Satinder Kumar
| Pooja Rani | Ruchika Jain | Kiran Sood | Simon Grima*
© 2023 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
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This research examines the effects of social media use on job performance, transactive memory capability (TMC) and the role of transactive memory capability as a mediator between job performance and social media use. The study is conducted on the teaching faculty member in the North of India's public universities. A snowball sampling has been employed, and 608 respondents who met the study's selection criteria have been identified. The hypothesis and numerous interactions between variables of this study were tested using Structural Equation Modeling. It has been found that social media has a significant and positive impact on job performance. This study has also indicated a partial mediating role of TMC in the relationship between social media use and job performance. The study adds to the empirical literature by demonstrating the positive effects of social media use by the teaching faculty on TMC development and job performance. It highlights that social media can be considered a legitimate communication tool to increase workplace connectivity. Faculties should understand how social media generates transactive memory capability so that they can use it more effectively. It also fills the gap by considering TMC among teaching faculties working together to store, retrieve and share data through social media in Indian Public universities.
social media use, transactive memory capability, job performance, structural equation modelling, Indian public universities
Nowadays, people may easily obtain the information they require online from any location at any time. People are now reliant on online sources rather than making an effort to remember the knowledge. Some claim that this phenomena led to a decline in people's capacity for memory [1]. However, Sparrow et al. [2] provided a fresh viewpoint with their investigation into how Google searches affect the human memory system. Instead of the knowledge itself, people tend to recall the location and method of acquisition.
Social media tools are being used by teams and organisations more and more to foster communication and organise information sharing among its members [3]. In other words, people perceive the external memory as a source of knowledge. This new memory structure has altered how information is processed, not decreasing human receptivity [2]. Explaining modern memory systems, which are used to recall repositories rather than information, is a fantastic method to teach the idea of transactive memory.
"Transactive memory" refers to a shared memory system that uses external storage to retrieve necessary information and knowledge. As a result, it differs from current memory systems, which rely on the individual's inadequate memory structure for important information and knowledge. Researchers in a study observed couples dating and came up with the concept of the transactive memory system [4]. It focuses on understanding the person with the necessary information and expertise rather than memorizing information and knowledge [5]. Most of the current research has focused on demonstrating the usefulness of transactive memory through organizational research. However, the increase in the influence of social media has increased the need for research into the memory of individual transactions [6].
Due to developments like the advent of various social network services, the widespread usage of smartphones, and the growing acceptance of mobile Internet, social media has a growing impact on society. Social media use has resulted in various benefits, including knowledge-sharing activities among users, the creation of human networks, communication with a wide range of people, and forming online interest groups. Social media users can now create a variety of human networks as a result of their creation. Social media has established itself as a different communication medium [7]. While customers use social media to acquire new information, businesses have developed different social media marketing strategies. The widespread adoption and use of social media have expanded the capacity of individuals to obtain and use such information.
Users of social media access the numerous pieces of information available on these platforms using their transactive memory. By using their transactive memory and removing unnecessary information to prevent information overload, they may quickly gather qualitative information. However, only some people have the same transactional memory at the individual level; rather, such memory is produced differentially depending on social environment and individual attributes. The degree of interpersonal connection, one's capacity for gathering and utilising knowledge, and one's willingness to do so can all influence individual variations. As a result, the ability to produce and use transactive memory, which differs between individuals, is termed as transactive memory capability [8]. As a result, studies on transactive memory at the organisational level have looked at the causal connection between transactive memory and group productivity [9-13]. Only a small number of studies on transactive memory from the viewpoint of the individuals within the organisation have been done.
This study explores the fundamental components that support the growth of transactive memory ability from the perspective of the individual within the organisation. It also takes into account how transactive memory influences how well people perform at work. This study also investigates the relationship between job performance and social media use, as well as the effects of social media use on transactive memory capacity and job performance.
The remainder of this article is structured as follows. In order to create a theoretical framework for research models to look at the impact of transactive memory ability on job performance via social media, Section 2 includes a literature review. While Section 4 demonstrates the study methodology and design, Section 3 explores hypotheses and provides a research model. Section 5 presents the findings, and Section 6 wraps up with a discussion of the study's contributions, implications, and restrictions as well as some possibly fruitful future research possibilities.
