1. Introduction
Knowledge Management Process (KMP) is a vital unit of Knowledge-Sharing (KS). In knowledge-based organizations such as a university, enhanced specialization in Knowledge Management (KM) works as a catalyst for boosting collaboration and exploration (Iqbal et al., 2019; Ramjeawon & Rowley, 2018). Higher education institutes (HEIs) transformational shift due to research and economic revolution has outpaced its old-fashioned teaching myth (Ramjeawon & Rowley, 2018). Currently, HEIs are thought of as institutions that specialize in vigorous and innovative knowledge production and dissemination (Bano & Taylor, 2015). HEIs are vital in knowledge creation, acquisition, storage, and sharing that can significantly contribute to social and economic progression (Ahmad et al., 2015; Fullwood & Rowley, 2017). HEIs require teamwork, association, and effort that significantly impact KM and can fill in as an impetus for enhanced collaboration and investigation that ultimately resulted in organizational performance (Ramjeawon & Rowley, 2018; Iqbal et al., 2019; Sahibzada et al., 2019). The metamorphic and reframing move of HEIs in research and economic reformation has dominated and surmounted outdated, musty, and decayed teaching tradition (Ramjeawon & Rowley, 2018).
The role of KM has become crucial for organizations in strategic and tactical planning and decision-making, problem-solving, dynamic learning, and achieving the full potential of an organization’s assets (Anantatmula & Kanungo, 2010; Gan, Ryan, & Gururajan, 2006; Okunoye & Karsten, 2002). This shows the need for knowledge planning, knowledge organizing, knowledge leadership, and access to knowledge effectively and efficiently (Piri & Asefzadeh, 2006). However, many organizations do not effectively apply KM as a strategic vision to their mission and goals, and in case they have applied KM, they have failed (Gholipour, Jandaghi, & Hosseinzadeh, 2010). Hongdiyanto et al. (2020) stated that entrepreneurial learning influences the entrepreneurial intention of Indonesian women with the help of two mediations - perceived behavioral control and attitude toward entrepreneurship. The results concluded that subjective norms had no significant influence on the entrepreneurial intention of Indonesian women. According to statistics, 84% of KM programs have failed. Even though many studies have been conducted on KM, implementation of KM in organizations is still complicated and difficult. Hence, HEIs need to extract innovative knowledge and disseminate it as extensively as never before (Bano & Taylor, 2015). HEI’s are essential for knowledge creation, acquisition, storage, sharing, and utilization, and in this manner add to the social and financial advancement and development. HEIs can enhance their processes and services, that is, teaching, instructing, learning, administration, research, curriculum development, management, and strategic planning with effective KM (Tan, 2016; Ahmed et al., 2015; Fullwood & Rowley, 2017; Iqbal et al., 2019).
Javaid, Soroya, and Mahmood. (2020) examined that the faculty members were familiar with the importance of KS and were eager to share their knowledge and expertise with others. The study showed that organizational factors played a significant role in enhancing the KS attitude of university teachers. The impact of these factors on KS attitude was significant. KS is a willful practice within which participants disseminate or exchange their existent knowledge and build further knowledge by using critical thinking, explanation, clarification, and reflection from diverse perspectives. With the growing concept of knowledge societies and knowledgebased economies, KS is growing in importance in every sector of life. Considering the importance of the subject, this study was conducted within the area of KS in HEIs. The objective of the study was to explore the KS attitude and therefore the impact KM processes on KS attitude of university teachers in Quetta, Pakistan.
HEIs significantly impact KM and may fill in as an impetus for enhanced cooperation and investigation (Iqbal et al., 2019) that ultimately result in improved KS attitude of university teachers in Pakistan. HEI’s are knowledgeintensive, knowledge-creating, and knowledge-sharing organizations (Ahmad et al., 2015 &; Ramjeawon & Rowley, 2018). The main focus of universities is on learning, creating, and distributing knowledge that they have. Universities are very significant and are regarded as one of the largest official social organization in terms of variety and size (Okunoye & Karsten, 2002). The process of storing information by HEIs is not new; however, new information and knowledge shared among them are new for organizations. The employees of one organization can utilize knowledge and information (which is new for them) received from other organizations, which ultimately helps them to achieve organizational goals. This new information can be used as an analytical tool by HEIs to fulfill knowledge deficiencies (Keramati & Azadeh, 2007).
HEIs manage, oversee, mix, and share information and knowledge among the faculty (Sohail & Daud, 2009). Faculty members work together and share knowledge in HEIs (Seonghee & Ju, 2008) and hence are called knowledge workforces. Bello and Oyekunle (2014) posited that in the context of HEIs as a center of knowledge, KS among knowledge holders may help in enhancing knowledge status within the university environment. As an employee, a member shares and transfers valuable information to others in an organization, and then such information and knowledge become a part of the organizational knowledge (Rhodes et al., 2008).
This research shows how organizational learning environment impacts teachers’ KS behavior. The learning environment of HEIs provide an educational environment in which the teacher learns, and they become more willing to share their knowledge among their colleagues and their students. The learning environment provides a stage of sustainable development based on active teaching, learning, and sharing knowledge and experience
An individual’s behavioral intention (education institute employee) cannot be the exclusive determinant of behavior where an individual’s control over the behavior is incomplete. By adding the “perceived behavioral control”, the theory of planned behavior (TPB) explains the relationship and association between actual behavior and behavior intentions. The TPB is important and provides predictions about the teaching behavior intention as compared to the theory of reason action (TRA). Moreover, both these theories can explain a person’s social behavior in the best possible manner by considering social norms as a vital variable.
From the perspective of HEIs, an academic organization like universities are a knowledge-based organization and their main object is knowledge creation, knowledge development, and knowledge sharing. The knowledge created by universities is shared with other institutions through the cooperative process thereby creating a continuous learning culture and improvement within the organization and outside the organization and enhancing the expertise of the individuals, which in turn increases the aptitude and excellence of research undertaken by HEIs. (Goh & Sandhu, 2013). Furthermore, KM enablers refer to all those features that ultimately influence radical growth in KM processes such as knowledge-oriented leadership (Thani & Mirkamali, 2018). Several prior studies have demonstrated the separate or simultaneous positive impact of KM enablers and processes on the performance or proficiency of firms (Gold, Malhotra, & Segars, 2001; Chiu & Chen, 2016; Shahzad et al., 2016; Iqbal et al., 2019).
Successful KM necessitates HEI’s to involve their employees to build trust, personal significance, and satisfaction to support and drive knowledge. Organizations that struggle with implementing sustainable KM can use internal marketing (IM) to enable professional and organizational interactions. Consequently, scholars call for empirical examination of facilitating part of organizational aspects (that is IM) in the effective enactment of KM processes in HEIs (Sahibzada et al., 2019)
The main objectives of the HEIs employees are teaching and dissemination of knowledge. However, instead of only teaching and dissemination of knowledge, the employees must initiate research to produce new knowledge, as such, it becomes their duty to spread new knowledge. Being the producer of new knowledge, they can contribute to effectively achieving organizational goals. They can efficiently share knowledge within the organization. The lack of KM and KS skills among employees in an organization can lead to underutilization of organization resources and ultimately the long-term goals will not be achieved; therefore, it is important to build KS skills among the employees of an organization.
