3 Research Methodology
3.1 Introduction
In early research exploring the factors affecting the employee’s knowledge sharing, researchers mainly analyzed from the technical and economic perspectives. However, as understanding the impact of knowledge sharing deepened, researchers generally recognized some limitations of the economic and technical perspectives in studying corporate knowledge management and knowledge sharing practices. For example, in response to the technology perspective, Eriksson137 argues that information technology plays an essential role in employee knowledge sharing and creating new knowledge but does not significantly improve employee knowledge sharing behavior. It is mainly because information technology does not motivate employees to share knowledge. However, according to motivation theory, human behavior has some motivation, i.e., the motivating function of motivation. Technology and information technologies are the only necessary methods and tools used in the knowledge-sharing process. Still, the focus is on the people in the organization, their behavior, and how that behavior is influenced. Based on knowledge management practices, Booz-Allen has found that at least 1/3 of knowledge management projects are unsuccessful due to both parties’ lack of motivation and willingness to share knowledge. And for an economic perspective, Lin138 noted that financial rewards (e.g., salaries, bonuses, and promotion opportunities) on employees’ willingness to share knowledge were insignificant. In addition, the study by Foss139 140 confirmed that the higher the potential economic motivation (e.g., promotion, monetary rewards, etc.), the lower the employees’ willingness to engage in knowledge sharing, i.e., extrinsic rewards have a significant negative effect on employee knowledge sharing. It is mainly due to the lack of willingness to share knowledge among the demanders and owners of knowledge sharing. From the above studies, it is easy to see that the willingness and behavior of individual knowledge sharing within an organization is the key to the organizational knowledge sharing practice instead of the mere optimization of information technology. Therefore, realizing the limitations of the technical and economic perspectives, researchers in knowledge management have gradually shifted their views to the social psychology field to explain the knowledge-sharing behavior of enterprise employees better141.
This study defines creative behavior of knowledge employees as the behavioral activities in which employees use knowledge to find opportunities, generate ideas, and put them into practice through mental work, including various behavioral performances in the process of finding creative opportunities, developing ideas, promoting ideas, and executing ideas, based on academic research on knowledge employees and creative behavior. In this study, the creative behavior of knowledge-based employees refers to the generation of creative ideas. It includes the preliminary exploration behavior, the promotion of ideas, and the overall implementation inside and outside the organization to generate ideas. At the same time, the core of the creative behavior of knowledge-based employees is “new,” which can be manifested in making minor adjustments to the original products or concepts. It can also be a breakthrough innovation utterly different from the actual product or concept.
This study requires a validation study of the relationship between employee knowledge-sharing motivation and employee creativity. The study is a validation study because many studies have been conducted abroad, and some results have been obtained. A part of the literature has developed culturally appropriate scales to measure the employee’s motivation for knowledge sharing and employee creativity. These scales comprehensively assess the factors that affect employee motivation and creativity.
According to the above, this study examines the relationship between the employee’s motivation for knowledge sharing and employee creativity.
3.2 Research Design
3.2.1 The Employee’s Motivation for Knowledge Sharing Scale and Its Measurement
As a unique, valuable, and critical resource, knowledge sharing is significant for gaining a competitive advantage142 However, knowledge flow within organizations is difficult, and knowledge sharing usually does not occur naturally143. To investigate and capitalize on knowledge-sharing to strengthen competitive advantage, many organizations try to induce employees to actively engage in knowledge-sharing behavior by implementing various management systems and policies. The key measure to improve the effectiveness of knowledge sharing is to design effective incentives, and motivation is the basis of incentive design. Therefore, knowledge-sharing motivation has increasingly attracted the research interest of scholars.
Foreign scholars have examined the issue of individual motivation for knowledge sharing in a Western cultural context, including three areas of research: first, the general role of motivation for knowledge sharing, which focuses on the significance of motivation for knowledge sharing to occur at a holistic level. Researchers from different disciplines have noted the critical role of motivation,144 arguing that people are unlikely to share knowledge without solid personal motivation145. Gupta’s146 study also affirmed the vital role of individual motivation for knowledge sharing. The second is the components and theoretical basis of knowledge sharing motivation, which focuses on what motivates individuals to engage in knowledge sharing behaviors, the theoretical origin behind each motivational factor, and the role of each motivational factor concerning specific knowledge sharing behaviors. The research on the components of knowledge-sharing motivation and their functions is mainly based on motivation theory, rational behavior theory, social exchange theory, social cognitive theory, personality trait theory, and social comparison theory.