2.1 Social media
Social media systems use various technological tools that work together on a social platform to help people communicate and share information in a virtual social and workplace environment [14]. Organizations use social media to help them manage their knowledge [15]. Organizations use social media tools (blogs, internal networks, and wikis) to improve knowledge management processes. These technologies are usually adapted to the organization's context and the tools already in place [16, 17]. Web and mobile-based social media platforms that allow workers to engage with others, exchange and modify texts and documents, and observe all communicated messages and relationships are classified as enterprise social media in the organizational environment [18, 19].
Knowledge management processes are facilitated by social media technologies, which provide resources for interacting and communicating with others and facilitate collaboration in developing TMS within knowledge-oriented teams. Dialogue practices are quasi-structures that describe simple conversational principles to reduce uncertainty in communication, eventually leading to the creation of TMS [20]. Previous research has demonstrated that social media can effectively support communication practices [21]. The cognitive dimension of social capital is related to the formation and understanding of shared language, codes, narratives and communication patterns, which can be favourably increased through social media [22]. The information technologies that define social media are conducive to communicative behaviour that fosters knowledge management techniques. One of the most essential parts of knowledge management is the knowledge repository, which establishes ownership and supports the dissemination, use and development of new knowledge. Organizations use online blogs, discussion boards, wikis, and communication protocols so that users can collaborate with the organization's knowledge management methods [23].
Studies on knowledge-sharing activities and participation in virtual community spaces demonstrate that social media can help people improve their transactive memory. Social media is a tool to facilitate the building of human networks and to increase the sense of trust among vast groups of people as it allows participation, sharing and an open environment through bilateral interaction [5]. Individuals receive opportunities to build various knowledge and social capital when their transactive memory capability improves due to strengthening existing relationships and building new ones. People with high transactive memory capability can mix and consolidate information and knowledge gained through social media activities and uses this knowledge and information more efficiently than others in such social media environments. The social media environment increases the opportunities for individuals to enhance their transactive memory capability based on the trust gained through knowledge-sharing activities and interactions.
2.2 Transactive memory and transactive memory capability
Effective teamwork requires efficient distribution, retrieval, knowledge sharing among team members, and collective learning. It's important to have a system to manage this kind of complex knowledge management across teams [24]. TMS is based on the concept of transactive memory theory [4]. Memory is dependent on knowledge about another person's memory system and is called transactive memory. The availability of information from another person's memory can affect how one person stores, encodes and retrieves information. While a single person can only own transactive memory, a transactive memory system can exist between people working together to store, retrieve, and share data [4, 25].
TMS comprises three sub-constructs distinct from each other: expertise, reliability, and coordination [15, 23, 26, 27]. TMS helps team members to easily coordinate to understand the specific expertise of other members, build confidence in the knowledge (credibility) of the whole team and integrate their knowledge (coordination) among the members. Wegner [4] was the first to introduce TMS as a psychological theory, while Liang et al. [28] used it in a laboratory environment to investigate its effect on team performance. Previous empirical investigations have also studied the enhancement of TMS through learning, communication and collaboration [20, 26, 29]. Meanwhile, Zhong et al. [27] and Mell et al. [30] found that focusing on the effective use of TMS can have long-term benefits on team performance. For this transactive memory to be formed, three conditions must be fulfilled. First, it is necessary to assess the expertise space in terms of what knowledge anyone has. Second, trust in those who transfer knowledge must be based on cognition. Third, coordinating a wide variety of knowledge and understanding communication strategies is essential to properly employ other people's expertise [13].
Expertise space refers to the perception of people with whom a relationship has been established through interaction about a particular area of knowledge. It must be preceded by an exam with expertise in a particular field. This step is necessary to make it easier to take advantage of the option of using an external repository whenever a person needs specific information. Trust in specific knowledge possessed by specific people and trust in those who possess specific information is called cognition-based trust [5]. It provides an opportunity to evaluate and judge whether the particular knowledge one has with the knowledge one intends to derive is, in fact, credible. Finally, coordination ability refers to a person's ability to communicate with others to access external memory and acquire knowledge at an appropriate time. Even if the specialization space is properly done and cognition-based trust is built, the individual may only be able to implement transactive memory if appropriate coordination abilities are properly prepared.
