1. Introduction
The extensive spread of internet technology is fundamentally changing how academics interact, share information, and collaborate with colleagues and stakeholders [1]. Social media (SM) is a driving force in education, providing new avenues for academics to share their work and connect with a vast network of scholars and experts [2]. Similarly, new technologies empower organizations with capabilities they previously lacked. Social media, along with other technical advancements, has demonstrably improved organizations by enabling a wide range of applications and functionalities, from enhanced demand forecasting to the development of innovative marketing strategies and business models [3,4]. Unsurprisingly, SM has been described as a revolutionary force, transforming the ways people connect, communicate, consume information, and create valuable content [5].
Social media refers to a collection of online resources that facilitate the creation and exchange of user-generated content, building upon the foundations of Web 2.0, a concept that emphasizes user-generated content and collaboration [6]. Currently, social media empowers individuals to expand their social networks and maintain ongoing communication. Internal communication, on the other hand, refers to the communication channels organizations utilize to manage relationships with and among employees [7]. Tools like newsletters, intranets, and bulletin boards can streamline these interactions, fostering a more engaged workforce. Businesses leverage a variety of internal communication channels to enhance teamwork, communication between staff and stakeholders, and ultimately, job performance [8]. Social media’s unprecedented power lies in its ability to create teams and improve teamwork across various departments within a company [9]. Social media also facilitates communication, team collaboration, and project management processes [10,11].
Similarly, social media use has permeated academia [12]. Because social media is becoming more and more ingrained in educators’ personal and professional lives, research on how it affects work performance in the teaching profession is crucial [13,14,15]. These online social platforms serve a multitude of functions for academics, encompassing core academic tasks like research and teaching, as well as professional development, profile building, information sharing, and social and professional networking [16,17].
The aim of the study is to investigate the impact of social media use on faculty job performance, exploring the mediating roles of teamwork and internal communication. It offers several key contributions that differentiate it from previous research in this area. First, this study breaks new ground by identifying internal communication and teamwork as the mediating processes through which social media use influences faculty performance. While prior research has established a connection between social media and job performance [11,18], it often fails to explain the “how” behind this relationship. This study fills this gap by proposing a novel framework that reveals the crucial role of internal communication and teamwork in facilitating the positive impact of social media on faculty job performance.
Second, this study delves deeper by exploring the contingent effects of different social media usage dimensions (social, hedonic, cognitive) on job performance. Current research often treats social media use as a monolithic concept, limiting our understanding of its nuanced effects [10,19]. This study breaks away from this approach by examining how the type of social media use (focusing on social interaction, entertainment, or information gathering) influences the strength of the mediating effects (internal communication and teamwork). This study concentrated on employees who use Facebook and LinkedIn since these platforms offer three crucial components of usage: social, hedonic, and cognitive. Facebook is often used for social and hedonistic purposes, providing users with entertainment and personal connections, while LinkedIn is mostly utilized for cognitive and professional aims, such as networking, learning, and career development.
This reveals a more intricate picture, where the effectiveness of social media in enhancing faculty performance depends on the specific way it is used.
Third, this study addresses a critical gap in the literature by focusing on faculty performance, particularly examining its two key dimensions: innovative and routine tasks. While prior research explores social media’s impact on general work performance (e.g., [20]), faculty members face unique demands. This study delves deeper by investigating how social media use influences both innovative tasks like research collaboration and knowledge dissemination, as well as routine tasks like student engagement. By examining these distinct performance dimensions, the study provides valuable insights for tailoring strategies and policies. Given the increasing prevalence of social media usage in both personal and professional contexts, it is critical to comprehend the effects these platforms have on teachers’ efficiency, ability to manage their time, and job performance.
Fourth, by identifying internal communication and teamwork as mediating processes, this study offers valuable insights for faculty development and institutional policies. It informs strategies for promoting effective social media use within academic settings, fostering collaboration, and communication, and ultimately, enhancing faculty performance.
Additionally, this study specifically focuses on teachers, a unique workgroup whose job performance and effectiveness are distinct from other professions. The specificity of teachers’ work is highlighted in this research, addressing the unique challenges and dynamics they face. This focus is crucial as it ensures that the findings are relevant and accurately reflect the academic context, which may not be directly applicable to other professions.
The paper is structured as follows. After the introduction, we present a research background that outlines the research model. This model depicts the relationships between social media use, job performance, internal communication, and teamwork, drawing upon established theoretical concepts. In light of these theories, we discuss the specific hypotheses of the study. Next, we conduct data analysis to test the hypotheses. Finally, the paper concludes by discussing the results and their corresponding theoretical and practical implications.
2. Theoretical Background and Research Hypothesis
2.1. Theoretical Background
This research investigates the potential for social media to indirectly influence faculty job performance through the mediating factors of internal communication and teamwork. The model outlined in Figure 1 depicts this mediated relationship. This aligns with the concept of a mediated model in social science research, where an independent variable (social media use) can have an indirect effect on a dependent variable (job performance) through the influence of mediating variables (internal communication and teamwork) [21]. We develop hypotheses based on existing research to understand the specific relationships between these factors.
Use of Social Media and Job Performance
This research investigates the motivations behind faculty members’ engagement with social media and its potential influence on job performance. Social media use (SMU) serves as the independent variable, representing the faculty members’ engagement with social media platforms. Job performance, on the other hand, is the dependent variable, reflecting the effectiveness of faculty members in their roles. The Uses and Gratifications (U&G) theory [22] provides a valuable framework for understanding the motivations behind SMU. U&G theory posits that individuals actively choose media to fulfill specific needs, categorized as social, hedonic, and cognitive [11,23]. Social media’s unique characteristics make it a platform that can cater to all three need categories. Here is why we focus on these three dimensions:
Social Use: Faculty members can leverage social media to maintain and strengthen relationships with colleagues [11].
Hedonic Use: Social media can cater to hedonic needs by providing opportunities for entertainment and relaxation [11,23].
Cognitive Use: Social media platforms are valuable tools for knowledge acquisition and information sharing [24,25].
Job performance refers to the effectiveness with which faculty members fulfill their job duties and responsibilities. This research focuses on two key dimensions of JP.
Innovative Job Performance. This dimension encompasses behaviors that go beyond the official job description and involve creativity [26]. It includes generating and implementing original ideas, tackling challenges with innovative solutions, and forming collaborations to bring new ideas to fruition [27,28,29].
Routine Job Performance. This refers to the consistent and dependable completion of essential tasks, obligations, and responsibilities [26,30]. Examples include meeting deadlines, delivering lectures effectively, and grading assignments accurately.
2.2. Direct Effect of Social Media Use on Job Performance (H1–H3)
The impact of social media use (SMU) on work performance has been a topic of growing interest [31,32]. This research delves deeper by examining the specific effects of SMU on two key dimensions of job performance: routine and innovative. Technology is a major driver of rapid change in the business world [33]. Social media has become a prominent tool for organizations, with some studies suggesting it can positively influence job performance [34]. Shang et al. [35] highlight the use of social media platforms by administrations for various functions and development purposes. Ali-Hassan et al. [11] found that social media use enhances workers’ abilities to generate, share, and acquire information, potentially leading to increased productivity. However, Liu et al. [36] and Zahmat Doost and Zhang [37] suggest that while social media use in the office can improve communication quality, it can also lead to job interruptions. Therefore, effectively utilizing social media is crucial for organizations to improve job performance.
Social media platforms offer faculty members a unique set of tools for communication, collaboration, and knowledge acquisition, which can be particularly beneficial for fostering innovative job performance (IJP). Studies suggest that the social use of social media (SU) can facilitate the exchange of ideas and expertise among colleagues [11]. For example, faculty can leverage social media to connect with researchers in their field, participate in online discussions on emerging topics, and share their own findings. This exposure to diverse perspectives and the exchange of knowledge can stimulate creative thinking and problem-solving, potentially leading to the generation of innovative ideas and solutions. Additionally, SU can be used to build and maintain relationships with colleagues [11], fostering a sense of community that can lead to increased collaboration on innovative projects. Therefore, we propose the following hypothesis:
Social use of social media (SU) is positively associated with innovative job performance (IJP).
Studies suggest that social media can foster closer relationships and enhance communication within educational settings [15]. This aligns with the concept of SU, which encompasses activities like building connections, sharing information, and participating in online discussions with colleagues. Faculty can leverage these capabilities to facilitate communication and collaboration, potentially leading to streamlined completion of routine tasks. For example, social media platforms can be used to share course materials, updates on curriculum or departmental policies, and best practices for teaching. This can save time faculty would otherwise spend searching for information or reinventing the wheel. Additionally, faculty can utilize social media to collaborate on projects, troubleshoot challenges, and share resources, potentially leading to increased efficiency in completing routine tasks like lesson planning and grading.
Research highlights the potential of social media as a tool for communication and collaboration within organizations [9]. This directly connects to the potential benefits of SU for faculty members. By efficiently sharing resources and information with colleagues through social media platforms, faculty can save time searching for materials. For instance, a faculty member might use social media to inquire about specific software or request recommendations for textbooks, receiving quick and relevant responses from colleagues. This saved time can then be directed towards completing other routine job tasks more efficiently.
Studies also suggest that social media can contribute to a sense of community within a department or institution [18]. This sense of community fostered by SU can lead to increased morale, collaboration, and a more supportive work environment. When faculty feel connected and supported by colleagues through online interaction, it can improve motivation and potentially lead to increased efficiency in completing routine job duties. For example, social media can facilitate knowledge exchange and problem-solving among faculty members. A faculty member encountering a challenge can leverage social media to seek advice from colleagues with expertise in the specific area. This type of online support can lead to quicker resolution of issues and improved efficiency in completing routine tasks. Therefore, we hypothesize:
Social use of social media (SU) is positively associated with routine job performance (RJP).
The relationship between the hedonic use of social media (HU) and innovative job performance (IJP) appears complex and multifaceted. Some research suggests the potential benefits of social media platforms. They can help reduce stress and improve communication among colleagues [36,37]. This improved communication could foster collaboration and idea-sharing, potentially leading to more innovative outcomes. Additionally, social media can connect individuals with diverse perspectives and information sources, which can be valuable for creative problem-solving [11].
However, other studies highlight the potential drawbacks of HU. Social media’s constant notifications and rapid updates can fragment attention and hinder the deep concentration needed for innovative thinking [38]. This fragmented attention could hinder the deep concentration needed for creative problem-solving and innovative thinking, potentially reducing IJP. Social media can significantly reduce the time dedicated to “deep work”—focused, uninterrupted effort required for innovation [39]. Mark et al. [40] found that even brief social media interruptions could significantly hinder task resumption and performance. This suggests HU could limit the time faculty have for deep work, potentially affecting their ability to generate innovative ideas. Taking into account the mixed nature of the existing literature, we hypothesize:
There is a relationship between hedonic use of social media (HU) and innovative job performance (IJP).
The relationship between hedonic social media use (HU) and routine job performance (RJP) appears multifaceted too. Research suggests that using technology for purely leisure purposes can lead to inefficiencies and negatively affect job performance [41]. This aligns with the concerns expressed by faculty members interviewed by Sobaih et al. [15] who viewed social media use for leisure during work hours as a waste of time that could be better spent on core job tasks. Studies suggest that engaging in hedonistic social media activities during work hours can be distracting, leading to decreased focus and ultimately impacting productivity on routine tasks [42]. The constant notifications and stimulation from social media can make it harder for employees to stay concentrated on routine tasks, leading to more task switching and potentially hindering performance. Studies have shown a negative correlation between excessive social media use and job satisfaction [43]. Dissatisfaction with work might lead to increased reliance on social media for enjoyment, creating a cycle that further hinders performance [43].
However, there are other studies pointing to potential benefits. Social media use can foster connections, improve communication, and even reduce stress, which could lead to positive effects on performance [6,44]. This mixed picture is further complicated by the potential influence of individual differences, work context, and specific types of social media use on the relationship between HU and RJP 08 [40]. Therefore, we hypothesize:
There is a relationship between hedonic use of social media (HU) and routine job performance (RJP).