This study adopts the Employee’s Motivation for Knowledge Sharing Scale developed by Zhao Shusong (2013), based on a comprehensive review of relevant research results of domestic and foreign scholars through interviews, questionnaires and combined with Chinese cultural characteristics, which has been verified by many scholars in practice and has satisfactory reliability and validity. The 18 entries on the scale, all of which are positive, are categorized into five dimensions: Perception of achievement, Collective emotions and responsibilities, Construction of social relations, Personal Interest, and Rule obedience. The motivation of the subjects to share knowledge was measured. The scale is a one-way variable, and all the scales in this study use a five-point Likert scale: 1 means “strongly disagree”; 2 means “disagree”; 3 means ” neither agree nor disagree “; 4 means”agree”; 5 means “strongly agree.” See Table 3.1
readxl::read_excel("./files/Table3-1.xlsx") %>%
gt::gt(caption = "The Employee's Motivation for Knowledge Sharing Scale") %>%
gt::cols_width(
Contents ~ px(500)
) %>%
fmt_missing(
missing_text = ""
)| Contents | Level |
|---|---|
| 1 2 3 4 5 | |
| Perception of achievement: | |
| 1 I think contributing knowledge to colleagues helps to be more recognized. | |
| 2 I think contributing knowledge to colleagues helps to be more praised. | |
| 3 I think contributing knowledge to colleagues helps to be more respected. | |
| 4 I think contributing knowledge and sharing experience reflect my achievements and value. | |
| Collective emotions and responsibilities: | |
| 5 I contribute my knowledge and share my experience with the organization because I love it. | |
| 6 I participate in knowledge-sharing activities because I love my organization. | |
| 7 It's my responsibility to contribute my knowledge and share experience with the organization or colleagues. | |
| Construction of social relations: | |
| 8 Contributing my knowledge and sharing experience to the organization can enhance colleagues' identification with me. | |
| 9 Contributing my knowledge and sharing experience with others has broadened my reach within the organization | |
| 10 Contributing my knowledge and sharing experience makes me more acquainted with other organization members. | |
| 11 Contributing my knowledge and sharing experience with others can promote our friendship and feelings. | |
| 12 Contributing my knowledge and sharing experience can strengthen your bonds with others. | |
| Personal Interest: | |
| 13 I think sharing my knowledge and experience with my colleagues is interesting. | |
| 14 I think sharing my knowledge and experience with my colleagues is pleasant. | |
| 15 I enjoy sharing my work experience and skills with colleagues very much. | |
| Rule obedience: | |
| 16 If contributing knowledge is a collective rule or regulation, all members should abide by it. | |
| 17 It's unethical to violate the collective rule or regulation of sharing knowledge. | |
| 18 If contributing knowledge is a rule in my organization, and I'll try to abide by it. | |
| Direction:1. Strongly disagree; 2. Disagree; 3. Neither agree nor disagree; 4. Agree; 5. Strongly agree. |
Scale Provenance: Zhao Shusong. The Research on Employee’s Motivation Model of Knowledge Sharing in the Context of Chinese Culture. Nankai Management Review, 2013,16(5): 26-37
3.2.2 Employee Creativity Scale and Its Measurement
In an era emphasizing the need for change, creativity, and innovation in organizations, particularly employees’ initiation of organizational change efforts,147 one wonders if job dissatisfaction is always detrimental to organizational effectiveness. Organization members dissatisfied with their jobs are, in essence, discontented with the status quo. Discontentment can trigger change when dissatisfied people seek to come up with new ways to improve current conditions. Consistent with this reasoning, some authors have suggested that job dissatisfaction positively impacts organizational effectiveness148.149 These authors have argued that when employees are dissatisfied with their jobs, they may try to change their current work situations by coming up with new and better ways of doing things150. Creating new and better ways of doing things is the essence of creativity. Employee creativity is the generation of new and potentially valuable ideas concerning new products, services, manufacturing methods, and administrative processes-contributes for organizations’ renewal, survival, and growth in today’s turbulent and competitive business environment151.152
As mentioned earlier, in an organizational context, creativity refers to the generation of novel and potentially valuable ideas153.154 An idea must have both novelty and usefulness to be considered creative. Employee creativity differs from organizational innovation because creativity is the generation of new and valuable ideas by individual employees. In contrast, innovation involves the organization’s successful implementation of creative ideas. Thus, employees’ creativity is often the starting point for innovation.