Past studies focused on KM that refers to what individuals or teams of employees know or know how to do (social and human knowledge as well as the organization’s processes, routines, tools, and rules (structured Knowledge). Previous research also emphasized KS among employees. However, very few studies focused on the organizational learning environment that impacts teachers’ intention of sharing knowledge among employees. By creating a learning environment, employees feel safe expressing their views about work, and employees are encouraged to take the risk and to adopt change. By creating a smooth organizational learning environment, HEIs can achieve targeted goals.
2. Literature Review
KM is vital for the organization to train its employees with new knowledge and techniques through training and development programs; therefore, this concept is gained fame in the twenty-first century. The term KM was first introduced by Davenport, De Long, and Beers (1998). Despite its importance for an organization, there are certain barriers in the KS process because employees think that knowledge is a valuable thing, therefore, its sharing process is not natural. Thus, employees are reluctant to share their new skills and knowledge within the organization (Riege, 2005). However, it’s surprising that when employees are rewarded with incentives they are more willing to share their knowledge among other employees within the organization. Rowley (2000) argues that HEIs are in the knowledge business because they are involved in knowledge creation, knowledge dissemination, and learning. Davenport, De Long, and Beers (1998) stated that the KM concept is associated with the exploration and creation of new knowledge, and it is considered as an asset of an organization, as a result, it helps employees to achieve their organizational objective. In the context of HEIs, the main objective of KM is to ensure that knowledge is disseminated among professors, researchers, and students. The ultimate purpose is to include corporate professors, researchers, and students within the ambit of KM.
The essence of active KM practices cannot be overemphasized in HEIs. As a result, knowledge usage which offers benefits to both the organization and knowledge workers indicates the ability to utilize organizational knowledge resources to generate a competitive advantage for any organization. Knowledge-based organizations, substantially aligned with KM architecture, ensure the appropriate quantity and type of knowledge accession and utilization by the appropriate people, at the appropriate place and at the appropriate time (Kianto, Vanhala, & Heilmann, 2016; Butt et al., 2018; Shujahat et al., 2018; Sahibzada et al., 2019). Moreover, knowledge is the major input of knowledge workforces; thus, ideal knowledge provision to the knowledge workers through KM, certainly improve their satisfaction (Kianto, Vanhala, & Heilmann, 2016; Sahibzada et al., 2020).
Furthermore, prior studies overlooked the mediating role of subjective norms in the relation between KM processes and the performance of HEIs. This study, in HEIs context, recommends the knowledge workers familiarity with KM could extensively meliorate their total fulfillment. Opfer, Pedder, and Lavicza (2011) found that tacit knowledge workers could generate higher profits for any institute merely by sharing and applying personal knowledge. If universities want to gain the maximum from their intellectual capital and compete efficiently in the global marketplace, they need to focus on the KS among their employees (Sohail & Daud, 2009). Comparatively insufficient studies have been conducted on KS in HEIs (Fullwood, Rowley, & Delbridge, 2013; Goh & Sandhu, 2013; Howell & Annansingh, 2013; Seonghee & Boryung, 2008; Nordin, 2012; Sohail & Daud, 2009; Wang & Noe, 2010). There is a positive relationship between the dimensions of organizational learning mechanisms and KS behavior and a positive and significant relationship between the dimensions of organizational learning mechanisms and teachers’ professional development ability. Providing development and learning needs, respectively have the most impact on teachers’ professional development ability. (Kalan et al., 2016; Ahmed et al., 2015).
This research fills the identified knowledge gaps of KM in HEIs and in doing so, makes numerous theoretical contributions to the current literature by assessing the interrelationship between KM processes and KS attitude and subjective norms (Lu & Wang, 2018). The study preliminary assesses the impact of KM processes and further ascertains whether subjective norms mediate the relationship between KM processes and KS attitude. This would help in providing a source for understanding the mediating mechanism through which KM processes affect the performance of HEIs. With significantly limited research on the role of KM in Quetta HEIs, the study would expressively help in understanding and highlighting the role of KM processes in Quetta HEIs.
2.1. Knowledge Management (KM) Processes
KM processes are demarcated as actions related to knowledge creation, acquisition, storage, sharing, and utilization that improve employee performance. (Barley, Treem, & Kuhn 2018; Nonaka &Takeuchi 1995; Iqbal et al. 2019; Teixeira, Oliveira, & Curado 2018). Moreover, KM enablers perceive all those aspects which ultimately influence radical growth in KM processes (Thani & Mirkamali 2018; Naqshbandi & Jasimuddin 2018; Ramezani et al., 2017). Several prior studies have demonstrated the separate or simultaneous positive impact of KM enablers and processes on KS attitude of university teachers (Gold, Malhotra, & Segars 2001; Iqbal et al., 2019; Sahibzada et al., 2020). An assessment of the present literature related to KM processes in HEIs has helped to spot significant gaps that require to be filled. Due to the importance of knowledge in the organizations, and in the contemporary context of high competitiveness between organizations, KM and organizational learning culture are considered key concepts in both managerial and academic settings (Schmitz et al., 2014).
HEIs are important to study since HEIs’ activities in Pakistan are at the preliminary phase (Epstein, 2017; Turner & Acker, 2017). KM processes are usually well explained as actions associated with knowledge creation, that improve organizational competitiveness (Sahibzada et al., 2020). KM processes, being systemic activities of organizational competencies, are considered vital by the researchers (Chang & Chuang, 2011; Alaar, Mohammed, & Bustamam, 2016). It is worth noting that KS is an extensive object of researchers in HEIs while ignoring the empirical exploration regarding knowledge acquisition and utilization in HEIs at large (Tan & Noor, 2013). Knowledge creation can be defined as the development of new concepts and ideas through the connection between explicit and tacit knowledge in the personnel’s mind (Bontis, Crossan, & Hulland, 2002).
Knowledge acquisition is defined as the acquisition of ideas, knowledge, and skills that multiply the current pile of knowledge (Holsapple & Singh, 2001; Tiwana, 2002; Choo, 2003). Knowledge storage is the knowledge that must be efficiently managed through its storage so that employees can access it conveniently (Davenport & Prusak, 1998). KS is a systematic propagation of knowledge within the limits of organizations (Yang, Lai, & Yu, 2006). Hence, in the case of HEIs, it is fostering academicians’ research collaboration (Tan & Noor, 2013). Knowledge utilization is a mixture of operational, technical and social aspects (Pasha & Pasha, 2012); it is the application of knowledge to organizational operations and processes to produce valuable output in terms of products and services (Iqbal et al., 2019).