Transactive memory studies can be examined from a team (group) and individual perspective. The transactive memory system is a collection of transactive memory that emerges at the team or group level [4, 9, 31]. The transactive memory system is a collection of mutually dependent transactive memories of individuals that make up a composite body. It is also Meta knowledge that members of a team or organization share [32, 33]. Transactive memory, on the other hand, deals with the Meta knowledge of an individual. It is based on the idea that one can experience and use the knowledge of others. As a result, people develop different transactive memories depending on their capabilities.
While research on transactive memory systems continues at the organizational level, research on systems from an individual perspective has begun around a decade [2, 6]. Due to the advancement of information technology and the widespread use of the Internet, people can now easily get the information and content they want online and connect with different people. Put another way, an environment allows people to improve and use their transactive memory online rather than simply in the real world or offline. Instead of slowly studying such material, people are now looking for ways to have the information and expertise they want to use more efficiently. This is shown in the Journal of Science's examination of recent changes in the human memory system [2]. People have recently preferred to obtain information through online searches rather than recalling all the necessary information and knowledge. In other words, instead of remembering everything, they now learn the keywords or strategies that allow them to quickly and accurately locate the information they need. In adapting to new technologies and changing environments, individuals seek ways to employ their memory capability more effectively than others [5]. Individual disparities will arise based on people's ability to employ transactive memory as this new memory system, known as a transactive memory system, becomes a highly important factor in enhancing individual performance in an ever-changing environment. Although the ability to employ transactive memory is generally established due to multiple social experiences, various individual factors may also play a role in its development. Therefore, this research mainly focuses on individual differences in transactive memory capability, or the ability to establish and use transactive memory capability [8].
There are two broad approaches to using transactive memory: The first step is to remember where essential information is stored on social media. This strategy involves memorizing the exact location of a book or website where relevant information can be found and laying the necessary groundwork for always-on access to the required knowledge. The second method involves using transactive memory, formed through interactions with other people with the necessary information. Because transactive memories created through this method become sources of knowledge gained through the creation of interpersonal interactions, they can aid in creating a more thorough and trustworthy information environment. The ability of others to access transactive memory via surrounding people was in the past intrinsically constrained by the lack of an Internet environment that allowed widespread human networks to be established. However, the development of social media environments has made it possible to improve transactive memory capability by creating interactions that are not limited by time or space [5, 23].
People can connect relationships with many other people, and there are many benefits to using social media as an external memory. It is possible to actively share knowledge and information because of these heightened relationships with others [34]. Establishing a favourable position in the network provides opportunities for improving the ability to access information and absorb knowledge [35, 36]. In addition, using external memory eliminates the need to recall all information and reduces memory overload, resulting in increased effectiveness and efficiency in performing professional tasks.
2.3 Social media and transactive memory capability
On a personal level, community-based online platforms inherently create more boundaries for building satisfying relationships and trust than in real situations. However, building a large social presence in an online context allows one to overcome the difficulties of an environment compared to an offline environment that deals with establishing trust [5]. Furthermore, developing relationships with individuals through an online environment where one can freely communicate anywhere and anytime can help one achieve more. Social media provides a very beneficial environment from which one can easily connect with others in the network and get various opportunities to express their thoughts and ideas online [37]. In addition, the current universal mobile environment has provided social media users with an environment where they can communicate anywhere and at any time, thus providing greater intimacy between users than community media. As a result, having a strong social presence on social media helps build the knowledge-based trust needed to improve the transactive memory capability of the individual. The increased social presence in social media allows for more effective communication and interaction between members of virtual groups [38]. In addition, communication barriers have been eliminated, and an environment has been established conducive to establishing relationships between geographically distant groups [39]. Finally, community members have a strong sense of social cohesion, which enhances knowledge-contributing activities [40]. Members' behaviours have also become more influential [37]. Members communicate clearly and honestly while maintaining a high social presence; knowledge-sharing activities are facilitated in such an environment [41]. This type of communication makes it much easier to evaluate other people's knowledge.