Previous research has alluded to the possibility that employees’ creativity may be an essential form of voice155156 157158 159. For example, Kay160 conducted a study where she asked three groups of participants to describe prototypical voice behaviors. She found that the prototypical voice behaviors identified by the participants included “propose new ways of doing things” and “make suggestions on how to improve things,” both of which are consistent with commonly used definitions of employee creativity.
Creativity involves coming up with new ideas and ways of doing things and carries certain risks because the new ideas may or may not deliver their intended positive results. Moreover, creativity entails departing from the status quo, traditional approaches, and habitual behaviors embedded in organizational systems and practices. Thus, engaging in creative activities can be risky, and if they fail, employees who initiate such actions may face negative consequences. Therefore, employees may choose to use creativity as an expression of voice only when they perceive that creative performance has the potential to be effective: New and practical ideas that others in the organization will support can be produced161. Reviews of the voice and creativity literature suggest that organizational context may be a vital determinant of these perceptions.
This study uses the Employee Creativity Scale developed by Zhou J. (2001) through interviews and questionnaires based on a comprehensive review of relevant research results by domestic and foreign scholars, consisting of 13 items to measure the subjects’ creativity. The scale is a one-way variable, and all the scales in this study use a five-point Likert scale: 1 means ” strongly nontypical “; 2 means”nontypical “; 3 means” neither typical nor nontypical “; 4 means”typical”; and 5 means ” strongly typical. See Table 3.2
readxl::read_excel("./files/Table3-2.xlsx") %>%
gt::gt(caption = "Employee Creativity Scale") %>%
gt::cols_width(
Contents ~ px(500)
) %>%
fmt_missing(
missing_text = ""
)| Contents | Level |
|---|---|
| 1 2 3 4 5 | |
| 1 Propose a realistic approach to goals or objectives. | |
| 2 Propose new practical ideas to improve performance. | |
| 3 Someone is searching for new science, procedures, technologies, and product ideas. | |
| 4 Propose new methods to improve quality. | |
| 5 It is an excellent source of ideas. | |
| 6 Not afraid to take risks. | |
| 7 Promote and support the ideas of others. | |
| 8 Be creative at the right time at work. | |
| 9 Develop appropriate plans and schedules for implementing new ideas. | |
| 10 Always have original ideas. | |
| 11 Creative solutions were proposed. | |
| 12 Always have new methods to solve problems. | |
| 13 Propose new ways to execute tasks at work. | |
| Direction:1. Strongly nontypical; 2. Nontypical; 3.Neither typical nor nontypical; 4. Typical; 5. Strongly typical. |
Scale Provenance: Zhou J. (2001). When job dissatisfaction leads to creativity: Encouraging the expression of voice. Academy of Management Journal, 44(4), 682-669.
3.3 Hypothesis
Hypothesis 1 Respondents with different personal characteristics significantly differ in the employee’s motivation for knowledge sharing scale.
Hypothesis 1-1 Gender significantly affects the employee’s motivation for knowledge-sharing.
Hypothesis 1-2 Age significantly affects the employee’s motivation for knowledge-sharing.
Hypothesis 1-3 Educational background significantly affects the employee’s motivation for knowledge-sharing.
Hypothesis 1-4 Work experience significantly affects the employee’s motivation for knowledge-sharing.
Hypothesis 1-5 Position level significantly affects the employee’s motivation for knowledge-sharing.
Hypothesis 2 Respondents with different personal characteristics significantly differ in the employee creativity scale.
Hypothesis 2-1 Gender has a significant effect on employee creativity. Hypothesis 2-2 Age has a significant effect on employee creativity.
Hypothesis 2-3 Educational background has a significant effect on employee creativity.
Hypothesis 2-4 Work experience has a significant effect on employee creativity. Hypothesis 2-5 Position level has a significant effect on employee creativity.
Hypothesis 3 The employee’s motivation for knowledge sharing significantly affects employee creativity.