2.2. The Knowledge Sharing Attitude (KSA)
The knowledge base organization provides core value in the market and create a competitive edge (Nonaka, 1994). The knowledge transfer takes place through employees because they share their experience, skill with other employees of an organization thus, knowledge is the core of an organization as a result, and through employees the process of KS takes place. From the above conceptualization of the KS concept, it can be defined as the process through which employees exchange their ideas and knowledge through the process of discussion and contribute to new ideas. Employees work in a team to achieve organizational goals (Alam et al., 2009).
The KSA within the organizations depends upon several factors. Employees’ tendencies are vital in this regard. Some of the researchers argued that two factors are important, the first one is the attitude and the second one is SN. It is observed that these two factors that are present in employees working in the organization influence KS. It means that attitude and the SN determine the behavior of an employee whether he/she is willing to share knowledge and expertise or not within the organization (Bock et al., 2005).
The researchers conducted studies to explain KS with different theories, and one of the famous theories is TPB. It explains the KS capabilities within individuals (Ajzen, 1991). This theory also concluded that the intention of the employee is considered as the motivational factor that influences or determines the behavior, thus, the stronger the intentions, the more probability exists to perform the behavior accordingly. Two famous scholars Chatzoglou & Vraimaki (2009) conducted a study in Greece on bank employees. This study was based upon the TPB of the employees. The empirical studied concluded that employees’ KS behavior depends upon their intention. However, another factor was also identified, which was the social norm. That is, employees’ KS behavior depends upon the external social pressure whether to share knowledge or not within the organization. Furthermore, the most important factor that determines the employee intention to share knowledge with other employee in the organization is PBC.
2.3. Knowledge Sharing in HEIs
It is essential to understand the fact when employees show the intention to share skills, experience, and knowledge with their fellow members. Similarly, it is also important for the organization to understand their employees’ skills from a research and practical perspective because sometimes they are not ready to share their knowledge and skills. It all depends upon the organization’s culture to create a KS environment. The previous empirical research showed that in the tertiary education system, there is a general perception that teachers showed reluctant behavior to share knowledge with their colleagues. The biggest challenge in managing knowledge is to change people’s behavior. That is, the biggest challenge that an organization faces with regard to KM is changing individual behavior, especially in terms of KS (Ruggles, 1998).
From the KM perspective, HEIs provide knowledge to society. They comprise highly skilled and educated faculty, who are experts in their relevant field of study. Although HEIs provide knowledgeable service to individuals, yet many HEIs’ academic staff do not show the intention and behavior to share knowledge with their colleagues and improve their performance. Various scholars found that HEIs are not utilizing their full potential to share knowledge. This situation also exists in HEIs in Quetta. The data and information are stored in the libraries of these universities; however, it is not properly managed and shared with the other staff member to contribute to the development of new knowledge (Mohayidin et al., 2007). The previous data can be helpful and provide useful insight over many topics to produce new knowledge (Mohayidin et al., 2007).
While most universities have taken initiative toward information technology communication, they have not created an environment to optimally share knowledge with other universities. Few empirical studies were conducted to investigate the KS behavior among universities (Ryu, Ho, & Han, 2003). Some of the empirical studies were conducted in Malaysia (Chatzoglou & Vraimaki, 2009; Cheng, Ho, & Lau, 2009; Ryu, Ho, & Han, 2003). The service industry was also considered while conducting research related to KS behavior among employees (Roberts, 2008). Moreover, empirical studies were also conducted to investigate the KS behavior among employees in the food and marketing industry (George, 2004). All these studies concluded that individuals who have a great amount of knowledge are unwilling to share knowledge. Moreover, these studies showed that organizations, both large and small, will only achieve a competitive advantage if they can integrate their employees’ knowledge, experience, expertise, and skills and make use of the most efficient management strategies in their daily operations. This includes KS and the translation of KS into reality.
2.4. Subjective Norms (SNs)
SNs can be defined as a general perception that is built from social pressure to carry on certain behavior, that is, “performing or not to performing a given behavior” (Armitage & Conner, 1999). SNs in society have some origin – ‘the perceived social pressure to perform or not to perform the behavior’. It is the combination of people’s perceptions that others think they should or should not perform the specific behavior and their motivation to comply with others’ wishes (Randall & Gibson, 1991; Sparks & Shepherd, 1992). According to Ajzen (1991) and Ajzen, Brown, & Carvajal (2004), the SN is based upon the collective general perception of the society that ultimately shows a specific behavior and all members of the society follow that behavior. The specific behavior based upon normative belief in society is called SN (Lu & Wang, 2018). From an organizational perspective, the SN is the specific behavior of the staff members shaped in such a way to comply with that specific behavior. (Randall & Gibson, 1991). It is also observed that SNs are dependent upon the other society members’ or individuals’ judgment about a specific performed behavior (Armitage & Conner, 1999).
In SNs, the individuals feel pressure from the other member of the society while performing a specific behavior because all the members of the society comply with rules and regulations of performing that specific behavior (Ajzen & Fishbein, 1980; Pavlou & Fygenson, 2006). The previous empirical studies’ results have shown that a positive relationship exists between SN and the intention to share knowledge. This can also be explained in organizational and societal context especially when an individual’s perception of the behavior is accepted, encouraged, and implemented (Bock et al., 2005; Karahanna & Straub, 1999; Taylor, 2006; Venkatesh & Davis, 2000). SNs, through normative and informational influences, minimize uncertainty with respect to whether the use of a system is appropriate. It appears that there is a positive relationship between SN and the intention to share knowledge (Evaristo & Karahanna, 1998).
2.5. Perceived Behavior Control (PBC)
Perceived behavioral control (PBC) is defined as a person’s or individual’s perception or intention of the ease or difficulty of performing the particular behavior. It consists of available control beliefs (an individual’s expectancies that future outcomes/events will result as a consequence of one’s behavior) of society. PBC is a function of the opportunities and resources an individual has (control beliefs) and the ‘assisting effect’ of those factors (perceived facilitation) (Randall & Gibson, 1991). Ajzen (1991) and Ajzen, Brown, & Carvajal (2004) defined PBC as “ the level of selfconfidence of an individual about their ability to perform the behavior based on how easy or difficult they perceive its performance as it relates to hindrances or facilitators”. Moreover, individual behavior is shaped upon the belief and acceptance of those factors determined by society to carry on that behavior or prohibit it (Ajzen, 1991). PBC factors are dispositional factors that refer to the individual’s beliefs about the perceived presence or absence of necessary opportunities and resources that facilitate or impede KS.
(Chennamaneni, 2007).
PBC is associated with internal as well as external factors that ultimately influence the KS in the organization. The internal factors include individual emotions, skills, and perceptions while the external factors are time and cooperation among employees (Ajzen, 1991). Randall & Gibson (1991) concluded that the perception of individuals is directly associated with the external and internal factors (for example, resources and opportunities) in the organization.
3. Research Methodology
HEIs of Pakistan encourages research culture, which leads to research activities at HEIs (Ha, Lo, & Wang 2016). Nevertheless, the initiatives taken by the HEC are not adequate and are indistinct for which an operative KM is unavoidable to counter the barriers of the knowledge-based economy (Feiz, Dehgani, & Farizadeh, 2019; Sahibzada et al., 2020).
