Abstract
Purpose
The primary objective of this investigation was to explore how employees’ utilization of social media for work-related purposes impacts their service innovation behavior, both directly and through the intermediary mechanisms of knowledge management and employees’ risk-taking.
Design/methodology/approach
In developing its conceptual framework, this study has drawn upon the stimulus-organism-response (SOR) theory. To test its hypotheses, this study has surveyed 241 financial analysts from ten Iranian financial companies and has employed variance-based structural equation modeling (specifically, PLS-SEM) with the assistance of “WarpPLS 8.0 software.”
Findings
The findings revealed that employees’ work-related use of social media positively influences their service innovation behavior using knowledge management, encompassing knowledge sharing and acquisition capability as well as employee risk-taking. However, this influence is not directly significant.
Originality/value
To the best of our knowledge, this study marks the first instance in which the effect of work-related use of social media on employee service innovation behavior directly and through the mediating roles of knowledge management and risk-taking has been investigated through the lens of the SOR paradigm, especially in the financial sector.
Keywords
Citation
Olfat, M. (2024), "The influence of employees’ work-related use of social media on their service innovation behavior: the SOR paradigm", Digital Transformation and Society, Vol. 3 No. 4, pp. 359-375. https://doi.org/10.1108/DTS-11-2023-0099
Publisher
:Emerald Publishing Limited
Copyright © 2024, Mohammad Olfat
License
Published in Digital Transformation and Society. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Social media, as defined by Wikipedia, facilitates interactive communication among organizations, communities and individuals (Leftheriotis & Giannakos, 2014), reshaping the digital landscape. It’s increasingly relied upon for entertainment, information consumption and leisure (Leftheriotis & Giannakos, 2014), revolutionizing social interactions. Originally personal, platforms like Facebook and Twitter are now used for workplace connectivity (Schmidt, Lelchook, & Martin, 2016), sparking debate among scholars (Olfat, Shokouhyar, Ahmadi, & Ghaderi, 2023). Research indicates positive outcomes like stress relief and enhanced organizational commitment from employee social media use (Olfat, Tabarsa, Ahmadi, & Shokouhyar, 2019), yet it also highlights negative effects such as cyber-loafing and cyberbullying (Giumetti & Kowalski, 2022). Despite concerns, recognizing its potential benefits, managers should focus on managing social media use rather than strict control (Olfat, 2023). This study aims to explore the untapped positive impacts of social media use in the workplace, providing managers with confidence in leveraging it for business prosperity.
In today’s dynamic business landscape, competitive advantage stems from organizational innovation, which is fueled by employees’ innovative behavior (García-Granero, LlopisFernández-Mesa, & Alegre, 2015). Supporting employees to take risks at work fosters innovation (Neves & Eisenberger, 2014), particularly when they possess in-depth knowledge of the problems they’re addressing (García-Granero et al., 2015). Knowledge management is instrumental in promoting innovation by empowering employees with information and proficiency (Neves & Eisenberger, 2014). Social media’s potential impact on enhancing employees’ risk-taking and innovation through facilitating knowledge management warrants exploration. Previous studies have not extensively investigated how employees’ work-related social media usage influences their service innovation behavior, either directly or through mediating factors like knowledge management and risk-taking (Appendix 1). To address this gap, this study proposes a conceptual model based on the stimulus-organism-response (SOR) theory. The conceptual model aims to demonstrate that by endorsing work-related social media usage and supporting knowledge management, managers can stimulate employees’ innovation behavior by encouraging risk-taking. The study seeks to answer the following questions to delve deeper into its objectives:
Does employees’ work-related use of social media directly improve their service innovation behavior?
Does employees’ work-related use of social media improve their service innovation behavior through the mediating roles of knowledge management and risk-taking?
According to Forsgren and Byström (2018), employees utilize two types of social media in the workplace: public platforms, accessible to both internal and external audiences and enterprise social media (ESM), limited to internal use by organizational policies. This study focuses on public platforms for easier data collection, given their widespread accessibility. This study aims to make three key contributions to management studies. Firstly, it seeks to challenge the notion that social media is merely a distraction in the workplace, asserting its potential as a valuable tool for business prosperity. Secondly, it aims to highlight the supportive role of social media in knowledge management and its positive impact on employees’ risk-taking and service innovation behavior. Lastly, this study is among the few to utilize the SOR theory to explore the beneficial effects of social media in the workplace.