3.4 Population and Sampling
The target population is the research object or the information that the researcher wishes to obtain from the research object. The studied population consists of all individuals of the exact nature of the object. Each individual that makes up the total is called a unit. The sample total is the information obtained from the extracted research subjects. Usually, the number of samples is equal to the target.
The sample of this study is the Industrial Park of Jiujiang National Economic and Technology Development Zone, which was established in July 1992, one of the first inland cities along the river in China to open up to the outside world as a national development zone, the first development zone in Jiangxi Province. The Zone includes one township, two fields, three streets, and four parks, and the regional jurisdiction covers an area of nearly 150 square kilometres, with a total population of about 150,000. Since the establishment of the Zone, it has introduced more than 40 listed companies’ investment projects. There are more than 500 enterprises, including Canon digital Corporate, Sunray Reddy, Tsuzuki Automobile, and other types of enterprises, and the number of industrial enterprises above state designated scale is 113. The number of total ordinary staff in the park is about 15,000.
3.5 Sampling
From the overall method of drawing samples, there are two types of sampling: non-probability and probability. There is no strict definition of non-probability sampling, and its most important characteristic is that the sample is drawn without a sentence of random principle. The advantage of non-probability sampling is that it is simple, does not require a sampling frame, is economical, fast, and easy to process the survey data. The limitation of non-probability sampling is that the sampling error cannot be calculated, the error cannot be controlled in probability, and the sample data cannot be inferred from the overall situation. At the same time, due to the greater arbitrariness in drawing the sample, the investigator usually selects the most easily accessible and friendliest units for the survey, which leads to systematic differences between the units surveyed. Probability sampling, also known as random sampling, is a sampling method based on the principle of randomness and designed to select some units from the overall population. It has the following characteristics: 1. The sample is drawn with a probability based on the randomness principle. 2. The probability of each sampled unit is known or can be calculated. 3. When the sample is used to estimate the overall, the probability of the sample being drawn is not examined. The estimated quantity is not only related to the observed value of the sample unit but also related to its probability of being sampled. The main point of probability sampling is that, since each sample unit is randomly selected and the probability of being sampled can be calculated for each unit, an estimate of the overall target variable is obtained. There is a way to determine how much each estimate is off and how reliable your inference is about the overall target quantity.
Probability sampling is divided into several basic sampling methods. 1. Simple random sampling 2. Selection of stratified samples 3. Sampling by the whole group 4. Multiple-stage sampling 5. Strict sampling
This study mainly adopts the whole-group sampling method within probability sampling. Whole-group sampling combines the total number of basic units into groups so the group’s composition. Sampling draws the cluster and all the basic units in the selected collection to implement the survey. Such a sampling method is called cluster sampling.
3.6 Sample Size
The population collection process can determine the sample size with probability-based sampling methods. For example, the number of samples suitable for calculation, the sample size used in the study was determined using the Taro Yamane sample size formula (1973), and the sample size was determined using a 95% confidence level and allowable values. The sampling error was 5%, or 0.05. The overall sample size was 10200 individuals. N = the population, n = the number of samples, and e = the number of random sample errors was set to 0.05.
The sample size and the formula for calculating it are as follows:
\(n=N/(1+Ne^2)\)
\(n=15000/(1+15000*0.05^2)\)
\(n=399\)
For the accuracy of the study results, approximately 450 questionnaires were distributed to 100 Chinese companies, covering Internet, manufacturing, and biological companies in the Industrial Park of Jiujiang National Economic and Technological Development Zone. Employees of the industrial park were selected as study subjects.
3.7 Data Collection
During the particular period of the COVID-19 epidemic, this questionnaire was distributed through the online platform [“Questionnaire Star”] (www.wjx.cn), and the respondents also used the platform [“Questionnaire Star”] (www.wjx.cn) to fill in the questionnaire and submitted it. It is planned to take 10-15 days to collect all the questionnaire data and evaluate the validity of the questionnaires, eliminate invalid questionnaires and keep only valid questionnaires for research analysis.
3.8 Data Analysis
One Sample/Group T-test: It is a comparison of the unknown overall mean represented by the sample mean with the known overall mean (usually the theoretical value, expected value, or stable value obtained after a large number of observations, etc.) to observe the difference between this sample and the overall.