KS is studied from different theoretical backgrounds; however, in organizational research, it is explored through the theory of Reasoned Action (TRA) (Casimir, Ngee, & Liou, 2012) and theory of Planned Behavior (TPB) (Lu & Wang, 2018; Jeon, Kim, & Koh, 2011); KS is also conceptualized from other theories such as the social capital theory, the theory of motivation, and social identity theory (Zhao et al., 2012). Instead of these theories, it is worthwhile, to mention that the social identity theory (Kim, Zheng, & Gupta, 2011) and social cognitive theory (Chiu, Hsu, & Wang, 2006) and its uses (Thuy & Swierczek, 2006; Tonteri et al., 2011) are the most successful theories that explain the KS behavior of the individuals in the best possible manner.
The present study adopted an integrated model of the theory and cross-sectional research for data collection. A questionnaire-based survey was conducted on permanent faculty members from different universities of Quetta, Pakistan. This study focuses on four universities operating in Quetta i.e. University of Balochistan (UOB), Sardar Bahadur Khan Women University (SBKWU), Balochistan University of Information, and Management Sciences (BUITEMS), and Alhamd Islamic University (AIU). The research data was adopted from previous studies by Fahimeh & Kermani (2011), Seba, Rowley, & Lambert (2012), and Smith (2015). The first questionnaire was based on demographic characteristics; Gender, Age, Marital status, Department, Work experience, Education (last degree attended from), Designation, Salary per month, and Nature of the organization, while the second part was based on measuring KM processes, KS attitude, subjective norms, perceived behavioral control, and intention of individuals to share knowledge. The dependent variable is the KS attitude. The responses for the variables were calculated using a fivepoint Likert-type scale (1 = Strongly disagree, 2 = Disagree, 3 = Neutral, 4 = Agree and 5 = Strongly agree).
4. Data Analysis Procedure
Research data was analyzed by statistical software SPSS and AMOS. For checking the validity of the data, the Confirmatory Factor Analysis (CFA) was used to test how well the measured variables represent the number of constructs. The validity tests i.e. convergent validity and discriminant validity were used to find out the validity of the data, and composite reliability and Cronbach alpha determination were used to find out the reliability of the data. The use of Structural Equation Modeling (SEM) in social sciences and business administration is nowadays widely used (Rehman et al., 2020; Chun, Lee, & Park, 2020; Goeltom et al., 2020). The descriptive statistics were analyzed as Frequencies; Mean, Standard Deviation (SD), Skewness, and Kurtosis and checked for the normality of the data. For hypothesis, SEM and Bootstrapping methods were used, especially, for mediation.
4.1. Descriptive Statistics
The respondents in the research were provided with five Likert scales. It ranged from strongly disagree to strongly agree. The results derived from the scale through statistical tests are given below in Table 1. The results of all seven variables are as follows; KM process (KMP) is 4.0375 and .70596, KS attitude (KSA) is 3.66 and .62, Subjective Norms (SN) is 3.78 and .65, and PBC is 3.73 and .65. The mean score outcome was correctly found in the research. The sample size of this research was large enough, therefore, outliers are excluded here. The mean value is mostly used for the measurement of central tendency (Saunders & Lewis, 2012).
Table 1: Descriptive Statistics
Mean | Std. Deviation | |
KMP | 4.0375 | .70596 |
KSA | 3.6600 | .62328 |
SN | 3.7867 | .65517 |
PBC | 3.7360 | .65829 |
4.2. Construct Validity and Reliability Assessment
There are a few measures that are useful for establishing validity and reliability such as Composite Reliability (CR) and Average Variance Extracted (AVE). The relationship of each variable to the underlying factor is expressed by factor loading The factor loading with a higher value than .50 is standard criteria. In the SEM approach, as a rule of thumb, 0.7 or higher factor loading represents that the factor extracts sufficient variance from that variable The accepted value of CR should be .70 and for AVE it should be .50 (Fornell & Larcker, 1981). Table 2 demonstrates the factor loading of each item that is greater than 0.50. The item having below .50 loadings are removed from further analysis. The CR for each item is more than 0.70 and the AVE of each item is more than 0.50 which shows the independent variables have achieved convergent validity that is sufficient for further regression analysis. The AVE values of KMP, KSA, SN, and PBC are 0.614, 0.600, 0.568, and .583 respectively, and composite reliability values are .863, .822, .797, and .874, respectively. Cronbach’s alpha test is specifically used by the researcher to measure the internal consistency in the research. The least accepted value is 0.70 while the maximum value that is accepted is <0.90; however, .50 - 0.70 is considered moderate value, and below 0.50 is considered as low reliability (Hinton, 2014). Table 3 shows the results of Cronbach’s alpha value and all variables have high reliabilities which highlight internal consistency for all the scales used in this study.
Table 2: Construct Reliability and Validity
Note: CR = Composite Reliability, AVE = Average Variance Extracted.
Table 3: Reliability Assessment
4.3. Structural Equation Modeling (SEM)
Linear regression assumes that there is little or no multicollinearity in the data. Multicollinearity occurs when the independent variables are too highly correlated with each other. The tolerance measures the influence of one independent variable on all other independent variables; the tolerance is calculated with an initial linear regression analysis. Tolerance is defined as T = 1 – R² for these first step regression analysis. With T < 0.1 there might be multicollinearity in the data and with T < 0.01 there certainly is. The variance inflation factor (VIF) identifies the correlation between independent variables and the strength of that correlation. The VIF of the linear regression is defined as VIF- 1/T. VIFs start at 1 and have no upper limit. A value of 1 indicates that there is no correlation between this independent variable and any others. VIFs > 5 indicates that multicollinearity is present. The value of Tolerance is .518 >.1 and the value of VIF is 1.931 < 5 showing that there is less multicollinearity between predictors. Moreover, to check multi-collinearity the correlation coefficient values among predictors need to be smaller than .85.
The model summary indicates that KMP is a dependent variable and KSA, SN, and PBC are predictors. The Durbin value is.904. The result attained during research analysis showed that attitude is positively correlated to KS. (β = 0.182). The outcome was obtained from the analysis that the attitude of the individual showed predictive association and relationship with the intention to share knowledge among the HEIs of Quetta. p = 0.004 < 0.05. The tolerance outcome value is (.518) and the value of the VIF outcome is (1.931).
It is imperative to relate and compare the results of the study with previous research. Therefore, the findings and outcomes of the research showed that they are consistent with the study of HEIs in Quetta (p = 0.004 < 0.05), while its Tolerance is (.518) and VIF is (1.931). Findings are consistent with the study by Bock et al. (2005), Fahimeh and Kermani (2011), Goh and Sandhu (2013), and Smith (2015). The research of these scholars provided guidance and a framework to initiate this research. The study of these researchers also indicated that attitude is positively correlated with the intention to share knowledge among the staff members of HEIs.
