.

On the Calibration of Spatially Distributed Hydrologic Models for Poorly Gauged Basins: Exploiting Information from Streamflow Signatures and Remote Sensing-Based Evapotranspiration Data

Spatially distributed hydrologic models are useful for understanding the water balance dynamics of catchments under changing conditions, thereby providing important information for water resource management and decision making. However, in poorly gauged basins, the absence of reliable and overlapping in situ hydro-meteorological data makes the calibration and evaluation of such models quite challenging. Here, we explored the potential of using streamflow signatures extracted from historical (not current) streamflow data, along with current remote sensing-based evapotranspiration data, to constrain the parameters of a spatially distributed Soil and Water Assessment Tool (SWAT) model of the Mara River Basin (Kenya/Tanzania) that is forced by satellite-based rainfall. The result is a reduced bias of the simulated estimates of streamflow and evapotranspiration. In addition, the simulated water balance dynamics better reflect underlying governing factors such as soil type, land cover and climate at both annual and seasonal time scales, indicating the structural and behavioral consistency of the calibrated model. This study demonstrates that the judicious use of available information can help to facilitate meaningful calibration and evaluation of hydrologic models to support decision making in poorly gauged river basins around the world.

Research on Library Book Information Resource Management Based on Artificial Intelligence and Sensors

In order to explore the research on library book information resource management, the author proposes a method based on artificial intelligence and sensors. Using an improved SVM algorithm, in order to realize the personalized data mining of the library, the support vector machine algorithm has supervised, scalable, and nonlinear high-efficiency characteristics in the use process, able to achieve nonlinear multicore data clustering effect, thereby improving the learning ability of data mining. The experimental results show the following: BP neural network was used to adaptively train the processed data samples, users give certain positive feedback during use, the sensor system is based on the result of feedback, continuous self-learning was carried out, sample data were updated and optimized, and a closed virtuous circle has been realized. In the classification experiment of Sogou Chinese text corpus data set, the classification effect of several classification models in the “sports” category is significantly higher than that of other categories; it shows that the text classification characteristics of the “sports” category are more significant. Among them, KNN ( K -Nearest Neighbor) has a classification accuracy of 99.7% in the “sports” category; this shows that some classification algorithms, in a specific category, can achieve its best classification performance. Prove the method based on artificial intelligence; it can better realize library book information resource management.

The Reproduction of Endemic Fish Opudi (Telmatherina prognatha Kottelat, 1991) in Lake Matano, South Sulawesi, Indonesia

Telmatherina prognatha is an endemic fish of the Matano Lake, in Indonesia, which demands a very crucial reproductive study, especially based on habitat conservation. This research, therefore, aims at analyzing its reproduction function as basic information for resource management in Matano Lake. Furthermore, the investigation was conducted from March 2018 to February 2019 in six sampling spots, where the habitat conditions, including the physicochemical characteristics of the water, were observed monthly. Also, the fish were sampled using a multifilament net that was 0.5 inches in mesh size and subsequently measured for total length, body weight, and gonad maturity. Moreover, the total number of specimens were approximately 1809 individuals (n), encompassing both sexes of male (1059) and female (750), which possessed a total length in the ranges of 36.46 – 72.00 mm and 32.93 – 66.20 mm, respectively. However, the sex ratio obtained was 1.4: 1, and those with matured and maturing gonads were found in each sampling’s month, and most of all, including those with spent gonads, are commonly discovered in February, March, and April. Also, the 50% probability of maturity was estimated as 57.31 mm for males and 45.16 mm for females, while the mean gonadal somatic indices (GSI) were 1.72±1.39 and 4.12±1.74, respectively. Therefore, the fecundity of this species was estimated to be in a range of 77-299 eggs, and the oocyte diameter was between 0.11 mm and 1.37 mm.