In this way, recognizing special knowledge and information held by others generates a transactive memory [4]. In this regard, strong relationships with people and frequent social interactions are associated with improvements in transactive memory capability. Increasing the relationship quantitatively and qualitatively increases trust in one's peers and makes it easier to determine whether the other party has specialized knowledge in a particular area [42, 43]. In addition, one can not only process specific knowledge through direct interaction with others but also get a chance to connect with others indirectly [43]. In other words, social interactions directly or indirectly increase the likelihood of encountering specific knowledge of others [4]. Existing research on transactive memory has found that interactions, including repetitive communication associated with one's job responsibilities, significantly affect the formation of transactive memory [13, 44]. In addition, repetitive interactions play an important role in creating a transactive memory system within a team, broadening opportunities for understanding and using knowledge [45]. Through new social online interactions, social media users form stronger relationships with others, actively share knowledge and information, and engage in personal, cultural and emotional exchanges based on these intimate relationships [34, 46].
Consequently, social interaction increases an individual's transactive memory capability by fostering a sense of trust and attribution. Based on these logical considerations, the following hypothesis is proposed.
H1: Their social media use is positively associated with an individual's transactive memory capability.
2.4 Transactive memory capability and job performance
Transactive memory allows people to make the most of new information and knowledge. Because people can take advantage of the latest information and knowledge with the help of their transactive memory capabilities, a person with a high transactive memory capability can receive and use information faster than others. The variables that make up transactive memory, such as expertise location, cognition-based trust and coordination ability, have been shown to positively increase job competencies at the team level in studies on transactive memory conducted from a team perspective [13]. Meanwhile, Liang et al. [28] found that transactive memory impacts team job abilities more than any other aspect. Choi et al. [10] found that installing a transactive memory system using information technology favoured knowledge-sharing and application activities. According to Akgun et al. [47], Successful knowledge-sharing activities benefitted organizations with well-established transactive memory systems. Finally, locating and using the knowledge and information needed to improve teamwork capabilities is an important aspect [48].
Transactive memory systems, created collaboratively by members of an organization, improve the information capacity of those who create the system [4, 49]. As a result, using a transactive memory system in the workplace has been associated with increased individual job performance [44]. Transitive memory can help people by enhancing their ability to learn and acquire new information [25]. The greater power of transactive memory, the more favourable the communication that occurs with the formation of transactive memory has a favourable effect on the operational capability of individuals [50]. People with strong transactive memory capabilities have a greater opportunity to acquire new and different types of knowledge as they link the flow of knowledge and information within a company due to their central position within the network [35]. Bachrach et al. [9] found that the strength of TMS (transactive memory system) for performance relationships varied depending on the characteristics of the national cultural context. The effect of TMS has stronger in cultural contexts where power distances and group collectivity are greater. According to Ali et al. [51], social media's five most important dimensions have diverse effects on the transactive memory system.
Furthermore, these dimensions of the transactive memory system improve team creative efficacy, which improves creative team performance. Wang et al. [52] found that the relationship between expertise and credibility and knowledge transfer has stronger than the link between coordination and knowledge transfer. In addition, knowledge transfer fully mediated the effect of attribute and reliability on team performance and predicted variation in team performance.
H2: Their transactive memory capability is positively associated with individuals' job performance.
2.5 Social media use on job performance
Social media is a physical resource with inherent qualities such as visibility, permanence and relevance that give it value [53]. Employees' use of social media can help organizations achieve their goals by creating a new network resource called Social Media Capital [54]. Due to social media use, employees will receive more resources (e.g., information and knowledge) to perform tasks, which encourages the exchange of information and the development of cognition-based supportive relationships [55]. Employees with access to adequate resources through social media are less stressed at work and more inclined to engage in behaviours not part of their job description. Helping and actively contributing to co-workers' responsibilities is an example of this conduct, which enhances interpersonal facilitation and workplace dedication [56]. Second, the presence of social media in terms of technical aspects, such as visibility, makes it easier for workers to see and collaborate with others, which can help them operate more efficiently [32].
On the other hand, it can help us avoid wasting time at work and doing the same thing repeatedly, increasing productivity and improving our work performance [57]. For example, employees may use and adjust available information to produce appropriate answers when relevant circumstances arise without duplicating tasks [32]. Our third hypothesis is as follows:
H3: Their social media use positively affects individuals' job performance.