The quantitative analysis of this research indicated that the SN variable has a relationship with KSA and this relationship is positively correlated. Hence, it is concluded from the results as shown in Table 4 that SN predicts the intention to share knowledge in a significant manner. (p = 0.000 < 0.05) while its Tolerance is (.447) and VIF is (2.237). The results of the research were found consistent with the study by Elogie (2010). It is worthwhile to mention that scale was taken from his study to measure the PBC.
Table 4: Perceived Behavior Control (PBC) has mediating Impact on Knowledge Management Process (KMP) on Knowledge Sharing Attitude (KSA)
Table 5: Subjective Norms (SN) has a Mediating Impact Impact on Knowledge Management Process (KMP) on Knowledge Sharing Attitude (KSA)
The result of ANOVA analysis was derived through the multiple regression analysis model. Following the result, it has been critically considered that the key functionality of ANOVA depends on Frequency analysis. Frequency is denoted by F=61.773 which shows a variance in the model at a significance level of 0.000. However, the value of Residual and Regression in ANOVA models in terms of Sum of Square is (60.958+97.421=158.379), Degree of Freedom is denoted by df and it is (3296=299) and the Mean square is (20.319, .329). The significance value is 0.000 which shows that the overall result is significant. As it is statistically proved when the value is less or equal to 0.5 and not greater than it shows that the relationship is statistically significant.
5. Conclusion and Discussion
The main objective of conducting this research was to determine the impact of the organizational learning environment on the teacher’s KS intention among academic staff at the HEIs of Quetta City. The first main concern of this study was to examine the impact of the organizational learning environment on KS intentions with mediation through Attitude, SN, and PBC. The second objective was to determine the KS intentions between foreign and local qualified male/ female faculty staff of the HEIs of Quetta. This study analyzed how KM has an impact on the teacher’s KS intention.
The learning environment of HEI provides an educational environment which the teacher learns, and they become more willing to share their knowledge among their colleagues and their student. The learning environment of the organizations provides a stage for sharing their knowledge and experience. The TPB is predominantly used to conceptualize the teacher’s non-volitional intention which cannot be exactly explained by incorporating TRA. An individual’s behavioral intention of academic staff (education institute employee) cannot be the exclusive determinant of behavior where an individual’s control over the behavior is incomplete. By adding the “perceived behavioral control”, the TPB explains the relationship and association between actual behavior and behavior intentions. The TPB is important and provides predictions about the behavior intention of the academic staff of the HEIs as compared to the TRA. Moreover, both these theories can explain a person’s social behavior in the best possible manner by considering social norms as a vital variable.
6. Limitation and Practical Implication
The limitations of this study opened new research horizons, such as: to overcome the generalizability problem that one should consider while using the random sampling method. A general problem with random sampling is that you could, by chance, miss out on a particular group in the sample. If the results are applied to a very narrow population or in a very specific situation, the results have poor generalizability. The targeted population of this study was the academic staff of HEIs. The prime reason for the selection of HEIs is that faculty staff (teachers) are creating a conducive study environment at universities in Quetta. Due to the low-cost issue, this study targeted only four universities that were operating in Quetta city. Colleges, schools, and other universities that are far from the city could be added when considering future research on this study. Moreover, this research paper targeted only academic staff (teachers) of the HEIs rather than admin staff, HR staff, accountants, and student affairs staff. This study was based on a paper-based questionnaire.
The cross-sectional research method was used for data collection. Closed-ended questionnaires were used, and open-ended questions and detailed interviews of the faculty staff were not taken due to the shortage of time. A certified proportionate method can be used for sample size in place of random sampling when considering future research on this study. This research explained the concept of KS behavior by incorporating the theory TPB in the KMP context, so its measurement was based on statistical logic and casual design, and the strategy in this study was based on an integrated model and cross-sectional research for data collection. Due to the limited time frame, this study was conducted as a quantitative study. This study could be conducted as a qualitative study when considering future research on this study.
The findings of this study have several practical implications for HEIs. First, this study advocates that HEIs should give more support to their employees. Moreover, they should be given environmental equipment and information technology, for example, updated software. HEIs must create an environment of KS among faculty and admin staff. The learning environment should be established in the HEIs to facilitate KS within the boundary of HEIs. The administrative issues and workload of the HEIs are the main hindrance to a KS environment. The research environment should be created, and the head of departments should facilitate that environment.
Thus, leaders of institutes should offer more training and teaching support to teachers and minimize their administrative load to facilitate KS intentions and the head of the department should allow their academic staff to share their knowledge with other members of the department. With the fast establishment of KM departments in universities, lecturers, assistant professors, and professors of these departments may have greater issues concerning KS. As such, these academic staff should be willing to adjust themselves in a learning environment that fosters KS. It is suggested that they should learn and give strength to the actions of KS and individual abilities to enable the HEIs to sustainably move forward.
In an organizational learning environment, the intention to share knowledge depends on the teacher’s attitude (willingness), PBC, and SN. When senior faculty members see that junior faculty members want to share new ideas in the department then it should be the responsibility of the senior faculty staff to reduce the barriers and allow them to openly share their idea, experience, or knowledge. The senior member also must provide them a platform such as a seminar or meeting with other academic staff, and conferences and training where he/she can easily share their knowledge; this will increase the junior faculty members’ confidence. According to this, a senior member can easily change the attitude, PBC, and SN of the academic staff. It will also change the behavior of those faculty staff members who are not willing or want to hide their knowledge and experiences.
Finally, the impact of the characteristics of leaders in the decision-making model on KS, and the effects of teachers’ KS intention among academic staff impact KMP; therefore, the academic staff must co-operate with that teacher in the challenges of writing research papers and publishing such research papers and these future research papers should be studied by adopting both qualitative and quantitative research approaches. Additionally, when academic staff work together and share their knowledge then they can easily change their environment into the learning environment and in that way, they can achieve their organizational goal and for this purpose, it is necessary to identify the “social norm” of an individual.
References
- Ahmed, N., Lodhi, S., Zaman, K., & Nasreen, I. (2015). Knowledge management: A gateway for organizational performance. Journal of Knowledge Management, 8(3), 859-876. https://doi/org/10.1007/s13132-015-0282-3
- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
- Ajzen, I., Brown, T. C., & Carvajal, F. (2004). Explaining the discrepancy between intentions and actions: The case of hypothetical bias in contingent valuation. Personality and Social Psychology Bulletin, 30(9), 1108-1121. https://doi.org/10.1177/0146167204264079
- Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ : Prentice-Hall
- Alaarj, S., Mohamed, Z. A., & Bustamam, U. S. A (2016). Mediating role of trust in the effects of knowledge management capabilities on organizational performance. Procedia - Social and Behavioral Sciences, 235, 729-738. https://doi.org/10.1016/j.sbspro.2016.11.074
- Alam, S. S., Abdullah, Z., Ishak, N. A., & Zain, Z. M. (2009). Assessing knowledge sharing behavior among employees in SMEs: An empirical study. International Business Research, 2(2), 115. https://doi.org/10.5539/ibr.v2n2p115
- Anantatmula, V. S., & Kanungo, S. (2010). Modeling enablers for successful KM implementation. Journal of Knowledge Management, 14(1), 100-113. https://doi.org/10.1108/13673271011015598
- Armitage, C. J., & Conner, M. (1999). The theory of planned behaviour: Assessment of predictive validity and perceived control. British Journal of Social Psychology, 38(1), 35-54. https://doi.org/10.1348/014466699164022
- Bano, S., & Taylor, J. (2015). Universities and knowledge-based economy: Perceptions from a developing country. Higher Education Research & Development, 34(2), 242-255. https://doi.org/10.1080/07294360.2014.956696
- Barley, W. C., Treem, J. W., & Kuhn, T. (2018). Valuing multiple trajectories of knowledge: A critical review and agenda for knowledge management research. Academy of Management Annals, 12(1), 278-317. https://doi.org/10.5465/annals.2016.0041
- Bello, O. W., & Oyekunle, R. (2014). Attitude, perceptions, and motivation towards knowledge sharing: Views from universities in Kwara state, Nigeria. African Journal of Library, Archives and Information Science, 24(2), 123-134.