Drought and flood dynamics of Godavari basin, India: A geospatial perspective

Assessing incidence and trends of droughts and floods can provide essential information for better water resource management, particularly in the context of ongoing climate change. The present study has examined the prevalence of drought and flood events of Godavari basin, India, for the last four decades using the Standardized Precipitation Evapotranspiration Index (SPEI) 3 months and 9 months, and their trends using Mann–Kendall’s test and Sen’s slope. The spatial distribution, frequency, and intensity of drought and flood episodes in the basin are not uniform. A 20-year breakdown, 1980–1999 and 2000–2019, shows a drastic increase in drought frequency in Manjra and Pranhita sub-basins, while Godavari Upper, Indravati, and northern part of Weinganga have registered increased flood frequency. The monthly trend of SPEI-3 shows that most of the sub-basins have registered a negative trend (increasing dryness) only during the winter months, while under the SPEI-9 scenario both Manjra and Pranhita registered strong negative trend throughout the year. Sen’s slope estimations detected an increasing trend in severity from SPEI-3 to SPEI-9. Dryness severity trend is highest in Pranhita, followed by Manjra, Wardah, and Lower Godavari sub-basins, and the increasing wetness severity trend is in Upper Godavari, while rest of the sub-basins do not show any significant trend. The interior districts of the basin are more prone to the high drought conditions in near future if the current trends persist. To sustain the future climate change challenges, the vulnerable districts need urgent structural and non-structural changes in their current water resource management practices.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12517-022-10041-5.

COVID-19 and role of pharmacists: Knowledge and perceptions of pharmacists from Iraq and Syria.

Objectives:To assess pharmacists from Syria and Iraq on their knowledge, readiness, and perspectives on their roles during the COVID-19 pandemic. In addition to investigate pharmacists’ perceived barriers towards delivering their emergency roles during the COVID-19 pandemic and perceived policymakers’ responsibilities.Methods:This cross-sectional study design was conducted in Iraq and Syria during COVID-19 outbreak over one month (April 2020). The study objectives were addressed through a validated online questionnaire. Data was analyzed using statistical package for social science (SPSS).Results:A total of 916 individuals participated in this study (447 from Iraq and 439 from Syria). Most of Iraqi participants were students while most of Syrian participants were graduates and technicians. The mean age for the Iraqi participants was 22.5 (SD= 4.1) while the mean age for the Syrian participants was 28.7 (SD= 9.1). A significant difference (p< 0.001) was found in following the latest coronavirus updates on management among pharmacy graduates and technicians and undergraduate students. Media was reported as the most used source for knowledge about epidemics/pandemics among the Iraqi and the Syrian participants. Around 72% strongly agreed/agreed that the faculties of pharmacy have a role in preparing them to deal with epidemics/pandemics. In addition, 64.3% strongly agreed/agreed that pharmacist associations and societies have a role in preparing them to deal with epidemics/pandemics such as the coronavirus. Moreover, 82.9% believed that pharmacists should receive training programs on how to provide mental health support for people during epidemic and pandemic outbreaks.Conclusion:Tertiary pharmacy education providers and pharmacy professional bodies have a strong role in preparing pharmacists to deal with pandemics. Therefore, the importance of providing beforehand training programs on epidemics/pandemics management, workshops, lectures, and online information resources for such circumstances is crucial for pharmacists.

The Construction of Big Data Computational Intelligence System for E-Government in Cloud Computing Environment and Its Development Impact.

The traditional E-government big data system fills and classifies algorithms with low accuracy and poor work efficiency. With the development and wide application of big data, the internet of things, and other technologies, the integration of information resources has become the key to information construction. In the process of information resource integration, there are still outstanding problems such as incomplete government information resource system, different standards of government information resource management system construction, and serious threats to network and information security. In order to solve this problem, a new E-government big data system filling and classification algorithm is studied in the cloud computing environment; E-government big data filling is carried out on the basis of complete compatibility theory; and the E-government big data computing intelligence system in the cloud computing environment is constructed and its development impact, so as to parallelize the data, classify the data through decision trees, and realize incremental update decision forest parallelization processing. To verify the effectiveness of the method, comparative experiments are set, and the results demonstrate that experiment one is randomly built into the classification model, and according to the decision forest algorithm, the optimal number of decision trees is 24.