The study's conceptual model (Figure 1) has been framed from literature, which has been given in the literature review, along with the literature gap from related studies. Social media has been used as an independent variable in the proposed model. At the same time, transactive memory capability has been employed as a mediator variable, and job performance has been used as a dependent variable. The theoretical background of the proposed model has been discussed in the literature review of the study. The direct effects of social media on TMC, indirect effects on job performance, and the direct effects of TMC on job performance have been examined in the model. In addition, the model has been tested for a direct effect of TMC on job performance and its mediating role between social media use and job performance.
Figure 1. Mediating research model
The following paragraphs provide important information on data gathering and hypothesis testing using the structural equation modelling (SEM) method.
4.1 Data collection and sampling for the study
Table 1. Demographic profile of respondents
|
Profile |
Profile |
Frequency |
Percentage (%) |
|
Gender |
Male |
255 |
41.9 |
|
|
Female |
353 |
58.1 |
|
|
Total |
608 |
100.0 |
|
Marital Status |
Single |
372 |
61.2 |
|
|
Married |
236 |
38.8 |
|
|
Total |
608 |
100 |
|
Age |
21-30 |
180 |
29.6 |
|
|
31-40 |
248 |
40.8 |
|
|
41-50 |
112 |
18.4 |
|
|
51 & Above |
68 |
11.2 |
|
|
Total |
608 |
100.0 |
|
Spouse Employment |
Yes |
286 |
47.0 |
|
|
No |
322 |
53.0 |
|
|
Total |
608 |
100.0 |
|
Designation |
Assistant Professor |
420 |
69.1 |
|
|
Associate Professor |
73 |
12.0 |
|
|
Professor |
115 |
18.9 |
|
|
Total |
608 |
100.0 |
|
Nature of Post |
Regular |
435 |
71.5 |
|
|
Contract |
127 |
20.9 |
|
|
Guest |
46 |
7.6 |
|
|
Total |
608 |
100.0 |
An online survey tool has been purposely created and distributed by public universities teaching faculty membersfrom the North of India. We received 608 valid responses (Table 1). 41.9% of the respondents were male, and 58.1% were female. Further, 40.8% of the respondents were members in the age group of 31-40, 29.6% were between 21-30, 18.4% were between 41 and 50, and 11.2% were in the age group of 51 and above. The largest proportion of respondents, 71.5% of the data was collected from full-time (Regular) members of public universities. Post-wise, 69.1% of the respondents were assistant professors, 18.9% were professors, and 12.0% were associate professors.
This study has adapted scales from previous studies mentioned in the literature review and measured them on a 7-point Likert-type scale ranging from "strongly disagree" to "strongly agree". Appendix 1 contains a questionnaire, a list of all constructs and their respective sources, and the number of items used for each construct. We have designed survey questions using existing scales from the literature to increase validity. The independent variable of this study, social media use, is adopted from the studies of Carlson et al. [58] and Moqbel et al. [59]. The measure of transactive memory capability has been adopted from the research of Lewis [25]. The job performance scale has been adopted from the study conducted by Huang and Liu [60]. All items have been measured on a seven-point Likert scale. The instrument items have been listed in Appendix.
Data has been analyzed using two software programs: Statistical Package for the Social Sciences (SPSS-20) and Analysis of Moment Structure (AMOS-20). Structural equation modelling has been used to analyze the data as it is very useful for examining a range of relationships between different variables [61].
5.1 Constructs validity and scales reliability
A confirmatory factor analysis (CFA) has been used to confirm the factor structure. The adequacy of the measurement model has been assessed using CFA, which is assessed using model fit, convergent validity, and discriminant validity criteria. Table 2 shows composite reliability values ranging from 0.914–0.977 and Cronbach's alpha values ranging from 0.908–0.977, all of which are greater than the acceptable range of 0.60. Cronbach's alpha has been determined to be greater than the minimum standard (0.6) for all measurement items, implying that all measurement items have been valid [62]. Cronbach's alpha measures the internal consistency of the items representing each factor. The recommendation level can be considered when the composite reliability exceeds 0.60 and the average variance extracted exceeds 0.50 [62, 63]. Average variance extracted (AVE) values ranging from 0.641–0.894, the recommendation level is considered. According to Hair et al. [64], a factor loading of 0.45 and above is appropriate for interpreting the rotated factor pattern. When the factor loading is greater than 0.45, convergent validity has been generally achieved. The number of factors has been determined using more than one Eigenvalue as a criterion [64].