- Bock, G.W., Zmud, R. W., Kim, Y. G., & Lee, J. N. (2005). Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Quarterly, 87-111. https://doi.org/10.2307/25148669
- Bontis, N., Crossan, M., & Hulland, J. (2002). Managing an organizational learning system by aligning stocks and flows. Journal of Management Studies, 39(4), 437-469. https://doi.org/10.1111/1467-6486.t01-1-00299
- Butt, M. A., Nawaz, F., Rahim, S. H., & Sousa, M. J. (2018). Individual knowledge management engagement, knowledge-worker productivity, and innovation performance in knowledge-based organizations: the implications for knowledge processes and knowledge-based systems. Computational and Mathematical Organization Theory, 25(3), 1-21. https://doi.org/10.1007/s10588-018-9270-z
- Casimir, G., Ngee K. N. Y., & Liou P. C. C. (2012). Using IT to share knowledge and TRA. Journal of Knowledge Management, 16(3), 461-479. https://doi.org/10.1108/13673271211238779
- Chang, H. H., & Chuang, S. S. (2011). Social capital and individual motivations on knowledge sharing: Participant involvement as a moderator. Information and Management, 28(3), 309-342. https://doi.org/10.1016/j.im.2010.11.001
- Chatzoglou, P. D., & Vraimaki, E. (2009). Knowledge-sharing behavior of bank employees in Greece. Business Process Management Journal, 15(2), 245-266. https://doi.org/10.1108/14637150910949470
- Cheng, M. Y., Ho, J. S. Y., & Lau, P. M. (2009). Knowledge sharing in academic institutions: A study of multimedia university Malaysia. Electronic Journal of Knowledge Management, 7(3), 1-12.
- Chennamaneni, A. (2007). Determinants of knowledge sharing behaviors: Developing and testing an integrated theoretical model. Doctoral thesis, University of Texas, Arlington. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.629.858&rep=rep1&type=pdf
- Chiu, C. N., & Chen, H. H. (2016). The study of knowledge management capability and organizational effectiveness in Taiwanese public utility: The mediator role of organizational commitment. Springer Plus, 5(1), 1-34. https://doi.org/10.1186/s40064-016-3173-6
- Chiu, C. M., Hsu, M. H., & Wang, E. T. (2006). Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems, 42(3), 1872-1888. https://doi.org/10.1016/j.dss.2006.04.001
- Choo, C. W. (2003). Perspectives on managing knowledge in an organization. Cataloging & Classification Quarterly, 37(1-2), 205-220. https://doi.org/10.1300/J104v37n01_14
- Chun, T. Y., Lee, D. K., & Park, N. H. (2020). The effect of marketing activities on brand recognition, brand familiarity, and purchase intention on the SNS of franchise companies. Journal of Asian Finance, Economics, and Business, 7(11), 955-966. https://doi.org/10.13106/jafeb.2020.vol7.no11.955
- Davenport, T. H., De Long, D. W., & Beers, M. C. (1998). Successful knowledge management projects. Sloan Management Review, 39(2), 43.
- Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Boston, MA: Harvard Business School Press.
- Elogie, A. (2010). Knowledge Sharing Behaviour of Academics at the University of Ibadan. Masters Project, Africa Regional Centre for Information Science, University of Ibadan, Nigeria.
- Epstein, I. (2017). Chinese education: Problems, policies, and prospects. London, UK: Routledge
- Evaristo, J. R., & Karahanna, E. (1998). The impact of mental workload in the evaluation of innovations. Information Systems Innovation and Diffusion: Issues and Directions, 48.
- Fahimeh, B., & Kermani, Z. J. (2011). Knowledge sharing behavior influences: A case of library and information science faculties in Iran. Malaysian Journal of Library & Information Science, 16(1), 1-14. https://mjlis.um.edu.my/article/view/6670
- Feiz. D., Dehgani, S. M., & Farizadeh, H. (2019). The effect of knowledge sharing on the psychological empowerment in higher education mediated by organizational memory. Studies in Higher Education, 44(1), 3-19. https://doi.org/10.1080/0.075079.2017.138595
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1) 39-50. https://doi.org/10.1177/002224378101800104
- Fullwood, R., & Rowley, J. (2017). An investigation of factors affecting knowledge sharing amongst UK academics. Journal of Knowledge Management, 21(1), 1254-1271. https://doi.org/10.1108/JKM-07-2016-0274/full/html
- Fullwood, R., Rowley, J., & Delbridge, R. (2013). Knowledge sharing amongst academics in UK universities. Journal of Knowledge Management, 17(1), 1-12. https://doi.org/10.1108/13673271311300831
- Gan, G. G. G., Ryan, C., & Gururajan, R. (2006). Soft enablers of knowledge management in Malaysian companies-a qualitative study. In: 17th Australasian Conference on Information Systems (ACIS) (pp. 1-10). 6-8 Dec 2006, Adelaide. https://aisel.aisnet.org/acis2006/101
- George, J. F. (2004). The theory of planned behavior and Internet purchasing. Internet Research, 14(3), 198-212. https://doi.org/10.1108/10662240410542634
- Gholipour, R., Jandaghi, G., & Hosseinzadeh, S. A. A. (2010). Explanation of knowledge management enabler as a latent variable: A case study of SMEs in Iran. African Journal of Business Management, 4(9), 1863-1872. https://doi.org/10.5897/AJBM.9000277
- Goeltom, V. A. H., Kristiana, Y., Juliana, J., Bernato, I., & Pramono, R. (2020). The Effect of Service Quality and Value of Five-Star Hotel Services on Behavioral Intentions with the Role of Consumer Satisfaction as Mediator. Journal of Asian Finance, Economics, and Business, 7(11), 967-976. https://doi.org/10.13106/jafeb.2020.vol7.no11.967
- Goh, S. K., & Sandhu, M. S. (2013). Affiliation, reciprocal relationships, and peer pressure in knowledge sharing in public Universities in Malaysia. Asian Social Science, 9(7), 290. https://doi.org/10.5539/ass.v9n7p290
- Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185-214. https://doi.org/10.1080/07421222.2001.11045669
- Ha, S. T., Lo, M. C., & Wang, Y. C. (2016). Releationship between knowledge management and organizational performance: A test on SMEs in Malayisa. Procedia- Social and Beaviorial Sciences, 224(2016), 184-189. https://doi.org/10.1016/j.sbspro.2016.05.438
- Hinton, P. R. (2014). Statistics explained. London, UK: Routledge.