Design of enterprise human resources decision support system based on data mining

The management and development of human resources have become one of the core contents of enterprise management. It is from the initial use as an auxiliary tool in human resources business management to the later development of a relatively independent and functional sub-system for the processing of personnel matters between modules, and then to achieve the integration of various sub-systems to form a relatively complete set of human resources information management system. In recent years, with the rapid development of data mining technology, more and more enterprises have begun to attach importance to the assistance of computer information systems to manage the completion of various decision-making activities in operation. This paper designs an enterprise human resource decision support system based on data mining to more effectively support human resource management business information, data standardization, intelligent decision analysis, optimize human resource business processes, improve human resource management business efficiency, provide more effective decision support for enterprise operation and promote enterprise development. Results of the study show the effectiveness of the research.

Enabling technologies daily nursing work and its practical implications

In the health area, technological and scientific advances are paramount for the development of innovative technologies used in the treatment of diseases. Likewise, several categories of technologies are also useful in numerous activities for organizational management, resources management (medicines, data and information sets and people, among other) in the health field. Technologies are essential in work environments, but they also have implications, directly, for professionals who handle and operate them. Therefore, this study aims to identify the main technologies used by nursing professionals (nurses and technicians in nursing) and their impacts on work. Results were obtained through literature review, prioritizing case studies. Was carried out in at Scopus, PubMed, Web of Science, Emerald and Science Direct. Two analyses were conducted, bibliometrics, in order to explore the main data of the articles (years of publications, scientific journals and more frequent terms) and qualitative (highlight the main contributions of articles regarding the researched topic). In qualitative analysis, the articles were submitted to filtering procedures, ordered and selected for full reading. Technologies have potential to improve or create obstacles to the performance at work, health and well-being of the professionals. Health Information Technology was widely disseminated in case studies analyzed; it reduces informational and technical burden at work, access to support from colleagues in large centers and decision-making support, among others positive impacts.

Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting

Abstract. Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from general circulation models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in GCM-based ETo forecasts. However, time-dependent errors resulting from GCM misrepresentations of climate trends have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian joint probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 (%) in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modeling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasting and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.

Human Resource Management and Management Information System in A Select Deposit Money Banks in Akwa Ibom State

This research was designed to examine the relationship between Human Resource Management and Management Information System in a Select Deposit Money Banks in Akwa Ibom State. A survey research design was adopted for the study and a sample size of 181 respondents was drawn from the population of 330. For the objective of the study to be achieved, four hypotheses were formulated. The major instrument for data collection was a structured questionnaire administered to the respondents using Random Sampling Techniques. Data collected were analyzed using simple percentage and Pearson Product Moment Correlation. Results show that there is a significant correlation between system software, Supervisor support, employee network design, business and process knowledge and Management Information System. Based on the finding of the analysis, it is concluded that Software design, supervisor support, employee network design, business and process knowledge are significant positive determinants of Human resource management. Companies that consider the application of management information system dimensions are likely to record a better HRM Key Performance Indicators (KPI). Although all four (4) management information system dimensions were strongly correlated, the coefficient for business and process knowledge was seen as the dimension that had the highest significant influence at 0.874 on the human resource. Consequently, it is recommended that Deposit Money Banks in Akwa Ibom State should help in the development of human resources management information system to measuring goals through training programs.

Quantitation of the uncertainty in the prediction of flow fields induced by the spatial variation of the fracture aperture

The problem of fluid flow in a single fracture with variable aperture is analyzed from a stochastic point of view. The spatially distributed fracture aperture is treated as a random variable and the random aperture process can be characterized as a zero-order intrinsic random process (nonstationary random process with homogeneous increments), resulting in nonstationary flow fields. To characterize the stochastic behavior of the flow process, the general solutions for the mean values of and variances of the pressure head and the integrated flux in the mean flow direction are derived. These derivations are performed using the Fourier-Stieltjes representation approach. The mean is an unbiased estimator and the variance can be used to characterize the uncertainty of the mean model. The theory is applied to the case where the process of random aperture can be described as fractal Brownian motion. The analysis shows that the larger the parameter H, the greater the variance of pressure head and integrated flux. The transfer function helps smooth out much of the small-scale variation caused by the random aperture field. The variance (uncertainty) of the integrated flux in x-direction around the mean flux (or the classical deterministic model solution) increases with distance. Estimation of total flux through a natural fractured system is often evaluated for the purpose of rational management of groundwater resources. The proposed stochastic theory for flow prediction under uncertainty under the natural field conditions as input to the flow model for a fractured system should provide useful information for regional groundwater resource management. To our knowledge, the subject of fluid flow in nonstationary random flow fields has not yet been addressed in fracture. Our findings will be useful for the interpretation of real field data and as a stimulus for further research in this area.

