Research on the Relationship Between Organizational Commitment

and Work Performance of College Teachers Based on Big Data

Technology

Wanxiang Xie

and Grace R. Tobias

†

College of Business Administration, University of the Cordilleras, Baguio City, 2600 Philippines

* †

Keywords: Big Data Technology, Teachers’ Organizational Commitment, Job Performance, Higher Education.

Abstract: This study explores big data technology's application and impact on understanding organizational

commitment's relationship to college teacher job performance. Through surveying teachers at home and

abroad, and using structural equation modeling (SEM) to analyze links between organizational commitment,

job satisfaction, stress, career development and performance, the study finds teachers' organizational

commitment significantly positively impacts their job performance. Affective and normative commitment

most significantly impact performance. Big data technology enriches measurement methods, revealing job

satisfaction's mediating role and work pressure and career development's moderating role. The findings

provide empirical evidence for improving job performance by increasing faculty commitment and satisfaction,

and indicate possible future research directions.

1 INTRODUCTION

1.1 Research Background

With the deepening of digital transformation, big data

technology has become an important force in

promoting innovation in all walks of life, including

higher education. As the main body of teaching and

scientific research, college teachers’ organizational

commitment and work performance are directly

related to the quality of higher education and the

development of the school. In recent years, many

studies have focused on how to improve the

efficiency and effectiveness of human resource

management through big data technology, especially

in higher education institutions. The application of

big data technology provides new ways to deeply

understand teachers’ work behaviors and improve

teachers’ work performance. Perspectives and

methods. For example, Marchena Sekli and De la

Vega (2021) explored the adoption of big data

analytics technology and its impact on the

organizational performance of higher education

institutions. Additionally, Beerkens (2021) discusses

the evolution of higher education performance data

and the changes it may mean for higher education

governance in the “big data” era.

1.2 Research Purpose and Significance

This study aims to explore the relationship between

organizational commitment and job performance of

college teachers based on big data technology.

Through in-depth analysis of the application of big

data technology in higher education human resources

management, we established a teacher organizational

commitment measurement model, evaluated the

application effect of big data technology in teacher

work performance evaluation, and analyzed the

relationship between teachers’ organizational

commitment and work performance. It aims to provide

decision-making support for higher education

management departments, improve teachers' work

performance, and thereby improve the quality of

education. The research not only has theoretical

significance, filling the gaps in existing research, but

also has practical significance, providing empirical

basis for higher education institutions to use big data

technology to optimize human resource management.

Xie, W. and Tobias, G.

Research on the Relationship Between Organizational Commitment and Work Performance of College Teachers Based on Big Data Technology.

DOI: 10.5220/0012789600004547

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Data Science and Engineering (ICDSE 2024), pages 5-10

ISBN: 978-989-758-690-3

2 LITERATURE REVIEW

2.1 Organizational Commitment

Theory

Organizational commitment refers to employees'

loyalty and sense of belonging to the organization to

which they belong. It is one of the important indicators

for predicting employee retention, job satisfaction and

job performance. Meyer and Allen (1991) proposed a

tripartite model of organizational commitment, which

includes three dimensions: affective commitment,

continuance commitment and normative commitment.

Affective commitment refers to employees' emotional

attachment and identification with the organization;

continuation commitment is based on the result of

cost-benefit evaluation, and employees have to stay in

the organization because the cost is too high;

normative commitment stems from employees' sense

of moral responsibility and obligation to the

organization . Wilkins, Butt, and Annabi (2017) found

that in the context of transnational higher education,

the level of organizational commitment of employees

at foreign campuses is not as good as that of

employees at domestic campuses. This is a problem

for managers to implement human resource

management strategies in different cultures and

environments. challenge.

2.2 Job Performance Evaluation

Methods

Job performance evaluation is a systematic evaluation

of employees' work performance and is an

indispensable part of human resources management.

Effective performance evaluation methods can

improve organizational efficiency and enhance

employee job satisfaction and organizational

commitment. Hanaysha (2016) research pointed out

that employee participation, work environment and

organizational learning have a significant positive

impact on organizational commitment. These factors

indirectly improve work performance by improving

employees' job satisfaction and organizational

commitment. In addition, research by Pinho et al.

(2017) shows that there is a positive relationship

between training, development and education

practices as well as performance and competency

assessment practices and organizational commitment,

especially the dimensions that affect affective and

normative commitment (Haerofiatna, et 2021).

2.3 Application of Big Data Technology

in Human Resources Management

With the rapid development of information

technology, the application of big data technology in

human resource management (HRM) has gradually

become a research hotspot. Big data technology can

help organizations collect, store, and analyze large

amounts of complex employee data, thereby providing

scientific basis for human resources decision-making.

Research by Aboramadan et al. (2020) shows that

HRM practices (such as performance evaluation,

rewards and compensation) have a significant impact

on improving employees' organizational commitment,

and work participation plays a role between

performance evaluation, rewards and organizational

commitment. Mediating Role. In addition, Marchena

Sekli and De la Vega (2021) pointed out that the

adoption of big data analytics can indirectly improve

the organizational performance of higher education

institutions by promoting the knowledge management

process. This shows that big data technology can not

only improve human resource management practices

but also enhance the overall performance of the

organization (Akter et al 2019).