All values listed above exceed what is considered acceptable. Finally, the square root of the AVE values of all the constructs has been calculated to evaluate the discriminant validity. Table 3 shows all square root values greater than the correlation between the constructs, indicating that the measurement model is discriminant valid. As a result, the convergent validity, discriminant validity, and reliability of the measuring model are acceptable.
The convergent validity of the measurement model has been assessed using factor loadings, composite reliability, average variance extracted and Cronbach's alpha. The findings of the convergent validity assessment are shown in Table 2. Exploratory factor analysis has revealed that factors such as social media use, transactive memory capability, and job performance have been found and employed as latent variables in the investigation.
Table 2. Convergent validity and reliability test
|
|
Items |
Factor Loadings |
Composite Reliabilities |
Average Variance Extracted |
Cronbach's Alpha |
|
Task-Oriented Social Media |
TOSM01 |
0.873 |
0.958 |
0.852 |
0.958 |
|
|
TOSM02 |
0.898 |
|
|
|
|
|
TOSM03 |
0.872 |
|
|
|
|
|
TOSM04 |
0.850 |
|
|
|
|
Relationship Building Social Media |
RBSM01 |
0.856 |
0.948 |
0.820 |
0.947 |
|
|
RBSM02 |
0.894 |
|
|
|
|
|
RBSM03 |
0.879 |
|
|
|
|
|
RBSM04 |
0.869 |
|
|
|
|
Deviant Social Media |
DSM01 |
0.910 |
0.977 |
0.894 |
0.977 |
|
|
DSM02 |
0.942 |
|
|
|
|
|
DSM03 |
0.961 |
|
|
|
|
|
DSM04 |
0.967 |
|
|
|
|
|
DSM05 |
0.964 |
|
|
|
|
Preoccupied Social Media |
PSM01 |
0.944 |
0.951 |
0.867 |
0.951 |
|
|
PSM02 |
0.957 |
|
|
|
|
|
PSM03 |
0.938 |
|
|
|
|
Intensity Use of Social Media |
IUSM01 |
0.861 |
0.935 |
0.743 |
0.939 |
|
|
IUSM02 |
0.841 |
|
|
|
|
|
IUSM03 |
0.908 |
|
|
|
|
|
IUSM04 |
0.898 |
|
|
|
|
|
IUSM05 |
0.879 |
|
|
|
|
Transactive Memory Capability |
TMC01 |
0.857 |
0.914 |
0.641 |
0.908 |
|
|
TMC02 |
0.900 |
|
|
|
|
|
TMC03 |
0.867 |
|
|
|
|
|
TMC04 |
0.736 |
|
|
|
|
|
TMC05 |
0.876 |
|
|
|
|
|
TMC06 |
0.745 |
|
|
|
|
Job Performance |
JB01 |
0.843 |
0.946 |
0.747 |
0.951 |
|
|
JB02 |
0.841 |
|
|
|
|
|
JB03 |
0.908 |
|
|
|
|
|
JB04 |
0.943 |
|
|
|
|
|
JB05 |
0.918 |
|
|
|
|
|
JB06 |
0.929 |
|
|
|
Table 3. Discriminant validity
|
Mean |
SD |
PSM |
TMS |
JP |
TOSM |
RBSM |
DSM |
IUSM |
|
|
PSM |
4.039 |
1.038 |
0.931 |
||||||
|
TMC |
4.183 |
1.184 |
-0.01 |
0.801 |
|||||
|
JP |
4.584 |
1.052 |
0.201 |
0.482 |
0.864 |
||||
|
TOSM |
4.577 |
1.057 |
0.137 |
0.243 |
0.414 |
0.923 |
|||
|
RBSM |
4.764 |
1.102 |
0.153 |
0.239 |
0.430 |
0.652 |
0.906 |
||
|
DSM |
3.329 |
1.196 |
0.132 |
0.167 |
0.108 |
0.140 |
0.011 |
0.946 |
|
|
IUSM |
3.215 |
1.156 |
0.169 |
0.309 |
0.240 |
0.348 |
0.295 |
0.201 |
0.862 |
Table 4. Mediation effect test result
|
|
IV |
M |
DV |
Effect of S.M. on TMC |
Effect TMC on J.P. |
Direct Effect (c) |
Indirect Effect (a*b) |
Total Effects (c) |
Confidence Interval |
P Value |
Result |
|
|
Lower Bound |
Upper Bound |
|||||||||||
|
H1 |
SM |
SC |
JB |
0.326 |
0.344 |
0.423** |
0.112** |
0.536 |
.075 |
.0162 |
0.001 |
Partial |
* P＜0.1; **P＜0.05; ***P＜0.01
After that, the discriminative validity was investigated. It has been confirmed by comparing the square foot of the average variance extracted from each variable and the correlation coefficient between the relevant variable and the other variables. Table 3 shows that the square foot of the average variance extracted from each variable was greater than the corresponding correlation coefficient; thus, the discriminative validity of the model has been recognized.