2. Research background and hypotheses development
2.1 Stimuli-organism-response (SOR) theory
The SOR framework, as outlined by Song, Yao, and Wen (2021), consists of three main elements: “stimulus,” “organism” and “response.” The stimulus, such as a person, event, product or online platform like social media, represents what an individual interacts with. The organism component explores an individual’s cognitive and emotional disposition toward the stimulus. Lastly, the response component encompasses the attitudes or behaviors influenced by the organism, covering both cognitive and emotional aspects (Soroya, Farooq, Mahmood, Isoaho, & Zara, 2021). Within this framework, a causal relationship exists among the stimulus, organism and response. The stimulus serves as a predictor capable of influencing or reinforcing the organism, which then shapes a specific response or observable behavior. The organism component includes cognitive and affective aspects, with cognitive conditions involving mental information processing, while affective states encompass emotional responses like pleasure, displeasure, arousal or dominance, intertwined with cognitions (Alsaggaf & Althonayan, 2018).
2.2 Social media and its capabilities
Presently, social media is predominantly accessed via mobile devices, with Facebook users checking their profiles multiple times daily and many managers recognizing platforms like X, Instagram and YouTube as critical for business success (Chen, Zheng, & Liu, 2024). Social platforms, as defined by Zolkepli and Kamarulzaman (2015), are internet-based applications rooted in Web 2.0 principles, enabling the creation and exchange of user-generated content. Through social media, individuals can easily share digital content, such as pictures and videos, online, fostering unrestricted communication (Khobzi & Teimourpour, 2014). Social media encompasses various classifications, including collaborative projects, blogs, social networking sites (SNSs) and virtual worlds (Khobzi & Teimourpour, 2014). Special attributes like openness, interaction and open-ended feedback enable large groups, particularly in the workplace, to share ideas, skills and knowledge effortlessly (Kwahk & Park, 2016). Organizations increasingly utilize social media for knowledge management, facilitating the sharing of both explicit and tacit knowledge (Xie, Chiu, & Ho, 2023; Kwahk & Park, 2016). Social media’s widespread adoption and rapid user engagement surpass other online tools in terms of interaction and personalization (Zolkepli & Kamarulzaman, 2015). Researchers have identified seven purposes for social media, including identity, conversations, sharing, presence, relationships, reputations and teams, each allowing individuals to disclose and evaluate specific aspects of social media (Zolkepli & Kamarulzaman, 2015).
2.3 Work-related use of social media
Previous research has demonstrated that social media significantly enhances social interactions, knowledge sharing and acquisition within communities (Hu, Gu, Liu, & Huang, 2017). Its integration into the workplace has transformed business operations, challenging the perception of it being merely a time-wasting tool (Olfat et al., 2023; Olfat, 2023). Employee motivations for using these platforms vary, with implications for organizations (Zhang, Ma, Xu, & Xu, 2019; Olfat et al., 2023). Ali-Hassan, Nevo, and Wade (2015) categorized these motivations into hedonic, social and work-related, encompassing relaxation, social interaction and professional activities, respectively. Surprisingly, using social media for leisure during work hours has been found to help employees relax and alleviate workplace stress (Charoensukmongkol, 2014). Social motives include interacting with colleagues, while work-related motivations involve building connections, partnerships, knowledge sharing and job-related tasks (Olfat, Shokouhyar, Ahmadi, Tabarsa, & Sedaghat, 2020). Our focus is specifically on employees’ work-related use of social media, highlighting positive outcomes such as enhanced performance (Chen, Ou, & Davison, 2022) and challenging restrictive regulations by emphasizing its role in knowledge management, organizational commitment and job satisfaction (Zhang et al., 2019). In the workplace, social media use for work-related purposes serves as an external stimulus influencing a range of emotions, from joy to anxiety (Chen & Wei, 2019). From the perspective of the SOR theory, social media acts as an external stimulus capable of shaping, empowering or influencing employees’ internal states.