The concept of cognitive social media use (CU) emphasizes utilizing social media platforms to create, share, and access information [45]. In the educational field, teachers leverage this cognitive dimension by generating and disseminating educational content [46]. This aligns with the findings of [47] who discovered a positive impact on innovative teaching practices when lecturers used social media for sharing and posting content. This makes sense, as sharing educational resources and collaborating online with colleagues through CU can foster innovative approaches to teaching. Further supporting this notion, [48] explore the potential of social networking sites (SNS) in academia. Their research highlights how researchers can utilize SNS for various purposes, including sharing research findings and collaborating with colleagues. These activities directly correspond with the core aspects of CU—content creation and knowledge sharing. In essence, these studies demonstrate the positive connection between CU and innovative practices within academic settings.
Cognitive use of social media (CU) is positively associated with innovative job performance (IJP).
Studies like the one by Ali-Hassan et al. [11] highlight the positive and indirect effect of social and cognitive technology use, including social media, on job performance. This effect likely stems from social capital—the benefits gained from social connections. Social media use, as highlighted in the [49] study, can help build social capital by creating information, maintaining social networks, and fostering trust among colleagues. Features of social media itself can contribute to better RJP. Jong et al. [42] suggest that aspects like easy access and information-sharing tools significantly influence work efficiency. This efficiency can translate to improved completion of routine tasks. The ability to share knowledge through social media is particularly valuable for routine tasks. Studies by [48,50] on faculty members demonstrate this. Sharing information, assigning tasks, and collaborating on projects through social media can streamline routine job processes for various professions, therefore:
Cognitive use of social media (CU) is positively associated with routine job performance (RJP).
2.3. Relationship between Social Media Use and Internal Communication (H4)
Several scholars have explored the concept of internal communication. Ali and Anwar [51] and Anwar and Abdullah [52] define it as the sharing of ideas, data, and knowledge among multiple individuals with the goal of reaching a consensus. Within organizations, effective communication is considered a crucial management practice [53]. It is seen as a key way for employees at all levels to learn about their roles and responsibilities [54]. Abdullah and Rahman [53] further describe internal communication as the exchange of meanings and interactions that occur within an organization.
The rise of social media offers a transformative approach to internal communication (IC) within organizations. Traditionally, internal communication relied on top-down methods or limited channels for employee interaction. However, social media platforms hold the potential to revolutionize how employees connect and engage with one another [55]. Employee communication fosters the social and emotional benefits crucial for organizational success [56]. Social media platforms can facilitate a more informal and interactive communication style, fostering a sense of community and belonging. This can lead to improved employee morale, engagement, and overall well-being. Social media can promote both horizontal communication (between colleagues) and vertical communication (between leadership and employees) [57]. Features like groups, discussions, and employee recognition programs can foster stronger relationships between coworkers, leading to better collaboration and knowledge sharing.
Social media platforms can encourage a more transparent and open communication environment. Employees can readily access information, participate in discussions, and provide feedback through social media channels. This two-way communication can foster trust and a sense of shared purpose within the organization. Therefore, we hypothesize:
Social use of social media (SU) is positively associated with internal communication (IC).
The influence of hedonistic social media use (focusing on enjoyment and entertainment) on organizational communication has been a topic of debate, with research suggesting both positive and negative effects [44]. While concerns exist about reduced productivity due to distractions, social media platforms can also be valuable tools for enhancing internal communication. They can facilitate information sharing, announcements, and team collaboration, fostering a sense of community and employee engagement [58,59,60].
It is important to acknowledge that excessive use for personal enjoyment might lead to information overload and hinder communication effectiveness [61,62]. However, research by Sun and Chao [63] suggest that hedonic social media use can be a positive force for internal communication when used strategically and balanced with work-related communication practices. Consequently, we recommend the following hypothesis:
There is a relationship between hedonic use of social media (HU) and internal communication (IC).
Employers can leverage social media by promoting scouting behavior, which refers to the voluntary search, exchange, and transmission of information related to organizational interests [44]. Studies suggest that businesses use social media platforms to meet the individual communication needs of employees, ensure a smooth flow of information, and provide ongoing feedback on both personal and organizational matters [64]. However, social media can also affect the information shared, privacy settings, and communication styles among team members, potentially favoring certain groups over others [65]. Similarly, social networking platforms are used by some instructors and students in higher education for both personal and professional purposes [66]. Cognitive use of social media can enhance internal communication when aligned with organizational goals and established communication practices, therefore, we hypothesize:
Cognitive use of social media (CU) is positively associated with internal communication (IC).
2.4. Relationship between Social Media Use and Teamwork (H5)
The success of many endeavors hinges on the effectiveness of teamwork. Teams, composed of individuals with complementary skillsets, synergistically work towards achieving shared goals [67]. Collaboration often leads to superior outcomes compared to individual efforts [67]. Furthermore, effective teamwork fosters a robust and flourishing work environment [68]. The rise of social media (SM) presents a novel avenue for exploring communication and collaboration within teams.
Research suggests that social media can be a valuable tool for enhancing teamwork. Song et al. [69] highlight its role as a complementary resource that creates synergies to improve both individual and team performance. The increasing use of social media by businesses and individuals, for both professional and personal purposes [32], offers opportunities for improved collaboration. In particular, studies suggest that the combined use of social media for work and social interaction can be beneficial. Song et al. [69] found that social media-facilitated social interaction becomes a regular part of some employees’ jobs and can even enhance and streamline work processes. Similarly, Lailiyah and Putra’s [70] research on higher education students found positive perceptions of teamwork through social media use. Based on the potential benefits of social media for fostering interaction and collaboration, we propose the following hypothesis:
Social use of social media (SU) is positively associated with teamwork (TM).
The integration of leisure and work facilitated by social media features has been linked to changes in teamwork patterns [71]. For example, Bodhi et al. [72] found that restricting personal social media use in the office could lead to unintended consequences, such as reduced employee contributions. This suggests that some level of social media integration might be beneficial for teamwork. However, the impact of hedonistic use (focusing on entertainment and leisure) on teamwork appears more complex. While Dodokh [73] suggests that such use solely has negative impacts, like increased job burnout and turnover intention, other research points to potential benefits. For instance, social media can facilitate informal interactions and build rapport among team members, which could indirectly contribute to better teamwork [74]. Given this nuanced relationship, we propose the following hypothesis:
There is a relation between hedonic use of social media (HU) and teamwork (TM).
Organizations increasingly leverage social media due to its potential for knowledge creation and dissemination within and across teams [75]. This user-generated content can be a valuable resource for teams. Research suggests that knowledge sharing in teams is more effective when it is visible, persistent, and easily editable, characteristics facilitated by social media platforms [76]. Furthermore, Kadar et al. [77] found that participation in online groups through social media helps employees express their ideas and gain a deeper understanding of their work. This collaborative knowledge-building can be a significant asset for teamwork. Based on the potential for enhanced knowledge sharing and collaboration, we propose the following hypothesis:
Cognitive use of social media (CU) is positively associated with teamwork (TM).
2.5. Mediating Effects (H6–H7)
2.5.1. Mediating Effect of Internal Communication
Internal communication (IC) is a well-established factor influencing employee performance [78,79]. Experts like Meng and Berger [80] highlight its key functions: informing employees about organizational goals, fostering a sense of purpose, facilitating collaboration, and ultimately enhancing performance for all stakeholders. Research by Jacobs et al. [81] further emphasizes the critical role of IC in maximizing worker performance, optimizing network efficiency, and communicating strategic options, particularly during crises.
Qin and Men [82] highlight that effective internal communication (IC) fosters a range of positive outcomes within an organization. It strengthens collaboration, facilitates the transfer of processes and values, and ultimately empowers employees to achieve exceptional performance [83]. This link between IC and performance suggests that IC can be considered a core internal capability that contributes to organizational success [84].
The question this study explores is whether IC acts as a mediator in the relationship between social media use (SMU) and job performance. A mediator is a variable that explains how one variable (SMU in this case) influences another (job performance). In this context, we propose that social media can facilitate communication and collaboration, potentially improving IC, which in turn, could lead to enhanced job performance. Based on the potential mediating role of internal communication, we propose the following hypotheses:
Internal communication (IC) mediates the relationship between social use of social media (SU) and innovative job performance (IJP).
Internal communication (IC) mediates the relationship between social use of social media (SU) and routine job performance (RJP).
Internal communication (IC) mediates the relationship between hedonic use of social media (HU) and innovative job performance (IJP).
Internal communication (IC) mediates the relationship between hedonic use of social media (HU) and routine job performance (RJP).
Internal communication (IC) mediates the relationship between cognitive use of social media (CU) and innovative job performance (IJP).
Internal communication (IC) mediates the relationship between cognitive use of social media (CU) and routine job performance (RJP).
2.5.2. Mediating Effect of Teamwork
Effective teamwork is essential for workplace success. Collaboration within teams fosters motivation, generates creative ideas, and leverages the strengths of diverse individuals to achieve common goals [85]. Teamwork success can be measured by achieving predetermined objectives [86]. Research by McEwan et al. [87] further highlights a positive correlation between teamwork and individual employee performance. This study examines whether teamwork acts as a mediator in the relationship between social media use (SMU) and job performance. Social media can potentially facilitate communication and collaboration within teams. If social media use enhances teamwork, this improved teamwork could then lead to better job performance. Based on the potential mediating role of teamwork, we propose the following hypotheses:
Teamwork (TM) mediates the relationship between social use of social media (SU) and innovative job performance (IJP).
Teamwork (TM) mediates the relationship between social use of social media (SU) and routine job performance (RJP).
Teamwork (TM) mediates the relationship between hedonic use of social media (HU) and innovative job performance (IJP).
Teamwork (TM) mediates the relationship between hedonic use of social media (HU) and routine job performance (RJP).
Teamwork (TM) mediates the relationship between cognitive use of social media (CU) and innovative job performance (IJP).
Teamwork (TM) mediates the relationship between cognitive use of social media (CU) and routine job performance (RJP).
2.6. Mediators and Job Performance (H8–H10)
2.6.1. Internal Communication and Job Performance
Internal communication is a cornerstone of successful job performance, influencing how employees understand expectations, collaborate with colleagues, and ultimately contribute to organizational goals [78]. Innovative job performance, on the other hand, reflects an employee’s ability to develop and implement new ideas for products, services, processes, or business models, ultimately leading to a competitive advantage for the organization [88]. Research suggests that internal communication can enhance a company’s innovative capabilities by fostering collaboration and knowledge sharing among employees [89]. Effective communication allows employees to share insights, learn from each other’s expertise, and build upon existing knowledge, which can spark innovative ideas.
However, the impact of social media on this relationship is not entirely clear. While some studies (e.g., Soares et al. [90]) suggest that social media may not directly influence innovative behavior, others (e.g., Amalina and Pusparini, [91]) highlight a positive link between social media use and internal communication. Taking these mixed findings into account, we propose the following hypothesis:
Internal communication (IC) is positively associated with innovative job performance (IJP).
Effective internal communication fosters a supportive work environment and empowers employees to perform their regular tasks efficiently, transparency and open communication channels can lead to higher employee satisfaction and better performance [82,92]. Research also suggests that knowledge-sharing practices and clear communication contribute positively to routine job performance [93,94]. Based on the link between effective communication and routine job performance, we propose the following hypothesis:
Internal communication (IC) is positively associated with routine job performance (RJP).