The square root of variance has been shared by the average variance extracted and is represented by the diagonal elements (in bold). Off-diagonal elements have represented the correlations between the constructs.
5.2 Evaluation of the structural model
Figure 2 shows the standardized path coefficients, path significance, and explained variance for each dependent variable as a result of the structural model test. Research models have supported the results except for hypotheses H1, H2, and H3. Social media use at the workplace significantly affects TMC (b = 0.326, p < 0.001, t-value = 6.690). Thus H1 has been supported respectively [34, 46, 65]. Transactive memory capability (b = 0.344, p < 0.001, t-value = 8.409) has been positively influencing the job performance of teaching faculties, thus supporting H2 [13, 45, 47, 66]. Social media use at the workplace also has a significant impact on job performance (B = 0.423, p < 0.001, t-value = 8.882). This model supports H3 [67-69]. There has been a significant association with social media use, TMC and job performance, so it accepts H1, H2 and H3. We have demonstrated in this study that using social media at the workplace improved job performance by enhancing the transactive memory capabilities of teaching faculties. In short, all research hypotheses are acceptable. The mediation analysis (Table 4.) showed that TMC partially mediates the effect of social media use on job performance. We have used this to examine the mediating role of TMC on social media use and job performance. The results indicated that TMC partially mediates the link between social media use and job performance.
Figure 2. Results of structural equation model
5.3 Model fit
After analyzing the measurement model, we have put the structural model to the test, and the SEM results are shown in Figure 2. CFI, IFI, TLI, NFI and RMSEA are all within a reasonable range, indicating that the model has a good overall fit. Consequently, the measurement model has been accepted as the 'final' measurement model for the study [70-74]. According to the results of measurement model testing, the goodness-of-fit statistics and coefficients have been χ2 = 2323.921, df = 481, χ2/df = 4.831, p-value = 0.000, NFI = 0.904, CFI = 0.922, TLI = 0.915, IFI = 0.922, RMSEA = 0.079 which have been very good results of the structural model.
5.4 Mediation analysis
The bias-corrected percentile method link has led to the following results. This is shown first as the lower bound indirect effect, then as the upper bound indirect effect. The lower bound confidence interval for our indirect job performance test through social media use and TMC has been 0.075, while the upper bound has been 0.162. Confidence Interval has been demonstrated to have a significant indirect effect between L.B. and U.B., as there is no zero between them. Two-tailed significance has been shown by a value of 0.001 at the intersection of S.M. (IV) and J.P. (DV), which is less than 0.05 in this case. Therefore, we have concluded that TMC has mediated the relationship between social media and job performance.
From these findings, we concluded that social media use significantly indirectly affected job performance through TMC. The use of social media has been an indirect effect on job performance of 0.112 a*b (0.326*0.344). The same result has been examined for the confidence intervals generated by our bootstrap. The lower bound of the confidence interval has been 0.075, while the upper bound has been 0.162. We know indirect influence has been significant, but how has TMC impacted this? To learn more about this, we have investigated the "C" path in the "Estimates" link in the output or the direct link between social media use and job performance. We can see that there has been a significant association between social media use and job performance (β = 0.423, p < 0.005). It has been indicated that the effect of social media use on job performance has been partially mediated through the construct of TMC.