Development of Deep Learning Models to Improve the Accuracy of Water Levels Time Series Prediction through Multivariate Hydrological Data

Since predicting rapidly fluctuating water levels is very important in water resource engineering, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to evaluate water-level-prediction accuracy at Hangang Bridge Station in Han River, South Korea, where seasonal fluctuations were large and rapidly changing water levels were observed. The hydrological data input to each model were collected from the Water Resources Management Information System (WAMIS) at the Hangang Bridge Station, and the meteorological data were provided by the Seoul Observatory of the Meteorological Administration. For high-accuracy high-water-level prediction, the correlation between water level and collected hydrological and meteorological data was analyzed and input into the models to determine the priority of the data to be trained. Multivariate input data were created by combining daily flow rate (DFR), daily vapor pressure (DVP), daily dew-point temperature (DDPT), and 1-hour-max precipitation (1HP) data, which are highly correlated with the water level. It was possible to predict improved high water levels through the training of multivariate input data of LSTM and GRU. In the prediction of water-level data with rapid temporal fluctuations in the Hangang Bridge Station, the accuracy of GRU’s predicted water-level data was much better in most multivariate training than that of LSTM. When multivariate training data with a large correlation with the water level were used by the GRU, the prediction results with higher accuracy (R2=0.7480–0.8318; NSE=0.7524–0.7965; MRPE=0.0807–0.0895) were obtained than those of water-level prediction results by univariate training.

The role of big data and predictive analytics in the employee retention: a resource-based view

PurposeThe authors have attempted to understand how big data and predictive analytics (BDPA) can help retain employees in the organization.Design/methodology/approachThis study is grounded in the positivism philosophy. The authors have used a resource-based view (RBV) to develop their research hypotheses. The authors tested their research hypotheses using primary data gathered using a single-informant questionnaire. The authors obtained 254 usable responses. The authors performed the assumptions test, performed confirmatory factor analysis (CFA) to test the validity of the proposed theoretical model, and further tested their research hypotheses using hierarchical regression analysis.FindingsThe statistical result suggests that the various human resource management strategies play a significant role in improving retention under the mediating effect of the BDPA.Research limitations/implicationsThe authors have grounded their study in the positivism philosophy. Moreover, the authors tested their hypotheses using single-informant cross-sectional data. Hence, the authors cannot ignore the effects of the common method bias on their research findings. Moreover, the research findings are based on a particular setting. Thus, the authors caution the readers that their findings must be examined in the light of their study limitations.Practical implicationsThe study provided empirical findings based on survey data. Hence, the authors provide numerous guidelines to the practitioners that how the organization can invest in creating BDPA that helps analyze complex data to extract meaningful and relevant information. This information related to employee turnaround may guide top management to reduce the dissatisfaction level among the employees working in high-stress environments resulting from a high degree of uncertainty.Social implicationsThe study helps understand the complex factors that affect the morale of the employee. In the high-paced environment, the employees are often exposed to various negative forces that affect their morale which further affect their productivity. Due to lack of awareness and adequate information, most of the employees and their issues are not dealt with effectively and efficiently by their line managers. Thus, the BDPA can help tackle the most complex problem of society in a significant way.Originality/valueThis study offers some useful contributions to the literature which attempts to unfold the complex nexus between human resource management, information management and strategy. The study contributes to the BDPA literature and how it helps in the retention of employees is one of the areas which still remains elusive to the academic community. Moreover, the managers are still skeptical about the application of BDPA in understanding human-related issues due to a lack of understanding of how and to what extent the employee-related information can be stored and processed. This study’s findings further open the new avenues of research that need to be tackled.