2.6.2. Teamwork and Job Performance
Diversity in knowledge and skills within a team is a well-established predictor of creativity [95]. However, simply having a diverse team is not enough. To harness the full potential of this diversity, teams need effective processes and collaboration skills. This is where teamwork comes in. Strong teamwork fosters a positive team climate, characterized by trust, psychological safety, and a shared focus on innovation [96,97]. In such an environment, team members feel comfortable sharing ideas, taking risks, and building upon each other’s contributions, which ultimately leads to greater innovation [98,99]. Building on the link between teamwork and a positive climate for innovation, we propose the following hypothesis:
Teamwork (TW) is positively associated with innovative job performance (IJP).
Teamwork plays a significant role in enhancing routine job performance. Studies suggest that collaboration fosters information sharing and knowledge transfer among team members, allowing them to learn from each other and improve their efficiency on routine tasks [98,100]. Furthermore, strong teamwork involves well-established processes like communication, coordination, and cooperation [101]. These processes ensure tasks are clearly defined, responsibilities are shared effectively, and team members can support each other when needed, ultimately leading to smoother completion of routine tasks. Building on the evidence of how teamwork facilitates knowledge sharing, communication, and coordination, we propose the following hypothesis:
Teamwork (TW) is positively associated with routine job performance (RJP).
2.6.3. Internal Communication and Teamwork
Internal communication and teamwork are intertwined concepts that contribute significantly to organizational success. Clear communication of goals and objectives is crucial for ensuring team members are aligned with each other and the broader organizational vision [102]. When employees understand the organization’s goals, it fosters a sense of common purpose and motivates collaboration within teams [103]. Effective internal communication, characterized by open and transparent channels, facilitates the flow of information among team members [104]. This timely and relevant information allows teams to coordinate efforts, solve problems collaboratively, and ultimately work together more effectively. Research by Sari, Indrajaya, and Nurminingsih [105] further supports this notion, highlighting the positive influence of internal communication on teamwork. Based on the connection between clear communication and effective collaboration, we propose the following hypothesis:
Internal communication (IC) is positively associated with teamwork (TW).
Considering the literature review above, Figure 2 illustrates the conceptual model, which shows the connections between the constructs and the proposed relationships.
3. Research Methodology
3.1. Data Sampling and Collection
We collected data through a Google Form-based online survey distributed to teaching faculties of selected universities in northern India, using convenience and snowball sampling methods. We aimed to collect data from at least 450 participants, considering a minimum sample size of 384 determined through a power analysis. The power analysis, conducted using G*Power, aimed to detect a medium effect size of 0.5 (Cohen’s d) with a desired power of 0.8 and a significance level of 0.05. A total of 550 surveys were distributed, resulting in a high response rate of 86% (478 completed surveys). However, during data refinement, 22 responses were excluded due to missing or contradictory data. Only surveys with complete responses for the final set of questions were included in the final analysis (456).
To address potential biases, such as self-selection bias, we employed several procedural measures. First, participants were assured of the survey’s anonymity and encouraged to provide honest responses, emphasizing that the information would only be used for research purposes. Second, the cover page avoided mentioning any connections between the study variables to psychologically isolate the respondents. Finally, to further reduce participants’ perception of connections, the survey asked about social media usage first, followed by questions on teamwork (TW), internal communication (IC), and job performance.
A self-reported questionnaire was developed to measure the key constructs of the study. The questionnaire employed a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree) for all items. The data collection for this study was conducted during Spring 2024.
To ensure the clarity and effectiveness of the questionnaire, we conducted a pilot study with a small group of 12 teaching faculty members before the main data collection phase. This pilot testing involved administering the survey to the pilot group and then analyzing the responses. The pilot testing helped us identify any issues with the survey instrument, such as unclear questions, ambiguous wording, or problematic flow. Based on the pilot study results, we refined the questionnaire by removing unclear questions or modifying the wording for better clarity. This iterative process ensured that the final survey instrument used for data collection was well-understood and easy for participants to complete.
3.2. Measurement
Most constructs’ measurement scales were modified versions of previously used ones. Minor adjustments were made to the existing Janssen and Yperen [30] scales for innovative and routine job performance to fit the self-reported setup of this research. It is noteworthy that self-reporting of creative and/or standard job performance occurs frequently [41,106,107], especially when confidentiality concerns prevent access to objective performance measures. Researchers have not found the ideal metric for assessing an individual’s performance, and self-reported data is no less accurate than other types of data [41].
The social use of the SM scale was borrowed from Ali-Hassan et al. [11], and the measurement scale for cognitive use was derived from social interaction ties found in Chiu et al., [108]. The knowledge-sharing instrument developed by Van den Hooff and Huysman [109] and the purpose to share implied knowledge scale developed by [110] informed the cognitive use component of SM. The hedonic use of SM items was inspired by Nevo and Nevo’s [111] scale for using virtual environments and Agarwal and Karahanna’s [112] scale for increased enjoyment of using the web. Measurement scales for measuring internal communication were adopted from Lee, Park and Lee [113], and for measuring teamwork, items were drawn from Jiang and Chen [61]. Table 1 presents the measurement items.
To address potential common method bias arising from the self-reported survey data, we employed factor analysis using AMOS software. Specifically, Harman’s one-factor test was conducted to assess the explained variance by a single common factor.
Confirmatory factor analysis (CFA) was performed using AMOS 24.0 to assess the measurement model fit. This analysis evaluated the reliability and validity of the constructs measured in our survey instrument. Various fit indices, including the Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR), were employed to assess the overall model fit and the fit of the measurement model. Thresholds for acceptable fit were established based on prior research [114].
Structural Equation Modeling (SEM) was then conducted using AMOS to test the proposed model. SEM allowed for the evaluation of the hypothesized relationships between the research variables. The analysis included assessing direct and indirect effects, mediation analysis, and testing for partial and serial mediation.
4. Results
4.1. Sample Demographics
Table 2 shows that male participants (55.5%) outnumbered female respondents (44.5%). This finding is fairly near to the sex distribution of Indian workers. Additionally, the majority of respondents (45.6%) were between the ages of 31 and 40. The majority of participants (50.7%) were assistant professors and worked as regular employees (67.5%). The respondents’ demographic characteristics are presented in Table 2.
4.2. Measurement Model Evaluation
Factor analysis was conducted to assess common method bias using Harman’s one-factor test [115]. Only 12.825% of the total variance was explained by the first factor, well below the acceptable threshold of 40% [116]. This result suggests that common method bias was not a significant concern in our data.
While the chi-square test statistic (χ2) is often used in CFA, it can be sensitive to sample size, particularly in large samples like ours (456 participants). In our analysis, the chi-square value was 2.189 [117]. Therefore, we relied on alternative fit indices to provide a more robust assessment of model fit. The analysis yielded satisfactory results across all the alternative fit indices used. The Normed Fit Index (NFI) reached a value of 0.917, exceeding the recommended threshold of 0.90 [118]. The Comparative Fit Index (CFI) and the Tucker–Lewis Index (TLI) were both well above 0.90 (CFI = 0.953, TLI = 0.947) [118], further supporting a good model fit. Finally, the Root Mean Square Error of Approximation (RMSEA) fell below the recommended value of 0.08, with an actual value of 0.051 [118]. These results collectively suggest that the proposed model adequately represents the relationships between the study variables in our data.
To assess the internal consistency and reliability of our measures, we examined both alpha coefficients (α) and composite reliability (C.R.) values. As shown in Table 3, all constructs achieved alpha coefficients and composite reliability estimates exceeding 0.7, which satisfy the established benchmarks for acceptable reliability [119,120].
Convergent validity refers to the extent to which a measure captures its intended construct. We evaluated convergent validity using average variance extracted (AVE), composite reliability (C.R.), factor loadings, and significance levels of factor loadings. [120] suggest that acceptable convergent validity is indicated by a C.R. greater than 0.7, an AVE exceeding 0.5, statistically significant factor loadings (p < 0.01), and factor loadings with an absolute value greater than 0.7. The results presented in Table 3 demonstrate that all constructs meet these criteria for convergent validity.
In Table 4, the correlation analysis of seven constructs was conducted. The data suggest that the constructs are generally independent of one another because the square root of each AVE is bigger than its construct relationships.
The heterotrait–monotrait ratio of correlations (HTMT) matrix table values all fell below 0.85, thus supporting the discriminant validity of each construct. This result is presented in Table 5.
4.3. Validation of the Structural Model
Four statistical procedures were employed using CB-SEM with AMOS 24.0 to validate the hypothesized relationships in the structural model: mediation analysis, assessment of specific indirect effects, parallel mediation analysis to confirm individual mediator effects, and analysis of serial mediation to explore potential cascading effects between mediators.
The first step examined whether the independent variables (social, hedonic, and cognitive use of social media) exerted any direct effects on the dependent variable (innovative and routine job performance). The path coefficients in the model represent the total effects of social use, hedonic use, and cognitive use of social media on innovative and routine job performance, respectively (estimates: 0.198, 0.231, 0.418, 0.131, 0.087, and 0.416). All total effects were statistically significant (p < 0.05) except for the effects of social use (p > 0.05) and cognitive use (p > 0.05) on innovative job performance. Therefore, the results support all hypotheses H1–H3, but not H3a based on the total effects analysis.
In the second step, the hypothesized mediators (internal communication and teamwork) were incorporated into the path model alongside the direct effects. The results revealed that for the relationships between SU and IJP, HU and RJP, and CU and IJP, the introduction of the mediators explained a significant portion of the initial relationships. This is because the direct effects of the independent variables on the dependent variables became non-significant. This suggests that internal communication and teamwork likely play a mediating role in these specific relationships.
However, for the relationships between SU and RJP, HU and IJP, and CU and RJP, the introduction of the mediators did not fully explain the initial relationships. While the direct effects of the independent variables were somewhat reduced, they remained statistically significant. This suggests that internal communication and teamwork might partially explain these relationships, but there might be other factors at play.
The analysis revealed that social use (estimate: 0.177), hedonic use (estimate: 0.384), and cognitive use (estimate: 0.103) of social media directly influence internal communication, which in turn, impacts innovative (estimate: 0.217) and routine job performance (estimate: 0.130). Similarly, social use (estimate: 0.118), hedonic use (estimate: 0.228), and cognitive use (estimate: 0.100) of social media directly influence teamwork, which in turn, impacts innovative (estimate: 0.478) and routine job performance (estimate: 0.136). Additionally, the analysis found a significant direct effect of internal communication on teamwork (estimate: 0.264).
Most hypothesized relationships were statistically significant (p < 0.05), supporting hypotheses H4a through H5c (except H5a), H8a and H8b, and H9a through H10. The effect of social use of social media on teamwork (H5a) was not statistically significant (p > 0.05). This finding suggests no mediation for the relationship between social media use and teamwork (H5a), while the remaining significant relationships with internal communication (INC) as a mediator likely involve partial mediation (SU ---> INC ---> IJP, HU ---> INC ---> RJP, and CU ---> INC ---> IJP). The detailed results of the mediated effects analysis are presented in Table 6.
Figure 3 illustrates the strength and direction of the relationships between social media use (SU, HU, and CU), internal communication (INC), teamwork (TW), and job performance (IJP and RJP).
To examine the specific indirect effects of both mediators, a parallel mediation analysis was employed [121]. The results revealed significant indirect effects of social media use (social, hedonic, and cognitive) on innovative job performance through internal communication (b = 0.03, p = 0.002; b = 0.067, p = 0.001; b = 0.015, p = 0.014, respectively). Similarly, significant indirect effects were found for social media use on innovative job performance through teamwork (b = 0.045, p = 0.043; b = 0.088, p = 0.001; b = 0.032, p = 0.029, respectively).
Furthermore, the analysis revealed significant indirect effects of social media use on routine job performance, again mediated by internal communication (b = 0.023, p = 0.008; b = 0.05, p = 0.01; b = 0.011, p = 0.014, respectively). Social media use also had a significant indirect impact on routine job performance through teamwork (b = 0.016, p = 0.032; b = 0.031, p = 0.014; b = 0.011, p = 0.022, respectively). These findings suggest the presence of partial mediation in most relationships [122].