This study has examined the significance of TMC in mediating the association between social media use and job performance. The results showed that there had been a positive and significant indirect effect of social media use on job performance (β = 0.112, P = 0.001). Furthermore, in the presence of the mediator, social media's direct effect on job performance is significant (β = 0.423, P = 0.001). Therefore, TMC has been partially mediated in the relationship between social media use and job performance. The mediation analysis summary is presented in Table 4.
This study examines the effect of social media use on transactive memory capability and the effect of transactive memory capability on an individual's job performance. The measurement model findings indicate that the research model has been of sufficient reliability and validity across all constructs. Furthermore, the structural model results show that all path coefficients have been significant.
First, the study results support H1, which has been predicted to have a positive association between social media and transactive memory capability. This suggests that social media provides many facilities, such as communication and coordination systems, knowledge stores, and platforms for finding and accessing information and building knowledge on the social media platform. Support for social media use can enhance TMC effectiveness among individuals within an organization. The path coefficient in this study from social media use to TMC at the workplace suggests that social media use affects TMC, thereby improving individual job performance. If organizations use social media tools such as blogs and wikis to discuss, create and exchange knowledge and information while expanding the knowledge directory, social media will have a greater impact on TMC [34, 46, 65]. Study results support H2, which has been shown to have a positive association between TMC and job performance. TMC is an important aspect that positively impacts an individual's job performance. In previous studies, TMC has been shown to affect job performance [5, 25, 35, 48, 50]. TMC is played an important role in improving the job performance of teaching faculties. Study results support H3, which has been shown to have a positive association between social media and job performance. Social media helps us avoid wasting time at the workplace and doing the same thing repeatedly, increasing productivity and job performance [33, 55, 57]. Finally, the study examines the relationship between social media and job performance through mediated transactive memory capability. Based on previous research, we can conclude that TMC mediators enhance individual job performance through social media.
This study specifically examined social media on the performance of teaching faculty in public universities in India. The findings of the study have important theoretical and managerial implications. First, using the mediating role of transactive memory capability, this study fills an existing gap related to the effect of social media in enhancing the job performance of teaching faculties, especially faculty members of public universities in India. Second, this research adds to the theoretical understanding of the dynamics between social media use, transactive memory capability, and job performance in internal relationships. Based on the use and satisfaction theory, the present study adds to the empirical literature in this area by demonstrating the positive effects of social media use by teaching faculty on TMC development and job performance. We also offered further research showing how social media use enhances TMC and the job performance of teaching faculties.
Furthermore, this study has several practical implications. Social media is a legitimate communication tool to increase workplace connectivity. Second, faculties must understand how social media generates transactive memory capability so that they can use it more effectively. In the current study, the benefits of social media in universities include organizing meetings, scheduling appointments, sending documents and communicating about work activities with colleagues. As a result, Administration must educate faculties about using social media to maximize their opportunities. Third, another important contribution of this study is including transactive memory capability as a mediator in the model. Our research found that faculties have transactive memory capability, which affects university job performance through social media use. In conclusion, this study adds to previous knowledge of transactive memory capability for teaching faculties interacting with colleagues in universities via social media.
As the popularity of social media grows, an increasing number of organizations are adopting it in their operations to aid communication and collaboration. However, there is rarely empirical research on social media use at work. We developed a model to study the impact of social media in public universities and the underlying mechanisms for how they generate value at work by combining media synchrony and transactive memory capability. As we have shown, social media helps build the transactive memory capability of the teaching faculties, significantly impacting job performance. We quantify the benefits of social media for public universities, inspiring teaching faculty to use it in the workplace with high hopes. Public universities can improve the job performance of teaching faculties by following the guidelines presented in this study.
Although this study has many implications for future research, it has limitations. Our research has resulted in many unresolved questions, which can be studied in future studies. First, we focused on subjective indicators that may or may not accurately reflect actual reality. Second, the data used in this study are cross-sectional. TMC cannot be developed in an instant but rather develops over time as a result of individual interaction and collaboration [13].
Consequently, future studies should examine the effect of social media use on TMC using longitudinal data. Future research should also examine how social media can help develop TMC over time. Finally, because this research is conducted in India, it is influenced by Indian culture. Consequently, the generalizability of the study can be called into doubt. Future research should apply the findings to cultural contexts in other countries to remove this doubt. Consequently, a cross-sectional, longitudinal and in-depth study on measuring individual transactive memory capability is needed to consider these limitations.
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