3 RESEARCH METHODS

3.1 Research Design

This study adopts quantitative research methods and

constructs a structural equation model (SEM) to

explore the relationship between big data technology

and organizational commitment and job performance

of college teachers. The research hypotheses were

based on existing theoretical frameworks and were

adjusted and refined with reference to variables and

measurement tools used in previous studies to fit the

specific context of this study (Tus et al 2022).

3.2 Data Collection and Processing

The data comes from questionnaires on teachers in

higher education institutions at home and abroad,

including teachers’ basic information, work situation,

feelings of organizational commitment and self-

evaluation of work performance. The questionnaire

was designed using a 5-point Likert scale (Mishra

2014). After data collection, data cleaning and

preprocessing were first performed, including steps

such as processing missing values, outliers, and data

ICDSE 2024 - International Conference on Data Science and Engineering

standardization to ensure the effectiveness and

accuracy of subsequent analysis.

3.3 Analysis Methods

In order to analyze the relationship between teachers'

organizational commitment and job performance, we

use the following formula:

Organizational commitment measurement

formula:

11 2 2 3 3 nn

OC X X X X

αα α α

=+ + ++

(1)

Among them,

represents organizational

commitment,

represents factors affecting

organizational commitment, and

is the

corresponding coefficient.

Job performance measurement formula:

11 2 2 3 3 mm

WP Y Y Y Y

ββ β

=+ + ++

(2)

Among them,

represents work

performance,

represents factors affecting work

performance, and

is the corresponding

coefficient.

Structural Equation Model (SEM) formula:

WP OC

=+ò

(3)

Among them,

represents organizational

commitment,

is the relationship coefficient

between the two, and

is the error term.

4 BIG DATA TECHNOLOGY IS

APPLIED TO RESEARCH ON

TEACHERS’

ORGANIZATIONAL

COMMITMENT AND WORK

PERFORMANCE

4.1 Teacher Organizational

Commitment Measurement Model

Based on Big Data

When using big data technology to explore the

measurement model of teachers' organizational

commitment, it is first necessary to construct a

comprehensive index system containing multiple

dimensions. This system not only covers traditional

questionnaire data, but also integrates a large amount

of unstructured data generated in teachers' daily work,

such as email communications, online teaching

activity logs, and social media interactions. Through

in-depth mining and analysis of these big data,

teachers’ behavioral patterns, emotional attitudes, and

professional development needs can be captured more

comprehensively (Çolak et al 2014).

Using machine learning algorithms, such as

cluster analysis and sentiment analysis, to

automatically classify and quantify teachers' online

behaviors and expressed emotions can provide a more

detailed and dynamic assessment of teachers' affective

commitment, continuance commitment and normative

commitment. In addition, by building a time series

analysis model, the trend of changes in teachers'

organizational commitment over time can also be

tracked, providing managers with real-time, data-

based feedback to more effectively formulate human

resource management strategies and teacher

development plans.

4.2 Application of Big Data Analysis in

Teacher Performance Evaluation

The application of big data analysis technology in

teacher performance evaluation breaks through the

limitations of traditional evaluation methods and

provides a more objective, comprehensive and

dynamic evaluation method. By collecting and

analyzing teachers' teaching activity data, student

evaluation data, scientific research output data and

other multi-source data, combined with data mining

technology, such as association rule analysis,

predictive modeling, etc., teachers' teaching quality,

scientific research capabilities and services can be

comprehensively evaluated. contribute.

For example, by using text analysis technology to

conduct in-depth analysis of students’ online course

feedback and evaluations, we can discover the factors

that have the greatest impact on teachers’ teaching

effectiveness; through quantitative analysis of

teachers’ scientific research projects, published

papers, patents and other scientific research outputs,

we can objectively evaluate The scientific research

level and innovation ability of teachers. These analysis

results based on big data can not only provide

guidance for teachers' personal career development,

but also help higher education institutions optimize

resource allocation and improve the quality of

teaching and scientific research.

Research on the Relationship Between Organizational Commitment and Work Performance of College Teachers Based on Big Data

Technology

4.3 Analysis of the Relationship

Between Teachers’ Organizational

Commitment and Work

Performance

Utilizing big data technology to analyze the

relationship between teachers' organizational

commitment and job performance can reveal the

intrinsic connection and interaction mechanism

between the two. By constructing large-scale datasets,

including teachers' organizational commitment

assessments and work performance indicators, and

using statistical analysis and machine learning

methods, such as regression analysis and path

analysis, the strength and direction of the impact of

organizational commitment on work performance can

be accurately assessed.

In addition, by segmenting the data set, it is also

possible to explore the differences between

organizational commitment and job performance

among different types of teachers (such as teachers of

different ages, genders, and subject backgrounds), as

well as possible moderating factors, such as job

satisfaction, Career development opportunities and

more. This analysis not only helps to understand the

multiple influencing factors of teachers’ work

performance, but also provides university

management with targeted human resource

management and teacher motivation strategies.