The significant direct effect of internal communication on teamwork (H10) prompted a follow-up hypothesis regarding a potential serial mediation process. This additional hypothesis was not initially considered because the focus was primarily on the direct influence of social media use on job performance, with internal communication seen as a single mediator. However, the unexpected finding of a strong relationship between internal communication and teamwork warranted further exploration of a multi-step mediation model. Serial mediation was examined to investigate the potential influence of internal communication on teamwork, following the alternative theory that emotions precede thoughts [123]. The results revealed significant effects for serial mediation, with detailed outcomes presented in Table 7.
5. Discussion
The study’s findings offer a comprehensive understanding of how different dimensions of social media use influence job performance through the mediating roles of internal communication and teamwork.
Social Use (SU): The study found that social use of social media positively impacts routine job performance (RJP) both directly and indirectly through internal communication (INC) and teamwork (TW). Additionally, social use positively impacts innovative job performance (IJP) indirectly through INC and TW, although the direct effect is not significant. This finding highlights the importance of social interactions facilitated by social media in enhancing routine tasks. It suggests that social media can be a valuable tool for maintaining communication and collaboration, which are crucial for routine job performance. These results align with previous research indicating the positive effects of SU on workplace communication and collaboration [11,18].
Hedonic Use (HU): Hedonic use of social media, which involves using social media for pleasure and entertainment, was found to positively impact innovative job performance (IJP) both directly and indirectly through INC and TW. It also positively impacts routine job performance (RJP) indirectly through INC and TW, but the direct effect is not significant. This finding is particularly noteworthy because it contrasts with much of the existing literature, which often reports a negative relationship between hedonic use of social media and routine job performance. For instance, Ali-Hassan et al. [11] discuss how hedonic use generally has a negative impact on routine performance but can contribute positively to social ties and innovative performance. Similarly, Jong et al. [42] highlight the complex effects of social media use on work efficiency, noting that different types of use can have varying impacts. Kock and Moqbel [124] also suggest that positive emotions related to social media use can enhance job satisfaction and performance. Our study suggests that hedonic use can indeed have a beneficial effect, highlighting the complexity of social media’s impact on work performance.
Cognitive Use (CU): The findings of this study highlight the significant role of cognitive use of social media in enhancing job performance. Specifically, cognitive engagement with social media positively impacts routine job performance (RJP) both directly and indirectly through improved internal communication (INC) and teamwork (TW). This aligns with previous research indicating that social media use can foster better communication and collaboration among employees, thereby enhancing their routine job performance [11,125]. Moreover, while cognitive use of social media positively influences innovative job performance (IJP) indirectly through INC and TW, the direct effect is not significant. This suggests that the benefits of cognitive engagement with social media on innovation are primarily mediated by enhanced communication and teamwork. These results are consistent with studies showing that social media can facilitate knowledge transfer and the formation of social capital, which are crucial for innovative performance [125]. Overall, the study extends the understanding of how cognitive engagement with social media can be leveraged to improve job performance. By using social media for information and knowledge sharing, employees can enhance their communication and teamwork, leading to better routine and innovative job performance. These insights are valuable for organizations aiming to optimize their social media strategies to boost employee performance.
The findings of this study underscore the significant mediating roles of Internal Communication (INC) and Teamwork (TW) in the relationship between social media use (SU, HU, and CU) and job performance (IJP and RJP). Both INC and TW are crucial in translating social media use into improved job performance. This aligns with the theory of parallel mediation, highlighting that effective communication and collaboration are key mechanisms through which social media use can enhance job performance [11,42].
The contrast comparison analysis revealed a stronger mediating effect of internal communication, particularly for the relationship between hedonic use (HU) and job performance. This finding extends existing knowledge by providing a more nuanced understanding of how social media use influences job performance. It suggests that internal communication is a critical factor in leveraging the benefits of hedonic social media use for job performance [126,127].
The study provides detailed insights into the pathways through which social media use influences job performance. Social use (SU) positively impacts both innovative job performance (IJP) and routine job performance (RJP) through improved internal communication and teamwork. Similarly, hedonic use (HU) enhances job performance through these mediators, with internal communication playing a particularly strong role. Cognitive use (CU) also positively influences job performance through the same pathways.
While the initial conceptual model focused on parallel mediation, the study also explored serial mediation. This analysis examined whether social media use influences job performance through a sequential process, where internal communication first affects teamwork, and then teamwork affects job performance. Interestingly, the results provided evidence supporting this unexpected serial mediation effect. This finding suggests that both internal communication and teamwork can play a role in influencing job performance but through different mediating pathways. This supports existing theories that emphasize the role of communication and collaboration in improving job performance through social media use [128,129].
Our findings align with those of Ali-Hassan et al. [11] and Jong et al. [42], who also identified internal communication and teamwork as key mediators in the relationship between social media use and job performance. These studies highlight the importance of effective communication and collaboration in leveraging social media for improved job outcomes. Furthermore, the stronger mediating effect of internal communication, particularly for hedonic use, is consistent with the findings of Moqbel et al. [126] and Fusi and Feeney [127], who emphasize that internal communication is crucial for translating the benefits of hedonic social media use into enhanced job performance.
Additionally, the unexpected serial mediation effect observed in our study, where internal communication influences teamwork, which in turn affects job performance, supports the theories proposed by [128,129]. These theories suggest that communication and collaboration are sequential processes that collectively enhance job performance through social media use.
6. Theoretical and Practical Implication
In this study, we explored whether and to what degree social media (SM) use influences routine and creative job performance. Our findings suggest that the context in which social media is used significantly influences job performance outcomes unique to the workplace. Specifically, using social media for knowledge exchange, entertainment, or socializing has different effects on work performance. We found that internal communication and teamwork are the mechanisms through which these changes occur. This discovery establishes a new practical connection between SM, internal communication, teamwork, and job performance.
Our results indicate that friendships among coworkers in a multi-person online relationship promote the development of strong bonds, which in turn open up opportunities for coworkers to communicate through comments. This can enhance teamwork and effective communication, leading to fewer misunderstandings. This finding is particularly relevant in today’s increasingly digital work environments, where effective communication and collaboration are essential. Kamboj et al. [23] support this by highlighting the role of social media in fostering strong interpersonal relationships and reducing misunderstandings.
Additionally, our study contributes to the ongoing debate among management regarding the use of SM at work. While social media may negatively impact some jobs, such as routine ones, it can positively affect more innovative and creative roles. Our findings suggest that social media technologies when used appropriately, can improve employees’ job performance. Ali-Hassan et al. [11] also found that social media use can enhance job performance, particularly in creative tasks. Employees must understand how social media contributes to internal communication and teamwork within their work environment culture, resulting in better job performance. This is supported by [130], who emphasizes the importance of understanding the cultural context in which social media is used.
In the context of universities, social media’s advantages may include the ability to plan meetings, make appointments, exchange research papers, and share information about work activities with coworkers. University administration should clearly understand the kinds of work performance that are beneficial to them and plan their SM utilization according to their institutional culture. Çetinkaya and Sütçü [131] highlight the need for institutions to align social media use with their specific cultural and operational needs. By integrating concepts from the uses and gratification theory [22], our research provides a theoretical framework for understanding how people use SM at the workplace according to their institutional culture, as it differs from one institution to another.
Finally, we recommend legalizing the use of SM during work hours to plan work-related events and blur the distinction between business and social activities. These activities promote the teamwork and efficient internal communication needed to achieve high levels of job performance on SM platforms. Managers can benefit from teaching staff how to use social media to maximize their professional potential. Ghorbanzadeh et al. [18] support this by demonstrating the benefits of social media training for enhancing professional capabilities.
7. Limitation and Future Research Direction
This study differs from others in that it is primarily concerned with assessing the work performance of teachers. Teaching is distinct from other professions it is not exclusively concerned with output or productivity measurements since it entails not only presenting knowledge but also managing classroom dynamics, evaluating student achievement, and promoting social and emotional growth. Because of this, the research’s findings and conclusions are specific to the teaching profession and might not apply to other professions whose performance standards and work circumstances are very different. The study’s limitations should be taken into account when evaluating the findings.
Additionally, the findings may be limited to the specific cultural and institutional context of northern India. Cultural norms, institutional policies, and regional educational practices can significantly influence how social media is used and its impact on job performance. Therefore, the generalizability of the results to other regions or types of institutions may be limited.
A longer-term study is required to fully comprehend the effects of social media on teachers’ job performance, even though this one offers insightful information in this area. The relationship between social media use and job performance at work may alter over time, and studies conducted over a short period can miss these dynamic shifts. Researchers could see patterns, trends, and possible behavioral changes over time with a longitudinal approach, leading to a more thorough understanding of how social media affects teachers’ performance over the course of their careers. Such studies could provide more reliable results and guide future policy.
To provide a more thorough explanation of the phenomena, future research should examine how social media use affects job performance using a variety of theoretical frameworks. Applying additional theories, such as the Job Demands–Resources (JD-R) Model, Media Richness Theory (MRT), or the Technology Acceptance Model (TAM), could provide fresh perspectives even though this study may have concentrated on particular models. These theories may shed light on the ways in which social media affects engagement, stress, motivation, and teamwork. Researchers can reveal more intricate links and complexities by utilizing a variety of theoretical viewpoints, which will enhance their ability to analyze the effects of social media on job performance.
8. Conclusions
The findings of this study illuminate the multifaceted impact of social media use on job performance, mediated by internal communication and teamwork. This research underscores the potential benefits of social media in enhancing both routine and innovative job performance through improved communication and collaboration among employees.
Organizations that restrict or ban social media use may inadvertently forgo these advantages, missing out on opportunities to foster better teamwork, knowledge sharing, and problem-solving capabilities. By understanding the nuanced effects of different types of social media use—social, hedonic, and cognitive—managers can develop informed policies that leverage these tools to enhance employee performance while mitigating potential downsides.
This study contributes to the broader discourse on the role of social media in the workplace, offering valuable insights for organizations aiming to optimize their social media strategies. By embracing the positive aspects of social media use, educational institutes can create a more connected, innovative, and productive workforce, ultimately driving better organizational outcomes. University administrators must understand how social media contributes to internal communication and teamwork within their work environment culture, which results in better job performance. University administration should clearly understand the kinds of work performance that are beneficial to them and plan their SM utilization according to their institutional culture.
While this study provides valuable insights into the impact of social media use on faculty job performance, several limitations should be acknowledged. Firstly, the study was conducted among faculty members at public state colleges in northern India. Consequently, the findings may not be generalizable to faculty in other regions or countries with different cultural, educational, and technological contexts. Secondly, the data were collected through an online survey, which relies on self-reported measures. This method may introduce biases such as social desirability bias, where respondents might overreport positive behaviors or underreport negative ones. Thirdly, the study employs a cross-sectional design, which captures data at a single point in time. This limits the ability to infer causality between social media use and job performance.
To build on the findings of this study, future research could explore several areas. Conducting longitudinal studies would help establish causal relationships between social media use and job performance, providing a clearer picture of how these dynamics evolve over time. Expanding the research to include faculty from different regions, countries, and types of educational institutions would enhance the generalizability of the findings. Additionally, examining other professional groups could provide insights into how social media use impacts job performance across various occupations and industries. Investigating other dimensions of social media use, such as professional networking, marketing, and advocacy, could offer a more comprehensive understanding of how these platforms influence job performance. Exploring the role of organizational culture, leadership styles, and individual differences in technology proficiency could also provide a more nuanced view of the factors that mediate the relationship between social media use and job performance.
Conceptualization, S.K., Z.S., R.J., I.S. and R.R.-A.; methodology S.K., Z.S., R.J., I.S. and R.R.-A.; validation, S.K., Z.S., R.J., I.S. and R.R.-A.; formal analysis, S.K., Z.S., R.J., I.S. and R.R.-A.; data curation, S.K., Z.S., R.J., I.S. and R.R.-A.; writing—original draft preparation, S.K., Z.S., R.J., I.S. and R.R.-A.; writing—review and editing, R.R.-A. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Data are available from authors upon reasonable request.