Meta-heuristic algorithm-based human resource information management system design and development for industrial revolution 5.0

Meta-heuristic algorithm-based human resource information management system design and development for industrial revolution 5.0

Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product

Abstract. The increasing availability of high-quality remote sensing data and advanced technologies has spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985–2017 at a 30 m resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover products and five products related to land surface change. The LCMAP land cover products were validated using a collection of ∼25 000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5 %. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and shared Mesos cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30 m annual land change datasets that include land cover, land cover change, and spectral change spanning from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future. The LCMAP products introduced in this paper are freely available at https://doi.org/10.5066/P9W1TO6E (LCMAP, 2021).

Geographical information systems in sustainable rural tourism and local community empowerment: A natural resources management appraisal for Musina Municipality’ Society

ABSTRACT The purpose of this examination is to prospect the underlying capacity of diverse geographic information systems in sustainable rural tourism and local community empowerment in South Africa and, extensively, Africa. Hence, corresponds with diverse rural areas elsewhere globally, utilizing the case of Musina Municipality in Limpopo Province, South Africa. The case study appropriates narrative literature review (with existing documents reviews), interviews, and focuses group discussion in studying African and comprehensive agricultural, environmental, and tourism reservoirs management situations. It links community-based natural resource management (CBNRM) and geographic information systems to sustainable tourism implications in Musina Municipality. Numerous agricultural communities in Southern Africa and Africa comprehensively reside in low-income districts; Musina Municipality is no different. Such localities are plentiful in natural biodiversity and tourism host provinces. Geographic information systems, sustainable tourism, agriculture (AG), and community-based natural resource management can generate a medley of local community development schemes within such environs.

Relationship between Recruitment Information System and Management of Human Capital: A case of James Finlay Limited, Kenya

Technological advancement in the 21st century has unlocked possibilities in all spheres of existence. Lately, with the advent of the Covid-19 pandemic, the workplace has been transformed, and businesses are operating in unchartered waters. A human resource management information system is believed to guarantee a more efficient and effective way of managing human capital in organizations. However, its implementation has not effectively transformed from the manual method of running the human resource functions. Many organizations are, therefore, still stuck with the traditional way of managing human capital. On this basis, this study sought to establish the effectiveness of recruitment information systems in human capital management. The study was anchored on the resource-based theory of the firm and used a cross-sectional research design with a target population of 458 employees. A sample size of 213 respondents was determined while stratified and simple random sampling techniques were adopted to select the respondents for the study. A structured questionnaire was used to obtain primary data from the respondents. The reliability of the questionnaire was tested through a pilot study where an average Cronbach alpha coefficient of 0.89 was obtained. The validity of the questionnaire was ensured by doing a detailed literature review and consultation with subject experts. Descriptive statistics (mean and standard deviation) were used to summarize data, while correlation analysis was used to test the study's hypothesis. Results were presented using tables. The findings established that recruitment information systems had a strong positive relationship (R = 0.873, β1 = 0.518, R2 = 0.762, p = 0.05) with human capital management, indicating that James Finlay Limited should invest more in Recruitment information systems. The study recommended that the company improve the recruitment information system to enhance the job interview process and improve the system to allow efficiency in tracking job applications and appointment processes.

Measurement model of service quality of tourism supply chains in Thailand

ABSTRACT The quality of service plays an important role in the tourism industry; moreover, it is a crucial issue that encourages competition potential to attract tourists. This study develops a measurement model of the service quality perception of tourism supply chains in Thailand using exploratory factor analysis and second-order confirmatory factor analysis. The study used a questionnaire survey administered to 1,400 domestic tourists. The research found that there are seven significant factors, namely, Customer relationship management, Order process management, Service performance management, Capacity and resource management, Demand management, Sustainability management, and Information and technology management affecting the service quality perception of tourism supply chains in Thailand.