- Holsapple, C. W., & Singh, M. (2001). Knowledge chain model: Activities for competitiveness. Expert Systems with Applications, 20(1), 77-98. https://doi.org/10.1016/S0957-4174(00)00050-6
- Hongdiyanto, C., Teofilus, T., Sutrisno, T. F. C. W., & Dewanti, P. S. P. (2020). The Effect of Entrepreneurial Learning on the entrepreneurial intention of Indonesian Women. Journal of Asian Finance, Economics, and Business, 7(9), 573-582. https://doi.org/10.13106/jafeb.2020.vol7.no9.573
- Howell, K. E., & Annansingh, F. (2013). Knowledge generation and sharing in UK universities: A tale of two cultures? International Journal of Information Management, 33(1), 32-39. https://doi.org/10.1016/j.ijinfomgt.2012.05.003
- Iqbal, A., Latif, F., Marimon, F., Sahibzada, U. F., & Hussain, S. (2019). From knowledge management to organizational performance: Modelling the mediating role of innovation and intellectual capital in higher education. Journal of Enterprise Information Management, 32(1), 36-59.. https://doi.org/10.1108/JEIM-04-2018-0083/full/html
- Lu, H., & Wang, J. (2018). The entrepreneurial intention of two patterns of planned behaviour and alertness: Empirical evidence in China. Journal of Asian Finance, Economics, and Business, 5(2), 63-72. https://doi.org/10.13106/jafeb.2018.vol5.no2.63
- Javaid, J., Soroya, S. H., & Mahmood, K. (2020). Impact of personal and organizational factors on knowledge sharing attitude of university teachers in Pakistan. The Electronic Library, 38(2), 317-336. https://doi.org/10.1108/EL-05-2019-0121
- Jeon, S., Kim, Y. G., & Koh, J. (2011). An integrative model for knowledge sharing in communities-of-practice. Journal of Knowledge Management, 15(2), 251-269. https://doi.org/10.1108/13673271111119682
- Kalan, S., Mohammad, S., Zirak, R., Azad, R., & Golparvar, B. (2016). Determining the relationship between organizational learning mechanisms and teachers' knowledge sharing behavior and professional development ability. International Journal of Organizational Leadership, 5(3). 218-230. https://doi.org/10.33844/ijol.2016.60191
- Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & Management, 35(4), 237-250. https://doi.org/10.1016/S0378-7206(98)00096-2
- Keramati, A., & Azadeh, M. (2007). Exploring the effects of top management's commitment to knowledge management success in academia: A case study. Paper presented at the Proceedings of World Academy of Science, Engineering and Technology, International Journal of Industrial and Manufacturing Engineering, 1(3), 59-64. https://doi.org/10.5281/zenodo.1075859
- Kianto, A., Vanhala, M., & Heilmann, P. (2016). The impact of knowledge management on job satisfaction. Journal of Knowledge Management, 20(4), 621-636. https://doi.org/10.1108/JKM-10-2015-0398
- Kim, H. W., Zheng, J. R., & Gupta, S. (2011). Examining knowledge contribution from the perspective of online identity in blogging communities. Computers in Human Behavior, 27(5), 1760-1770. https://doi.org/10.1016/j.chb.2011.03.003
- Mohayidin, M. G., Azirawani, N., Kamaruddin, M. N., & Margono, M. I. (2007). The application of knowledge management in enhancing the performance of Malaysian universities. Electronic Journal of Knowledge Management, 5(3), 301-312.
- Naqshbandi, M. M., & Jasimuddin, S.M. (2018). Knowledge-oriented leadership and open innovation: Role of knowledge management capability in France-based multinationals. International Business Review, Elsevier, 27(3), 701-713. https://doi.org/10.1016/j.ibusrev.2017.12.001
- Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14-37. https://doi.org/10.1287/orsc.5.1.14
- Nonaka, I., & Takeuchi, K. (1995). The knowledge-creating company. New York, NY: Oxford University Press.
- Nordin, N. (2011). The influence of leadership behavior and organizational commitment on organizational readiness for change in a higher learning institution. Asia Pacific Education Review, 13(2), 239-249. https://doi.org/10.1007/s12564-011-9200-y
- Okunoye, A., & Karsten, H. (2002). ITI as an enabler of knowledge management: Empirical perspective from research organizations in sub-Saharan Africa. Paper presented in Proceedings of the 35th Annual Hawaii International Conference on System Sciences (pp. 1-10). 10 Jan 2002, Big Island, HI, USA. https://doi.org./10.1109/HICSS.2002.994044
- Opfer, V. D., Pedder, D. J., & Lavicza, Z. (2011). The influence of school orientation to learning on teachers' professional learning change. School Effectiveness and School Improvement, 22(2), 193-214. https://doi.org/10.1080/09243453.2011.572078
- Pasha, M. A. R., & Pasha, S. (2012). A pragmatic approach for implementing knowledge management in Pakistani organizations using open source technologies. International Journal of Computer Applications, 49(7), 10-18. https://doi.org/10.5120/7638-0721
- Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 115-143. https://doi.org/10.2307/25148720
- Piri, Z., & Asefzadeh, S. (2006). How Knowledge Management (KM) can be applied to healthcare organizations? The Journal of Qazvin University of Medical Science, 10(1), 124-132.