This paper is part of the project COST CA19130 FinAI—Fintech and Artificial Intelligence in Finance—Towards a Transparent Financial Industry.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Measurement items.
| Construct | Items | Source |
|---|---|---|
| Social Use of Social Media (SU) | Make new connections at work | [ |
| Get to know coworkers who have similar interests to me | ||
| Learn about professional opportunities from colleagues on social media | ||
| Connect with colleagues from different departments on social media | ||
| Build relationships with mentors or potential collaborators on social media | ||
| Hedonic Use of Social Media | Use social media to appreciate my pause at work | [ |
| Take a break from my job by browsing social media for entertainment | ||
| Amuse myself on social media during my work breaks | ||
| Relax at my workstation by using social media for lighthearted interaction or browsing | ||
| Cognitive Use of Social Media | Share documents, presentations, or other work-related content with colleagues on social media | [ |
| Use social media to stay updated on information and resources shared by colleagues | ||
| Collaborate with colleagues on creating content for work-related projects using social media | ||
| Post questions or requests for information on social media to leverage colleagues’ expertise | ||
| Use social media to identify and learn from best practices shared by colleagues | ||
| Internal Communication (IC) | I use social media to communicate with my coworkers | [ |
| Social media is an effective tool for communicating within our organization | ||
| Our organization’s social media policies support effective internal communication | ||
| Social media has improved my ability to collaborate with colleagues | ||
| Social media has increased my awareness of organizational events and news | ||
| Teamwork (TW) | Team members use social media to openly share information and knowledge with each other | [ |
| Participants of the team use SM to keep each other informed about progress | ||
| Members of the team work together to coordinate tasks and resources through social media | ||
| Members of the team respect each other’s opinions and ideas on social media | ||
| Innovative Job Performance (IJP) | Generate new and original ideas for improving work processes or products | [ |
| Champion creative ideas proposed by yourself or others | ||
| Seek out unconventional approaches to solve problems | ||
| Develop practical applications for creative ideas | ||
| Provide unique solutions to challenges faced at work | ||
| Routine Job Performance (RJP) | I consistently finish the tasks outlined in my job description | |
| I consistently fulfill all of my job’s formal performance standards | ||
| I prioritize completing tasks outlined in my job description | ||
| I am able to meet all deadlines associated with my work responsibilities |
Demographic profile.
| Measure | Item | Frequency | Percentage |
|---|---|---|---|
| Gender | Male | 253 | 55.5 |
| Female | 203 | 44.5 | |
| Marital Status | Single | 140 | 30.7 |
| Married | 316 | 69.3 | |
| Age | 21–30 | 68 | 14.9 |
| 31–40 | 208 | 45.6 | |
| 41–50 | 137 | 30.0 | |
| 50 and above | 43 | 9.4 | |
| Designation | Assistant Professor | 231 | 50.7 |
| Associate Professor | 181 | 39.7 | |
| Professor | 44 | 9.6 | |
| Experience | Less than 10 years | 242 | 53.07 |
| N (456) |
Outcomes of the measurement model.
| Variables | Items | Factor Loadings | CR | AVE | CA |
|---|---|---|---|---|---|
| Social Use of Social Media | SU1 | 0.827 | 0.902 | 0.649 | 0.897 |
| Hedonic Use of Social Media | HU1 | 0.826 | 0.910 | 0.717 | 0.908 |
| Cognitive Use of Social Media | CU1 | 0.894 | 0.953 | 0.804 | 0.948 |
| Internal Communication | IC1 | 0.870 | 0.943 | 0.770 | 0.941 |
| Teamwork | TW1 | 0.816 | 0.910 | 0.717 | 0.905 |
| Innovative Job Performance | IJP1 | 0.790 | 0.898 | 0.596 | 0.89 |
| Routine Job Performance | RJP1 | 0.786 | 0.842 | 0.572 | 0.838 |
Square roots of AVEs.
| IJP | SU | CU | HU | INC | TW | RJP | |
|---|---|---|---|---|---|---|---|
| IJP | 0.772 | ||||||
| SU | 0.436 | 0.805 | |||||
| CU | 0.296 | 0.513 | 0.897 | ||||
| HU | 0.520 | 0.573 | 0.347 | 0.847 | |||
| INC | 0.535 | 0.407 | 0.293 | 0.485 | 0.877 | ||
| TW | 0.669 | 0.381 | 0.295 | 0.437 | 0.439 | 0.847 | |
| RJP | 0.301 | 0.471 | 0.528 | 0.365 | 0.372 | 0.373 | 0.756 |
Heterotrait–Monotrait ratio (HTMT).
| SU | CU | HU | INC | TW | IJP | RJP | |
|---|---|---|---|---|---|---|---|
| SU | |||||||
| CU | 0.531 | ||||||
| HU | 0.589 | 0.354 | |||||
| INC | 0.412 | 0.303 | 0.497 | ||||
| TW | 0.381 | 0.302 | 0.445 | 0.453 | |||
| IJP | 0.463 | 0.304 | 0.531 | 0.536 | 0.689 | ||
| RJP | 0.478 | 0.534 | 0.369 | 0.382 | 0.370 | 0.315 |
Results of Parallel Mediation model.
| Path | Standardized Path Coefficients (β) | 95% Confidence Level (Lower Bound, Upper Bound) | Sig. Level | Hypothesis | Supported? |
|---|---|---|---|---|---|
| Total Effects | |||||
| SU ---> IJP | 0.198 | (0.056, 0.34) | 0.008 | H1a (total) | Yes |
| SU ---> RJP | 0.231 | (0.096, 0.352) | 0.002 | H1b (total) | Yes |
| HU ---> IJP | 0.418 | (0.293, 0.533) | 0.001 | H2a (total) | Yes |
| HU ---> RJP | 0.131 | (0.029, 0.25) | 0.014 | H2b (total) | Yes |
| CU ---> IJP | 0.087 | (−0.009, 0.182) | 0.074 | H3a (total) | No |
| CU ---> RJP | 0.416 | (0.296, 0.538) | 0.001 | H3b (total) | Yes |
| Direct Effects | |||||
| SU ---> IJP | 0.081 | (0.034, 0.198) | 0.191 | H1a (direct) | No |
| SU ---> RJP | 0.186 | (0.054, 0.307) | 0.004 | H1b (direct) | Yes |
| HU ---> IJP | 0.177 | (0.054, 0.3) | 0.006 | H2a (direct) | Yes |
| HU ---> RJP | 0.037 | (0.08, 0.17) | 0.547 | H2b (direct) | No |
| CU ---> IJP | 0.004 | (0.087, 0.093) | 0.934 | H3a (direct) | No |
| CU ---> RJP | 0.385 | (0.262, 0.509) | 0.001 | H3b (direct) | Yes |
| SU ---> INC | 0.177 | (0.058, 0.294) | 0.004 | H4a | Yes |
| HU ---> INC | 0.384 | (0.258, 0.497) | 0.001 | H4b | Yes |
| CU ---> INC | 0.103 | (−0.016, 0.194) | 0.02 | H4c | Yes |
| SU ---> TW | 0.118 | (0.003, 0.233) | 0.057 | H5a | No |
| HU ---> TW | 0.228 | (0.088, 0.359) | 0.002 | H5b | Yes |
| CU ---> TW | 0.1 | (0.012, 0.2) | 0.03 | H5c | Yes |
| INC ---> IJP | 0.217 | (0.116, 0.328) | 0.001 | H8a | Yes |
| INC ---> RJP | 0.13 | (0.024, 0.231) | 0.016 | H8b | Yes |
| TW ---> IJP | 0.478 | (0.353, 0.592) | 0.001 | H9a | Yes |
| TW ---> RJP | 0.136 | (0.022, 0.24) | 0.025 | H9b | Yes |
| INC ---> TW | 0.264 | (0.14, 0.364) | 0.002 | H10 | Yes |
| Indirect Effects | |||||
| SU ---> INC ---> IJP | 0.03 | (0.012, 0.064) | 0.002 | H6a1 | Yes (Full mediation) |
| SU ---> INC ---> RJP | 0.023 | (0.005, 0.055) | 0.008 | H6a2 | Yes (Partial Mediation) |
| HU ---> INC ---> IJP | 0.067 | (0.03, 0.114) | 0.001 | H6b1 | Yes (Partial Mediation) |
| HU ---> INC ---> RJP | 0.05 | (0.011, 0.1) | 0.01 | H6b2 | Yes (Full mediation) |
| CU ---> INC ---> IJP | 0.015 | (0.003, 0.033) | 0.014 | H6c1 | Yes (Full mediation) |
| CU ---> INC ---> RJP | 0.011 | (0.002, 0.03) | 0.014 | H6c2 | Yes (Partial Mediation) |
| SU ---> TW ---> IJP | 0.045 | (0.002, 0.096) | 0.043 | H7a1 | Yes (Partial Mediation) |
| SU ---> TW ---> RJP | 0.016 | (0.001. 0.046) | 0.032 | H7a2 | Yes (Partial Mediation) |
| HU ---> TW ---> IJP | 0.088 | (0.032, 0.166) | 0.001 | H7b1 | Yes (Partial Mediation) |
| HU ---> TW ---> RJP | 0.031 | (0.006, 0.075) | 0.014 | H7b2 | Yes (Partial Mediation) |
| CU ---> TW ---> IJP | 0.032 | (0.004, 0.067) | 0.029 | H7c1 | Yes (Partial Mediation) |
| CU ---> TW ---> RJP | 0.011 | (0.002, 0.035) | 0.022 | H7c2 | Yes (Partial Mediation) |
Result of serial mediation.