5 EMPIRICAL RESEARCH

5.1 Sample Selection and Data

Description

This study selected 1,000 teachers from 30 higher

education institutions at home and abroad as a

research sample. The sample covered different types

of higher education institutions, including

comprehensive universities, science and engineering

universities, and normal universities, to ensure the

broad applicability of the research results. and

representativeness. The age distribution of the

teachers in the sample ranges from 25 to 60 years old,

and the teaching experience ranges from 1 to 35 years,

covering teachers with different professional titles and

subject backgrounds.

In terms of data collection, this study adopted

two methods: questionnaire survey and university

database. The questionnaire survey mainly collects

teachers’ basic information, organizational

commitment, self-evaluation of work performance

and other data; the university database provides

objective data such as teachers’ scientific research

output and teaching evaluation. All data are

preprocessed, including removing missing values,

outlier processing and data standardization, to ensure

the accuracy and reliability of data analysis.

5.2 Establishment and Verification of

Empirical Model

In order to explore the relationship between teachers'

organizational commitment and job performance, this

study constructed an empirical model containing the

main variables and their interactions. In the model,

organizational commitment is used as the independent

variable and job performance is used as the dependent

variable. Variables such as job satisfaction, job

pressure and career development opportunities are

also considered as moderator variables or mediating

variables to reveal their role in the relationship

between organizational commitment and job

performance. mechanism.

The model was validated using structural

equation modeling (SEM) analysis method. First, the

applicability of the measurement dimensions and

structure of the model was confirmed through

exploratory factor analysis (EFA) and confirmatory

factor analysis (CFA); secondly, the maximum

likelihood estimation method was used to estimate the

model parameters, and the goodness-of-fit index was

used to estimate the model parameters. (such as CFI,

RMSEA, etc.) to evaluate the overall fit of the model;

finally, path analysis is used to explore the direct and

indirect effects between variables.

5.3 Result Analysis and Discussion

The results of the empirical analysis show that

teachers' organizational commitment has a significant

positive impact on their work performance, which

verifies the research hypothesis. Specifically, affective

commitment and normative commitment have a more

significant positive impact on job performance, while

the impact of continuance commitment is relatively

weak. In addition, job satisfaction plays a partial

mediating role between organizational commitment

and job performance, indicating that improving

teachers' job satisfaction can further promote the

improvement of their job performance.

ICDSE 2024 - International Conference on Data Science and Engineering

Figure 1: Teacher Commitment vs. Work Performance.

As shown in Figure 1, as the level of teachers'

organizational commitment increases, their job

performance scores also show a positive growth trend.

At the same time, the different shades of color

represent the level of job satisfaction. It can be seen

from this visual analysis that teachers with high

organizational commitment and high job satisfaction

tend to have higher job performance, which further

confirms the importance of improving teachers'

organizational commitment and job satisfaction in

improving job performance.

6 CONCLUSION

6.1 Main Conclusions

This study explores the application of big data

technology in investigating the relationship between

organizational commitment and job performance

among college teachers through empirical analysis.

The study found teachers' organizational commitment

significantly positively impacts their work

performance, especially affective commitment and

normative commitment, which more obviously

enhance performance. Additionally, big data

technology enables more precise, in-depth

measurement and analysis of teachers' organizational

commitment and job performance by providing richer,

multidimensional data. Big data analysis revealed job

satisfaction mediates the relationship between

organizational commitment and job performance,

while work pressure and career development

opportunities moderate this relationship.

6.2 Research Limitations

There were several limitations in the design and

execution of this study. First, the sample selection

mainly focuses on higher education institutions at

home and abroad, and may lack consideration of other

types of educational institutions or industry

backgrounds, limiting the general applicability of the

research conclusions. Secondly, although big data

technology can provide rich data resources, the

complexity of data collection and processing may also

lead to deviations in analysis results. In addition, this

study mainly uses quantitative analysis methods and

lacks qualitative analysis of the deep-seated causes

and mechanisms of the relationship between teachers'

organizational commitment and job performance.

Research on the Relationship Between Organizational Commitment and Work Performance of College Teachers Based on Big Data

Technology

6.3 Future Research Directions

Based on the findings and limitations of this study,

future research can be expanded and deepened in the

following directions:

Diverse sample research: Expand the sample

scope to include different countries, types of

educational institutions, and industries to improve the

generalizability and applicability of research

conclusions.

Methodological diversification: Combine

quantitative and qualitative analysis methods to

deeply explore internal causes and mechanisms

affecting teachers' organizational commitment and

work performance, providing a more comprehensive

understanding.

In-depth application of big data technology:

Explore and utilize the latest big data analysis

technologies and tools, such as artificial intelligence

and machine learning, to conduct deeper analysis and

prediction of teacher behavior and performance.

Intervention research: Based on findings of this

study, design and implement specific intervention

measures, such as improving job satisfaction, reducing

work stress, and creating career development

opportunities, to verify their effects on improving

teachers' organizational commitment and work

performance.

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ICDSE 2024 - International Conference on Data Science and Engineering