- Ramezani, A., Fathain, M., & Tajdin, A. (2013). Investigating critical success factors of knowledge management in research organizations: A case study concerning one of the research organizations of Iran. Education, Business and Society: Contemporary Middle Eastern Issues, 6, 101-115. https://doi.org/10.1108/EBS-08-2012- 0037
- Ramjeawon, P. V., & Rowley, J. (2018). Knowledge Management (KM) in higher education institutions in Mauritius. International Journal of Educational Management, 32(2), 1319-1332. https://doi.org/10.1108/TLO-03-2017-0030
- Randall, D. M., & Gibson, A. M. (1991). Ethical decision making in the medical profession: An application of the theory of planned behavior. Journal of Business Ethics, 10(2), 111-122. https://doi.org/10.1007/BF00383614
- Rehman, K., Khan, H. H., Sarwar, B., Muhammad, N., Ahmed, W., & Rehman, Z. U. (2020). A multi-group analysis of risk management practices of public and private commercial banks. Journal of Asian Finance, Economics, and Business, 7(11), 893-904. https://doi.org/10.13106/jafeb.2020.vol7.no11.893
- Rhodes, J., Hung, R., Lok, P., Lien, B. Y., & Wu, C. H. (2008). Factors influencing organizational knowledge transfer: Implications for corporate performance. Journal of Knowledge Management, 12(3), 84-100. https://doi.org/10.1108/13673270810875886
- Riege, A. (2005). Three-dozen knowledge-sharing barriers managers must consider. Journal of Knowledge Management, 9(3), 18-35. https://doi.org/10.1108/13673270510602746
- Roberts, K. R. (2008). Using the theory of planned behavior to explore restaurant managers' support for employee food safety training. Kansas State University. Retrieved from http://hdl.handle.net/2097/591
- Rowley, J. (2000). Knowledge organization for a new millennium: principles and processes. Journal of Knowledge Management, 4(3), 217-223. https://doi.org/10.1108/13673270010350011
- Ruggles, R. (1998). The state of the notion: Knowledge management in practice. California Management Review, 40(3), 80-89. https://doi.org/10.2307/41165944
- Ryu, S., Ho, S. H., & Han, I. (2003). Knowledge sharing behavior of physicians in hospitals. Expert Systems with Applications, 25(1), 113-122. https://doi.org/10.1016/S0957-4174(03)00011-3
- Sahibzada, U. F., Cai., J., Latif, K. F., & Sahibzada, H. F. (2019). Knowledge management processes, knowledge of worker satisfaction, and organizational performance: Symmetric and asymmetric analysis. Aslib Journal of Information Management, 72(1), 112-129. https://doi.org/10.1108/AJIM-10-2019-0276
- Sahibzada, U. F., Cai., J., Latif, K. F., Shafait, Z., & Sahibzada, H. F. (2020). Interpreting the impact of knowledge management processes on organizational performance in Chinese higher education: Mediating role of knowledge worker productivity. Studies in Higher Education, 2(11). https://doi.org/10.1080/03075079.2020.1793930
- Saunders, M. N., & Lewis, P. (2012). Doing research in business & management: An essential guide to planning your project. London, UK: Pearson.
- Schmitz, S., Rebelo, T. M., Gracia, F. J., & Tomas, I. (2014). Learning culture and knowledge management processes: To what extent are they effectively related? Revistade Psicologiadel Trabajoydelas Organizaciones, 30(3), 113-121, https://doi.org/10.1016/j.rpto.2014.11.003
- Seba, I., Rowley, J., & Lambert, S. (2012). Factors affecting attitudes and intentions towards knowledge sharing in the Dubai Police Force. International Journal of Information Management, 32(4), 372-380. https://doi.org/10.1016/j.ijinfomgt.2011.12.003
- Seonghee, K., & Ju, B. (2008). An analysis of faculty perceptions: Attitudes toward knowledge sharing and collaboration in an academic institution. Library and Information Science Research, 30(4), 282-290. https://doi.org/10.1016/j.lisr.2008.04.003
- Shahzad, K., Bajwa, S.U., Siddiqi, A.F.I., Ahmid, F., & Raza Sultani, A. (2016). Integrating Knowledge Management (KM) strategies and processes to enhance organizational creativity and performance: An empirical investigation. Journal of Modelling in Management, 11(1), 154-179. https://doi.org/10.1108/JM2-07-2014-0061/full/html
- Shujahat, M., Ali, B., Nawaz, F., & Durst, S. (2018). Translating the impact of knowledge management into knowledge-based innovation: The neglected and mediating role of knowledgeworker satisfaction. Human Factors and Ergonomics in Manufacturing, 28(4), 200-212 https://doi.org/10.1002/hfm.20735
- Smith, A. Y. (2015). Attitude, subjective norm, and perceived behavioral control as indicators for nurse educators' intention to use critical thinking teaching strategies: A structural equation model analysis. Andrews University.
- Sohail, S., & Daud, S. (2007). Knowledge sharing in higher education institutions: Perspectives from Malaysia. VINE, 39(2), 125-142. https://doi.org/10.1108/03055720910988841
- Sparks, P., & Shepherd, R. (1992). Self-identity and the theory of planned behavior: Assessing the role of identification with green consumerism. Social Psychology Quarterly, 55(4), 388-399. https://doi.org/10.2307/2786955
- Tan, C. N. L. (2015). Enhancing knowledge sharing and research collaboration among academics: The role of knowledge management. Higher Education, 71(4), 525-556. https://doi.org/10.1007/s10734-015-9922-6
- Tan, C. N. L., & Noor, M. S. (2013). Knowledge management enablers, knowledge sharing, and research collaboration: a study of knowledge management at research universities in Malaysia. Asian Journal of Technology Innovation, 21(2), 251-276. https://doi.org/10.1080/19761597.2013.866314
- Taylor, E. Z. (2006). The effect of incentives on knowledge sharing in computer-mediated communication: An experimental investigation. Journal of Information Systems, 20(1), 103-116. doi:https://doi.org/10.2308/jis.2006.20.1.103
- Teixeira, E. K., Oliveira, M., & Curado, C. M. M. (2018). Knowledge management process arrangements and their impact on innovation. Business Information Review, 35(1), 29-38. https://doi/org/10.1177/0266382118757771
- Thani, F. N., & Mrikamali, S. M. (2018). Factors that enable knowledge creation in higher education: a structural model. Data Technologies and Applications, 52(1), 1-10. https://doi.org/10.1108/DTA-10-2016-0068/full/html.
- Tiwana, A. (2000). Knowledge management toolkit: Practical techniques for building a knowledge management system. Upper Saddle River, NJ: Prentice-Hall.
- Thuy, P., N., & Swierczek, F. W. (2006). Facilitators of organizational learning in design. The Learning Organization, 13(2), 186-201. https://doi.org/10.1108/09696470610645502
- Tonteri, L., Kosonen, M., Ellonen, H. K., & Tarkiainen, A. (2011). Antecedents of an experienced sense of virtual community. Computers in Human Behavior, 27(6), 2215-2223. https://doi.org/10.1016/j.chb.2011.06.018
- Turner, Y., & Acker, A. (2017). Education in new China: Shaping ideas at work. London, UK: Routledge
- Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
- Wang, S., & Noe, R. A. (2010) Knowledge sharing: A review and directions for future research. Human Resource Management Review, 20, 115-131. https://doi.org/10.1016/j.hrmr.2009.10.001
- Yang, J., Lai, F., &Yu, L. (2006). Harnessing value in knowledge acquisition and dissemination: Strategic sourcing in product development. Technology Management, 33(/3), 299-317. https://doi.org/10.1504/IJTM.2006.008317
- Zhao, L., Lu, Y., Wang, B., Chau, P. Y., & Zhang, L. (2012). Cultivating the sense of belonging and motivating user participation in virtual communities: A social capital perspective. International Journal of Information Management, 32(6), 574-588. https://doi.org/10.1016/j.ijinfomgt.2012.02.006
Cited by
- A Latent Factor (PLS-SEM) Approach: Assessing the Determinants of Effective Knowledge Transfer vol.8, pp.2, 2021, https://doi.org/10.13106/jafeb.2021.vol8.no2.0851
- Determinants of Business Education on Student Satisfaction in Higher Education: A Case Study in Cambodia vol.8, pp.3, 2021, https://doi.org/10.13106/jafeb.2021.vol8.no3.1405