| Path | Standardized Path Coefficients (β) | 95% Confidence Level (Lower Bound, Upper Bound) | Sig. Level | Result |
|---|---|---|---|---|
| SU ---> INC ---> TW ---> IJP | 0.018 | (0.006, 0.037) | 0.002 | Significant |
| SU ---> INC ---> TW ---> RJP | 0.006 | (0.001, 0.017) | 0.01 | Significant |
| HU ---> INC ---> TW ---> IJP | 0.039 | (0.019, 0.066) | 0.001 | Significant |
| HU ---> INC ---> TW ---> RJP | 0.014 | (0.003, 0.033) | 0.014 | Significant |
| CU ---> INC ---> TW ---> IJP | 0.009 | (0.002, 0.019) | 0.014 | Significant |
| CU ---> INC ---> TW ---> RJP | 0.003 | (0, 0.008) | 0.018 | Significant |
References
1. Chugh, R.; Ruhi, U. Social media for tertiary education. Encyclopedia of Education and Information Technologies; Tatnall, A. Springer Nature: Cham, Switzerland, 2019; [DOI: https://dx.doi.org/10.1007/978-3-319-60013-0_202-1]
2. Carrigan, M. Social Media for Academics; Sage Publications Limited: Thousand Oaks, CA, USA, 2019; [DOI: https://dx.doi.org/10.51952/9781529201062.ch006]
3. Cao, X.; Yu, L. Exploring the influence of excessive social media use at work: A three-dimension usage perspective. Int. J. Inf. Manag.; 2019; 46, pp. 83-92. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2018.11.019]
4. Jain, R.; Kumar, S.; Sood, K.; Grima, S.; Rupeika-Apoga, R. A Systematic Literature Review of the Risk Landscape in Fintech. Risks; 2023; 11, 36. [DOI: https://dx.doi.org/10.3390/risks11020036]
5. Chang, T.S.; Hsiao, W.H. Time spent on social networking sites: Understanding user behavior and social capital. Syst. Res. Behav. Sci.; 2014; 31, pp. 102-114. [DOI: https://dx.doi.org/10.1002/sres.2169]
6. Kaplan, A.M.; Haenlein, M. Users of the world, unite! The challenges and opportunities of Social Media. Bus. Horiz.; 2010; 53, pp. 59-68. [DOI: https://dx.doi.org/10.1016/j.bushor.2009.09.003]
7. Welch, M.; Jackson, P.R. Rethinking internal communication: A stakeholder approach. Corp. Commun. Int. J.; 2007; 12, pp. 177-198. [DOI: https://dx.doi.org/10.1108/13563280710744847]
8. Etter, M. Broadcasting, reacting, engaging–three strategies for CSR communication in Twitter. J. Commun. Manag.; 2014; 18, pp. 322-342. [DOI: https://dx.doi.org/10.1108/JCOM-01-2013-0007]
9. Leonardi, P.M.; Vaast, E. Social media and their affordances for organizing: A review and agenda for research. Acad. Manag. Ann.; 2017; 11, pp. 150-188. [DOI: https://dx.doi.org/10.5465/annals.2015.0144]
10. O’Leary, M.B.; Mortensen, M.; Woolley, A.W. Multiple team membership: A theoretical model of its effects on productivity and learning for individuals and teams. Acad. Manag. Rev.; 2011; 36, pp. 461-478. [DOI: https://dx.doi.org/10.5465/amr.2009.0275]
11. Ali-Hassan, H.; Nevo, D.; Wade, M. Linking dimensions of social media use to job performance: The role of social capital. J. Strateg. Inf. Syst.; 2015; 24, pp. 65-89. [DOI: https://dx.doi.org/10.1016/j.jsis.2015.03.001]
12. Vandeyar, T. The academic turn: Social media in higher education. Educ. Inf. Technol.; 2020; 25, pp. 5617-5635. [DOI: https://dx.doi.org/10.1007/s10639-020-10240-1]
13. Manca, S.; Ranieri, M. Yes for sharing, no for teaching!”: Social media in academic practices. Internet High. Educ.; 2016; 29, pp. 63-74. [DOI: https://dx.doi.org/10.1016/j.iheduc.2015.12.004]
14. Mou, Y. Presenting professorship on social media: From content and strategy to evaluation. Chin. J. Commun.; 2014; 7, pp. 389-408. [DOI: https://dx.doi.org/10.1080/17544750.2014.938669]
15. Sobaih, A.E.E.; Moustafa, M.A.; Ghandforoush, P.; Khan, M. To use or not to use? Social media in higher education in developing countries. Comput. Hum. Behav.; 2016; 58, pp. 296-305. [DOI: https://dx.doi.org/10.1016/j.chb.2016.01.002]
16. Knight, C.G.; Kaye, L.K. ‘To tweet or not to tweet? a comparison of academics’ and students’ us-age of twitter in academic contexts. Innov. Educ. Teach. Int.; 2016; 53, pp. 145-155. [DOI: https://dx.doi.org/10.1080/14703297.2014.928229]
17. Murphy, J. We Asked Teachers about Their Social Media Use. Some of Their Answers Surprised Us. 2018; Available online: https://mdreducation.com/2019/01/17/teachers-social-media-use/ (accessed on 25 January 2020).
18. Ghorbanzadeh, D.; Khoruzhy, V.I.; Safonova, I.V.; Morozov, I.V. Relationships between social media usage, social capital and job performance: The case of hotel employees in Iran. Inf. Dev.; 2021; 39, pp. 6-18. [DOI: https://dx.doi.org/10.1177/02666669211030553]
19. Ellison, N.B.; Gibbs, J.L.; Weber, M.S. The use of enterprise social network sites for knowledge sharing in distributed organizations. Am. Behav. Sci.; 2015; 59, pp. 103-123. [DOI: https://dx.doi.org/10.1177/0002764214540510]
20. Soni, V.D. Importance and strategic planning of team management. Int. J. Innov. Res. Technol.; 2020; 7, pp. 47-50.
21. Preacher, K.J.; Hayes, A.F. Asymptotic and Resampling Strategies for Assessing and Comparing Indirect Effects in Multiple Mediator Models. Behav. Res. Methods; 2008; 40, pp. 879-891. [DOI: https://dx.doi.org/10.3758/BRM.40.3.879]
22. Katz, E.; Blumler, J.G.; Gurevitch, M. Uses and gratifications research. Public Opin. Q.; 1973; 37, pp. 509-523. [DOI: https://dx.doi.org/10.1086/268109]
23. Kamboj, S.; Kumar, V.; Rahman, Z. Social media usage and firm performance: The mediating role of social capital. Soc. Netw. Anal. Min.; 2017; 7, pp. 35-51. [DOI: https://dx.doi.org/10.1007/s13278-017-0468-8]
24. Quan-Haase, A.; Young, A.L. Uses and gratifications of social media: A comparison of Facebook and instant messaging. Bull. Sci. Technol. Soc.; 2010; 30, pp. 350-361. [DOI: https://dx.doi.org/10.1177/0270467610380009]
25. Whiting, A.; Williams, D. Why people use social media: A uses and gratifications approach. Qual. Mark. Res. Int. J.; 2013; 16, pp. 362-369. [DOI: https://dx.doi.org/10.1108/QMR-06-2013-0041]
26. Sparrowe, R.T.; Liden, R.C.; Wayne, S.J.; Kraimer, M.L. Social networks and the performance of individuals and groups. Acad. Manag. J.; 2001; 44, pp. 316-325. [DOI: https://dx.doi.org/10.2307/3069458]
27. Amabile, T.M.; Conti, R.; Coon, H.; Lazenby, J.; Herron, M. Assessing the work environment for creativity. Acad. Manag. J.; 1996; 39, pp. 1154-1184. [DOI: https://dx.doi.org/10.2307/256995]
28. Scott, S.G.; Bruce, R.A. Determinants of innovative behavior: A path model of individual innova-tion in the workplace. Acad. Manag. J.; 1994; 37, pp. 580-607. [DOI: https://dx.doi.org/10.2307/256701]
29. Kanter, R.M. Three Tiers for Innovation Research. Commun. Res.; 1988; 15, pp. 509-523. [DOI: https://dx.doi.org/10.1177/009365088015005001]
30. Janssen, O.; Van Yperen, N.W. Employees’ goal orientations, the quality of leader-member ex-change, and the outcomes of job performance and job satisfaction. Acad. Manag. J.; 2004; 47, pp. 368-384. [DOI: https://dx.doi.org/10.2307/20159587]
31. Suryanto, A.; Fitriati, R.; Natalia, S.I.; Oktariani, A.; Munawaroh, M.; Nurdin, N.; Ahn, Y.H. Study of working from home: The impact of ICT anxiety and smartphone addiction on lecturers at NIPA School of Administration on job performance. Heliyon; 2022; 8, e11980. [DOI: https://dx.doi.org/10.1016/j.heliyon.2022.e11980]
32. Ali, A.; Wang, H.; Khan, A.N. Mechanism to enhance team creative performance through social media: A transactive memory system approach. Comput. Hum. Behav.; 2019; 91, pp. 115-126. [DOI: https://dx.doi.org/10.1016/j.chb.2018.09.033]
33. Rupeika-Apoga, R.; Wendt, S. FinTech Development and Regulatory Scrutiny: A Contradiction? The Case of Latvia. Risks; 2022; 10, 167. [DOI: https://dx.doi.org/10.3390/risks10090167]
34. Chauhan, R. Impact of social media usage on job performance and employee retention: Role of knowledge sharing and organizational commitment. Glob. Bus. Organ. Excell.; 2023; 43, pp. 19-34. [DOI: https://dx.doi.org/10.1002/joe.22185]
35. Shang, Y.; Pan, Y.; Richards, M. Facilitating or inhibiting? The role of enterprise social media use in job performance. Inf. Technol. People; 2023; 36, pp. 2338-2360. [DOI: https://dx.doi.org/10.1108/ITP-04-2021-0268]
36. Liu, X.; Zheng, B.; Liu, H. Understanding the social media interactivity paradox: The effects of social media interactivity on communication quality, work interruptions and job performance. Inf. Technol. People; 2022; 35, pp. 1805-1828. [DOI: https://dx.doi.org/10.1108/ITP-12-2020-0845]
37. Zahmat Doost, E.; Zhang, W. The Effect of Social Media Use on Job Performance with Moderating Effects of Cyberloafing and Job Complexity. Inf. Technol. People; 2024; 37, pp. 1775-1801. [DOI: https://dx.doi.org/10.1108/ITP-04-2022-0249]
38. Czerwinski, M.; Horvitz, E.; Wilhite, S. A Diary Study of Task Switching and Interruptions. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; Vienna, Austria, 24–29 April 2004; ACM: New York, NY, USA, 2004; pp. 175-182. [DOI: https://dx.doi.org/10.1145/985692.985715]
39. Newport, C. Deep Work: Rules for Focused Success in a Distracted World; 1st ed. Grand Central Publishing: New York, NY, USA, Boston, MA, USA, 2016.