The Independent Sample T-test (Independent Sample T-test) is used when two samples are drawn independently from two totals, implying that the elements in one sample are independent of the elements in the other sample. It is used to test whether two independent samples come from the exact total with the same mean and whether the means of two average calculations are equal. Moreover, to examine whether two samples from independent totals have the same mean or centre of their independent totals.
Analysis of Variance (ANOVA): ANOVA, also known as “analysis of variance” or “F-test,” was invented by R. A. Fisher and is used to test the significance of the difference between the means of two or more samples. The test of significance of the difference between two or more samples The data obtained from the study shows fluctuations due to various factors. The causes of fluctuations can be divided into uncontrollable random factors and controllable factors imposed on the study that impact the results. ANOVA starts from the variance of the observed variables and examines which of the many control variables are the ones that have a significant effect on the observed variables. How much variance comes from different sources is examined to determine how many controllable factors affect the study results, which helps you figure out how much you can change things.
Correlation analysis is a statistical method used to determine if there is some relationship between things. It can also be used to determine the direction of the relationship and how strong it is for items with a relationship.
Factor analysis is a statistical technique to study the extraction of common factors from a population of variables. It was first proposed by the British psychologist C.E. Spielman. Factor analysis identifies hidden representative factors among many variables. By grouping variables of the same essence into a single factor, the number of variables can be reduced, and the relationship hypothesis between variables can be tested. The primary purpose of factor analysis is to describe some more basic, but not directly measurable, hidden variables (latent variables, latent factors) hidden in a set of measured variables.
Regression analysis: Regression analysis is a statistical analysis method to determine the quantitative relationship between two or more variables dependent on each other. Regression analysis is widely used. Regression analysis is divided into regression and multiple regression analysis according to the number of independent variables involved. The number of independent variables can be divided into univariate and multiple regression analyses; according to the relationship between the independent and dependent variables, it can be divided into linear and nonlinear regression analyses. Suppose only one independent variable and one dependent variable are included in the regression analysis, and a straight line can approximate the relationship. In that case, this is called one-dimensional linear regression analysis. Suppose two or more independent variables are included in the regression analysis, and the relationship between the dependent and independent variables is linear. In that case, it is called multiple linear regression analysis.
3.9 Reliability and Validity Analysis of the Scale
Reliability is the consistency of the results obtained when the same method is used to measure the same object repeatedly. Reliability indicators are mostly expressed as correlation coefficients, which can be broadly classified into three categories: stability coefficients (consistency across time), equivalence coefficients (consistency across forms), and internal consistency coefficients (consistency across items). There are four main methods of reliability analysis: the test-retest reliability method, the parallel-form reliability method, the split-half reliability method, and the alpha reliability coefficient method. Furthermore, the Cronbach alpha coefficient is the most commonly used reliability coefficient. The alpha coefficient evaluates the consistency between the scores of the items in the scale, which is an internal consistency coefficient. The “Cronbach alpha coefficient” (internal consistency reliability) index to test the stability and homogeneity of the scale is applied to the reliability analysis of attitude and opinion-based questionnaires (scales). The reliability coefficient of the total scale should preferably be above 0.8, and between 0.7 and 0.8 is acceptable; the reliability coefficient of the subscales should preferably be above 0.7, and between 0.6 and 0.7 is fine; the Cronbach alpha coefficient should be considered to reformulate the questionnaire if it is below 0.6.
Validity refers that how to measure the characteristics of the items and reflects the truthfulness and accuracy of the measurement. The scales used in this study, also known as the “Likert scale,” consists of statements that reflect an individual’s attitude or perception.
- Reliability and Validity analysis of the questionnaire. See Table 3.3
readxl::read_excel("./files/Table3-3.xlsx") %>%
gt::gt(caption = "Reliability and Validity analysis of the questionnaire") %>%
gt::cols_width(
everything() ~ px(200)
) %>%
fmt_missing(
missing_text = ""
)| Variable | Number of projects | Cronbach α |
|---|---|---|
| Employee Creativity （Dependent variable） | 13 | 0.960 |
| Employee's motivation for knowledge sharing （Inpendent variable） | 18 | 0.923 |
The reliability analysis shows that Cronbach’s alpha coefficient for employee creativity (dependent variable) is 0.96 and for employee’s motivation for knowledge sharing (independent variable) is 0.923, which indicates the reliability of the questionnaire for both independent and dependent variables is preferable. The overall reliability of the questionnaire is high.