40. Mark, G.; Gudith, D.; Klocke, U. The Cost of Interrupted Work: More Speed and Stress. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; Florence, Italy, 5–10 April 2008; ACM: New York, NY, USA, 2008; pp. 107-110. [DOI: https://dx.doi.org/10.1145/1357054.1357072]
41. Teigland, R.; Wasko, M. Knowledge transfer in MNCs: Examining how intrinsic motivations and knowledge sourcing impact individual centrality and performance. J. Int. Manag.; 2009; 15, pp. 15-31. [DOI: https://dx.doi.org/10.1016/j.intman.2008.02.001]
42. Jong, D.; Chen, S.-C.; Ruangkanjanases, A.; Chang, Y.-H. The Impact of Social Media Usage on Work Efficiency: The Perspectives of Media Synchronicity and Gratifications. Front. Psychol.; 2021; 12, 693183. [DOI: https://dx.doi.org/10.3389/fpsyg.2021.693183]
43. Scarpi, D. Work and Fun on the Internet: The Effects of Utilitarianism and Hedonism Online. J. Interact. Mark.; 2012; 26, pp. 53-67. [DOI: https://dx.doi.org/10.1016/j.intmar.2011.08.001]
44. Kim, J.N.; Rhee, Y. Strategic thinking about employee communication behavior (ECB) in public relations: Testing the models of megaphoning and scouting effects in Korea. J. Public Relat. Res.; 2011; 23, pp. 243-268. [DOI: https://dx.doi.org/10.1080/1062726X.2011.582204]
45. Raacke, J.; Bonds-Raacke, J. MySpace and Facebook: Applying the Uses and Gratifications Theory to Exploring Friend-Networking Sites. CyberPsychol. Behav.; 2008; 11, pp. 169-174. [DOI: https://dx.doi.org/10.1089/cpb.2007.0056]
46. Sobaih, A.E.E.; Hasanein, A.M.; Abu Elnasr, A.E. Responses to COVID-19 in higher education: Social media usage for sustaining formal academic communication in developing countries. Sustainability; 2020; 12, 6520. [DOI: https://dx.doi.org/10.3390/su12166520]
47. Murire, O.T.; Cilliers, L. Social media adoption among lecturers at a traditional university in East-ern Cape Province of South Africa. S. Afr. J. Inf. Manag.; 2017; 19, pp. 1-6. [DOI: https://dx.doi.org/10.4102/sajim.v19i1.834]
48. Nentwich, M.; König, R. Academia goes Facebook? The potential of social network sites in the scholarly realm. Opening Science: The Evolving Guide on How the Internet Is Changing Research, Collaboration and Scholarly Publishing; Springer: Berlin/Heidelberg, Germany, 2014; pp. 107-124. [DOI: https://dx.doi.org/10.1007/978-3-319-00026-8_7]
49. Ndung’U, J.; Vertinsky, I.; Onyango, J. The Relationship between Social Media Use, Social Media Types, and Job Performance amongst Faculty in Kenya Private Universities. Heliyon; 2023; 9, e22946. [DOI: https://dx.doi.org/10.1016/j.heliyon.2023.e22946]
50. Chawinga, W.D. Taking social media to a university classroom: Teaching and learning using Twitter and blogs. Int. J. Educ. Technol. High. Educ.; 2017; 14, 3. [DOI: https://dx.doi.org/10.1186/s41239-017-0041-6]
51. Ali, B.J.; Anwar, G. A Study of Knowledge Management Alignment with Production Management: A Study of Carpet Manufacture in Kurdistan Region of Iraq. Int. J. Engl. Lit. Soc. Sci.; 2021; 6, pp. 346-360. [DOI: https://dx.doi.org/10.22161/ijels.62.51]
52. Anwar, G.; Abdullah, N.N. Inspiring future entrepreneurs: The effect of experiential learning on the entrepreneurial intention at higher education. Int. J. Engl. Lit. Soc. Sci.; 2021; 6, pp. 183-194. [DOI: https://dx.doi.org/10.22161/ijels.62.26]
53. Abdullah, N.N.; Rahman, M.F.A. The Use of Deliberative Democracy in Public Policy Making Process. Public Policy Adm. Res.; 2015; 5, pp. 221-229. [DOI: https://dx.doi.org/10.2139/ssrn.2769105]
54. Ali, B.J.; Saleh, P.F.; Akoi, S.; Abdulrahman, A.A.; Muhamed, A.S.; Noori, H.N.; Anwar, G. Impact of Service Quality on the Customer Satisfaction: Case Study at Online Meeting Platforms. Int. J. Eng. Bus. Manag.; 2021; 5, pp. 65-77. [DOI: https://dx.doi.org/10.22161/ijebm.5.2.6]
55. Madsen, V.T. Internal Social Media and Internal Communication. Current Trends and Issues in Internal Communication: Theory and Practice; Palgrave Macmillan: Cham, Switzerland, 2021; pp. 57-74. [DOI: https://dx.doi.org/10.1007/978-3-030-78213-9_4]
56. Fang, R.; McAllister, D.J.; Duffy, M.K. Down but not out: Newcomers can compensate for low vertical access with strong horizontal ties and favorable core self-evaluations. Pers. Psychol.; 2017; 70, pp. 517-555. [DOI: https://dx.doi.org/10.1111/peps.12177]
57. Namhata, R.; Patnaik, P. The ‘Verticals’, ‘Horizontals’, and ‘Diagonals’ in Organisational Communication: Developing Models to Mitigate Communication Barriers Through Social Media Applications. Digital Business; Patnaik, S.; Yang, X.-S.; Tavana, M.; Popentiu-Vlădicescu, F.; Qiao, F. Lecture Notes on Data Engineering and Communications Technologies Springer International Publishing: Cham, Switzerland, 2019; Volume 21, pp. 343-373. [DOI: https://dx.doi.org/10.1007/978-3-319-93940-7_14]
58. Ernst, C.-P.H. Hedonic and Utilitarian Motivations of Social Network Site Usage. Factors Driving Social Network Site Usage; Springer Fachmedien Wiesbaden: Wiesbaden, Germany, 2015; pp. 11-28. [DOI: https://dx.doi.org/10.1007/978-3-658-09918-3_2]
59. Tamborini, R.; Grizzard, M.; Bowman, N.D.; Reinecke, L.; Lewis, R.J.; Eden, A. Media Enjoyment as Need Satisfaction: The Contribution of Hedonic and Nonhedonic Needs. J. Commun.; 2011; 61, pp. 1025-1042. [DOI: https://dx.doi.org/10.1111/j.1460-2466.2011.01593.x]
60. Rupeika-Apoga, R.; Solovjova, I. Profiles of SMEs as Borrowers: Case of Latvia. Contemp. Stud. Econ. Financ. Anal.; 2016; 98, pp. 63-76. [DOI: https://dx.doi.org/10.1108/S1569-375920160000098005]
61. Jiang, Y.; Chen, C.C. Integrating Knowledge Activities for Team Innovation: Effects of Transfor-mational Leadership. J. Manag.; 2018; 44, pp. 1819-1847. [DOI: https://dx.doi.org/10.1177/0149206316628641]
62. Falco, A.; Girardi, D.; Marcuzzo, G.; De Carlo, A.; Bartolucci, G.B. Work Stress and Negative Affectivity: A Multi-Method Study. Occup. Med.; 2013; 63, pp. 341-347. [DOI: https://dx.doi.org/10.1093/occmed/kqt054] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23719321]
63. Sun, W.; Chao, M. Exploring the Influence of Excessive Social Media Use on Academic Performance through Media Multitasking and Attention Problems: A Three-Dimension Usage Perspective. Educ. Inf. Technol.; 2024; [DOI: https://dx.doi.org/10.1007/s10639-024-12811-y]
64. Chau, H.T.H.; Bui, H.P.; Dinh, Q.T.H. Impacts of online collaborative learning on students’ intercultural communication apprehension and intercultural communicative competence. Educ. Inf. Technol.; 2023; 29, pp. 7447-7464. [DOI: https://dx.doi.org/10.1007/s10639-023-12100-0]
65. Hajli, N.; Lin, X. Exploring the Security of Information Sharing on Social Networking Sites: The Role of Perceived Control of Information. J. Bus. Ethics; 2016; 133, pp. 111-123. [DOI: https://dx.doi.org/10.1007/s10551-014-2346-x]
66. Sabah, N.M. The impact of social media-based collaborative learning environments on students’ use outcomes in higher education. Int. J. Hum.–Comput. Interact.; 2023; 39, pp. 667-689. [DOI: https://dx.doi.org/10.1080/10447318.2022.2046921]
67. Khawam, A.M.; DiDona, T.; Hernández, B.S. Effectiveness of teamwork in the workplace. Int. J. Sci. Basic Appl. Res. (IJSBAR); 2017; 32, pp. 267-286. Available online: https://www.gssrr.org/index.php/JournalOfBasicAndApplied/article/view/7134 (accessed on 12 July 2024).
68. Schmutz, J.B.; Meier, L.L.; Manser, T. How Effective Is Teamwork Really? The Relationship between Teamwork and Performance in Healthcare Teams: A Systematic Review and Meta-Analysis. BMJ Open; 2019; 9, e028280. [DOI: https://dx.doi.org/10.1136/bmjopen-2018-028280]
69. Song, Q.; Wang, Y.; Chen, Y.; Benitez, J.; Hu, J. Impact of the usage of social media in the workplace on the team and employee performance. Inf. Manag.; 2019; 56, 103160. [DOI: https://dx.doi.org/10.1016/j.im.2019.04.003]
70. Lailiyah, M.; Putra, S.P. Integrating the use of social media for group collaboration in ESP classroom. Engl. Teach. J. J. Engl. Lit. Lang. Educ.; 2022; 10, pp. 60-66. [DOI: https://dx.doi.org/10.25273/etj.v10i1.11716]
71. Thom, J.; Millen, D.; DiMicco, J. Removing gamification from an enterprise SNS. Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work; Washington, DC, USA, 11–15 February 2012; pp. 1067-1070. [DOI: https://dx.doi.org/10.1145/2145204.2145362]
72. Bodhi, R.; Luqman, A.; Hina, M.; Papa, A. Work-Related Social Media Use and Employee-Related Outcomes: A Moderated Mediation Model. Int. J. Emerg. Mark.; 2023; 18, pp. 4948-4967. [DOI: https://dx.doi.org/10.1108/IJOEM-09-2021-1359]
73. Dodokh, A.M.I. Examining the Impact of Personal Social Media Use at Work on Workplace Outcomes. Ph.D. Thesis; University of Plymouth: Plymouth, UK, 2022; [DOI: https://dx.doi.org/10.24382/659]
74. ÅžAHÄ, A.; Firat, A. The Effect of Social Media on Hedonic Consumption of Individuals. Turk. J. Mark.; 2018; 3, pp. 1-16.
75. Weber, M.S.; Shi, W. Enterprise social media. The International Encyclopedia of Organizational Communication; Scott, C.; Lewis, L. Wiley Blackwell: Hoboken, NJ, USA, 2016; Volume 4, pp. 1-9. [DOI: https://dx.doi.org/10.1002/9781118955567.wbieoc072]
76. Dhar, S.; Bose, I. Corporate users’ attachment to social networking sites: Examining the role of so-cial capital and perceived benefits. Inf. Syst. Front.; 2023; 25, pp. 1197-1217. [DOI: https://dx.doi.org/10.1007/s10796-022-10289-y]
77. Kadar, N.S.A.; Yadri, W.S.W.; Faris, N.D.; Johari, M.D.M.; Nurgeldiyevna, K.N.; Rahmat, N.H. Exploring Online Group Work Through The Social Cognitive Theory. Int. J. Acad. Res. Bus. Soc. Sci.; 2023; 13, pp. 1684-1698. [DOI: https://dx.doi.org/10.6007/IJARBSS/v13-i9/17744]
78. Tourish, D.; Hargie, C. Internal communication: Key steps in evaluating and improving perfor-mance. Corp. Commun. Int. J.; 1996; 1, pp. 11-16. [DOI: https://dx.doi.org/10.1108/eb059593]
79. Rupeika-Apoga, R.; Saksonova, S. SMEs’ Alternative Financing: The Case of Latvia. Eur. Res. Stud. J. XXI; 2018; pp. 43-52. [DOI: https://dx.doi.org/10.35808/ersj/1042]
80. Meng, J.; Berger, B.K. Measuring return on investment (ROI) of organizations’ internal commu-nication efforts. J. Commun. Manag.; 2012; 16, pp. 332-354. [DOI: https://dx.doi.org/10.1108/13632541211278987]
81. Jacobs, M.A.; Yu, W.; Chavez, R. The effect of internal communication and employee satisfaction on supply chain integration. Int. J. Prod. Econ.; 2016; 171, pp. 60-70. [DOI: https://dx.doi.org/10.1016/j.ijpe.2015.10.015]
82. Qin, Y.S.; Men, L.R. Exploring the Impact of Internal Communication on Employee Psychological Well-Being During the COVID-19 Pandemic: The Mediating Role of Employee Organizational Trust. Int. J. Bus. Commun.; 2023; 60, pp. 1197-1219. [DOI: https://dx.doi.org/10.1177/23294884221081838]
83. Men, L.R.; Yue, C.A. Creating a Positive Emotional Culture: Effect of Internal Communication and Impact on Employee Supportive Behaviors. Public Relat. Rev.; 2019; 45, 101764. [DOI: https://dx.doi.org/10.1016/j.pubrev.2019.03.001]
84. Danso, A.; Poku, K.; Agyapong, A. Mediating Role of Internal Communications in Market Orientation and Performance of Mobile Telecom Firms: Evidence from Ghana. Cogent Bus. Manag.; 2017; 4, 1403713. [DOI: https://dx.doi.org/10.1080/23311975.2017.1403713]
85. Octavia, D.H.; Budiono, B. Pengaruh Teamwork terhadap Kinerja Karyawan melalui Job Satisfac-tion. J. Ilmu Manaj.; 2021; 9, pp. 954-965. [DOI: https://dx.doi.org/10.26740/jim.v9n3.p954-965]
86. Awalia, A.R.; Fania, D.; Setyaningrum, D.U. Pengaruh Teamwork Terhadap Kinerja Karyawan (Study Kasus Pada Pt. XYZ Jatinangor). E-J. Equilib. Manaj.; 2020; 6, pp. 12-19.
87. McEwan, D.; Ruissen, G.R.; Eys, M.A.; Zumbo, B.D.; Beauchamp, M.R. Beauchamp. The Effectiveness of Teamwork Training on Teamwork Behaviors and Team Performance: A Systematic Review and Meta-Analysis of Controlled Interventions. PLoS ONE; 2017; 12, e0169604. [DOI: https://dx.doi.org/10.1371/journal.pone.0169604]
88. Shin, S.J.; Yuan, F.; Zhou, J. When Perceived Innovation Job Requirement Increases Employee Innovative Behavior: A Sensemaking Perspective. J. Organ. Behav.; 2017; 38, pp. 68-86. [DOI: https://dx.doi.org/10.1002/job.2111]
89. Söderlund, M.; Öhman, P. Internal communication and innovation capability. J. Bus. Commun.; 2016; 53, pp. 207-224.
90. Soares, M.E.; Mosquera, P.; Cid, M. Antecedents of innovative behaviour: Knowledge sharing, open innovation climate and internal communication. Int. J. Innov. Learn.; 2021; 30, pp. 241-257. [DOI: https://dx.doi.org/10.1504/IJIL.2021.117223]
91. Amalina, H.; Pusparini, E.S. Influence of Social Media Communication on Employee Innovative Work Behavior: Mediating Role of Work Engagement. 7th Global Conference on Business, Management, and Entrepreneurship (GCBME 2022); Atlantis Press: Amsterdam, The Netherlands, 2023; pp. 1518-1526. [DOI: https://dx.doi.org/10.2991/978-94-6463-234-7_159]
92. Zaidi, S.H.; Rupeika-Apoga, R. Liquidity Synchronization, Its Determinants and Outcomes under Economic Growth Volatility: Evidence from Emerging Asian Economies. Risks; 2021; 9, 43. [DOI: https://dx.doi.org/10.3390/risks9020043]
93. Kuo, H.; Lee, T.; Fang, C. Free Trade and Economic Growth. Aust. Econ. Pap.; 2014; 53, pp. 69-76. [DOI: https://dx.doi.org/10.1111/1467-8454.12021]
94. Baralou, E.; Dionysiou, D.D. Routine dynamics in virtual teams: The role of technological artifacts. Inf. Technol. People; 2022; 35, pp. 1980-2001. [DOI: https://dx.doi.org/10.1108/ITP-03-2020-0109]
95. West, M.A. Sparkling fountains or stagnant ponds: An integrative model of creativity and innova-tion implementation in work groups. Appl. Psychol.; 2002; 51, pp. 355-387. [DOI: https://dx.doi.org/10.1111/1464-0597.00951]
96. Anderson, N.R.; West, M.A. Measuring climate for work group innovation: Development and validation of the team climate inventory. J. Organ. Behav.; 1998; 19, pp. 235-258. [DOI: https://dx.doi.org/10.1002/(SICI)1099-1379(199805)19:3<235::AID-JOB837>3.0.CO;2-C]
97. Johari, A.; Abdul Wahat, N.W.; Zaremohzzabieh, Z. Innovative work behavior among teachers in Malaysia: The effects of teamwork, principal support, and humor. Asian J. Univ. Educ. (AJUE); 2021; 7, pp. 72-84. [DOI: https://dx.doi.org/10.24191/ajue.v17i2.13387]
98. Ariefahnoor, D.; Nugroho, R. The effect of innovation, organizational learning, and teamwork on managerial and organizational performance of rural banking in the province of south kalimantan. J. Mod. Proj. Manag.; 2022; 10, pp. 134-143. [DOI: https://dx.doi.org/10.1080/10615800801958637]
99. Grima, S.; Rupeika-Apoga, R.; Kizilkaya, M.; Romānova, I.; Gonzi, R.D.; Jakovljevic, M. A Proactive Approach to Identify the Exposure Risk to COVID-19: Validation of the Pandemic Risk Exposure Measurement (PREM) Model Using Real-World Data. Risk Manag. Healthc. Policy; 2021; 14, pp. 4775-4787. [DOI: https://dx.doi.org/10.2147/RMHP.S341500]
100. Hansen, M.T. Knowledge networks: Explaining effective knowledge sharing in multiunit compa-nies. Organ. Sci.; 2002; 13, pp. 232-248. [DOI: https://dx.doi.org/10.1287/orsc.13.3.232.2771]
101. Phulpoto, N.H. Teamwork and its impact on employee performance mediated by job satisfaction: A comprehensive study of services sector of Pakistan. J. Innov. Sustain. RISUS; 2023; 14, pp. 21-31. [DOI: https://dx.doi.org/10.23925/2179-3565.2023v14i3p21-31]
102. Katzenbach, J.R.; Smith, D.K. The Wisdom of Teams: Creating the High-Performance Organization; Harvard Business Review Press: Cambridge, MA, USA, 1993.
103. Miller, K.; Barbour, J. Organizational Communication: Approaches and Processes; Cengage Learning: Belmont, CA, USA, 2019.
104. Mercader, V.; Galván-Vela, E.; Ravina-Ripoll, R.; Popescu, C.R.G. A focus on ethical value under the vision of leadership, teamwork, effective communication and productivity. J. Risk Financ. Manag.; 2021; 14, 522. [DOI: https://dx.doi.org/10.3390/jrfm14110522]
105. Sari, M.; Indrajaya, T.; Nurminingsih,. The influence of internal communication and teamwork on employee performance in the microwave dismantle project division pt. panca karsa sejahtera bekasi city. J. Humanit. Soc. Sci. Bus. (JHSSB); 2023; 3, pp. 1-13. [DOI: https://dx.doi.org/10.55047/jhssb.v3i1.770]
106. Teigland, R.; Wasko, M.M. Integrating knowledge through information trading: Examining the re-lationship between boundary spanning communication and individual performance. Decis. Sci.; 2003; 34, pp. 261-286. [DOI: https://dx.doi.org/10.1111/1540-5915.02341]
107. Bakker, A.B.; Van Emmerik, H.; Van Riet, P. How job demands, resources, and burnout predict objective performance: A constructive replication. Anxiety Stress Coping; 2008; 21, pp. 309-324. [DOI: https://dx.doi.org/10.1080/10615800801958637] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18612856]
108. Chiu, C.M.; Hsu, M.H.; Wang, E.T. Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decis. Support Syst.; 2006; 42, pp. 1872-1888. [DOI: https://dx.doi.org/10.1016/j.dss.2006.04.001]
109. Van den Hooff, B.; Huysman, M. Managing knowledge sharing: Emergent and engineering ap-proaches. Inf. Manag.; 2009; 46, pp. 1-8. [DOI: https://dx.doi.org/10.1016/j.im.2008.09.002]
110. Bock, G.W.; Zmud, R.W.; Kim, Y.G.; Lee, J.N. Behavioral intention formation in knowledge shar-ing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Q.; 2005; pp. 87-111. Available online: https://www.jstor.org/stable/25148669 (accessed on 9 May 2024). [DOI: https://dx.doi.org/10.2307/25148669]
111. Nevo, S.; Nevo, D. Re-invention of applicable innovations: The case of virtual worlds. Proceedings of the 44th Hawaii International Conference on System Sciences; Kauai, HI, USA, 4–7 January 2011; [DOI: https://dx.doi.org/10.1109/HICSS.2011.355]
112. Agarwal, R.; Karahanna, E. Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Q.; 2000; 24, pp. 665-694. [DOI: https://dx.doi.org/10.2307/3250951]
113. Lee, S.; Park, J.G.; Lee, J. Explaining knowledge sharing with social capital theory in information systems development projects. Ind. Manag. Data Syst.; 2015; 115, pp. 883-900. [DOI: https://dx.doi.org/10.1108/IMDS-01-2015-0017]
114. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Convention-al criteria versus new alternatives. Struct. Equ. Model.; 1999; 6, pp. 1-55. [DOI: https://dx.doi.org/10.1080/10705519909540118]
115. Podsakoff, P.M.; Organ, D.W. Self-reports in organizational research: Problems and prospects. J. Manag.; 1986; 12, pp. 531-544. [DOI: https://dx.doi.org/10.1177/014920638601200408]
116. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common method biases in behavior-al research: A critical review of the literature and recommended remedies. J. Appl. Psychol.; 2003; 88, 879. [DOI: https://dx.doi.org/10.1037/0021-9010.88.5.879]
117. Bentler, P.M. EQS Structural Equations Program Manual; Multivariate Software: Los Angeles, CA, USA, 1995.
118. Hair, J.F. Multivariate Data Analysis with Readings; 4th ed. Prentice Hall: Englewood Cliffs, NJ, USA, 1995.
119. Cronbach, L.J. Coefficient alpha and the internal structure of tests. Psychometrika; 1951; 16, pp. 297-334. [DOI: https://dx.doi.org/10.1007/BF02310555]
120. Fornell, C.; David, F.L. Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. J. Mark. Res.; 1981; 18, pp. 382-388. [DOI: https://dx.doi.org/10.1177/002224378101800313]
121. Hayes, A.F. Process: A Versatile Computational Tool for Observed Variable Mediation, Moderation, and Conditional Process Modeling [White Paper]. 2012; Available online: http://www.afhayes.com/public/process2012.pdf (accessed on 11 August 2024).
122. Collier, J. Applied Structural Equation Modeling Using AMOS: Basic to Advanced Techniques; Routledge: London, UK, 2020; [DOI: https://dx.doi.org/10.4324/9781003018414]
123. Seaver, F.A.; Cummings, T.G.; Molloy, E.S. Improving Productivity and the Quality of Work Life; Praeger: Oxford, UK, 1977.
124. Kock, N.; Moqbel, M. Social Networking Site Use, Positive Emotions, and Job Performance. J. Comput. Inf. Syst.; 2021; 61, pp. 163-173. [DOI: https://dx.doi.org/10.1080/08874417.2019.1571457]
125. Cao, X.; Guo, X.; Vogel, D.; Zhang, X. Exploring the Influence of Social Media on Employee Work Performance. Edited by Professor Pan Wang, Professor Sohail Chaudhry, Professor Ling Li. Internet Res.; 2016; 26, pp. 529-545. [DOI: https://dx.doi.org/10.1108/IntR-11-2014-0299]
126. Moqbel, M.; Nevo, S.; Kock, N. Organizational Members’ Use of Social Networking Sites and Job Performance: An Exploratory Study. Inf. Technol. People; 2013; 26, pp. 240-264. [DOI: https://dx.doi.org/10.1108/ITP-10-2012-0110]
127. Fusi, F.; Feeney, M.K. Social Media in the Workplace: Information Exchange, Productivity, or Waste?. Am. Rev. Public Adm.; 2018; 48, pp. 395-412. [DOI: https://dx.doi.org/10.1177/0275074016675722]
128. Leonardi, P.M.; Huysman, M.; Steinfield, C. Enterprise Social Media: Definition, History, and Prospects for the Study of Social Technologies in Organizations. J. Comput.-Mediat. Commun.; 2013; 19, pp. 1-19. [DOI: https://dx.doi.org/10.1111/jcc4.12029]
129. Treem, J.W.; Leonardi, P.M. Social Media Use in Organizations: Exploring the Affordances of Visibility, Editability, Persistence, and Association. SSRN Electron. J.; 2012; [DOI: https://dx.doi.org/10.2139/ssrn.2129853]
130. Verheyden, M. Social Media and the Promise of Excellence in Internal Communication. J. Organ. Ethnogr.; 2017; 6, pp. 11-25. [DOI: https://dx.doi.org/10.1108/JOE-09-2016-0020]
131. Çetinkaya, L.; Sütçü, S.S. The Effects of Facebook and WhatsApp on Success in English Vocabulary Instruction. J. Comput. Assist. Learn.; 2018; 34, pp. 504-514. [DOI: https://dx.doi.org/10.1111/jcal.12255]
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Sohbaty, Zohour 1 ; Jain, Ruchika 2
; Shafi, Iqra 3 ; Rupeika-Apoga, Ramona 4
1 School of Management Studies, Punjabi University, Patiala 147002, Punjab, India;
2 Gobindgarh Public College, Khanna 147301, Punjab, India;
3 SKUAST-K, Kashmir University, Srinagar 191202, Jammu & Kashmir, India
4 Faculty of Economics and Social Sciences, University of Latvia, LV-1586 Riga, Latvia; Women Researchers Council (WRC), Azerbaijan State University of Economics (UNEC), Baku AZ1001, Azerbaijan