2.4 Employees’ knowledge sharing
In today’s business landscape, creativity, innovation and knowledge are widely recognized as crucial competitive advantages that significantly contribute to enterprise existence and efficiency (Sigala & Chalkiti, 2015). Knowledge sharing has long been viewed as a critical element in gaining a competitive edge for organizations (Grant, 2016). It occurs when individuals aim to deliver, acquire and communicate knowledge (Bilgihan, Barreda, Okumus, & Nusair, 2016). Ma and Chan (2014) define knowledge sharing as “the communication of knowledge from a source in such a way that it is learned and applied by the recipient” (Bilgihan et al., 2016). Lin (2007) describes knowledge sharing as “a social interaction culture, involving the exchange of employee knowledge, experiences, and skills throughout the department or organization.” While there is ongoing debate regarding the definition and functions of social media, literature extensively confirms that social media software comprises various emerging tools and infrastructures (such as Wikis, blogs, etc.) where individuals can share information, collaborate and form networks of communities (Zhang, Wang, & Chen, 2023). Community-driven and information-oriented social media tools hold immense potential for organizations to facilitate information and knowledge exchange among communities (Grant, 2016). Social media has notably transformed corporations’ knowledge systems, enabling them to expand and efficiently manage knowledge management activities beyond organizational boundaries in both formal and informal ways (e.g. via social networks) (Sigala & Chalkiti, 2015). From the perspective of the SOR theory, knowledge acquisition serves as an external stimulus for employees. Based on the aforementioned logic and to achieve its main objective, this study will evaluate the following hypothesis (Figure 1):
Work-related use of social media has a positive effect on employees’ knowledge sharing.
2.5 Employees’ knowledge acquisition capability
In today’s volatile business environments, knowledge management is crucial for the existence and success of organizations (Ozlati, 2015). Knowledge acquisition, a vital stage in knowledge management, is defined as “the process of accessing and absorbing knowledge through direct or indirect contact with knowledge sources” (Liao & Barnes, 2015). Recent research suggests that many individuals use social media for knowledge acquisition and social connections (Hu et al., 2017). Consequently, social media within organizations can serve as a tool for gaining knowledge and information, thereby enhancing knowledge acquisition capabilities. From the perspective of the SOR theory, knowledge acquisition acts as an external stimulus for employees. Building on this logic to achieve its main objective, this study will evaluate the following hypothesis (Figure 1):
Work-related use of social media positively affects employees’ knowledge acquisition capability.
Improving knowledge sharing within an organization enhances individuals’ ability to acquire knowledge. Analogously, like a generous father who provides more financial support to his children compared to a miser father, organizations with a culture of generous knowledge sharing enable employees to gain more knowledge. Knowledge sharing within an organization reflects the generosity and willingness of individuals to share knowledge. Therefore, the third hypothesis of this research posits that (Figure 1):
Employees’ knowledge sharing has a positive effect on knowledge acquisition capability.
2.6 Employees’ risk-taking
Risk entails uncertainty regarding the outcome of a decision or trouble (Charbaji & Jannoun, 2004). According to Liles (1981), risk is the likelihood of an undesired consequence resulting from a course of action (Charbaji & Jannoun, 2004). Drawing on social learning theory, risk-taking behavior is shaped by inherited traits, influence from significant others and situational factors (Charbaji & Jannoun, 2004). Employee risk-taking manifests as a willingness to navigate ambiguity by exploring new ideas, advocating unpopular opinions or tackling complex problems without clear solutions, aiming to increase the likelihood of success (Neves & Eisenberger, 2014). Dispositional factors like self-confidence, perceived competence and propensity for ventures influence risk-taking behavior in empirical settings (Neves & Eisenberger, 2014). Perceived organizational support (POS) has been shown to enhance employees' willingness to take risks (Neves & Eisenberger, 2014). Moreover, the use of social media in the workplace fosters POS by facilitating communication among employees (Schmidt et al., 2016), thereby potentially improving employees’ risk-taking behavior. From the perspective of the SOR theory, employees' work-related use of social media serves as a stimulus that empowers their ability to take risks in their work. Therefore, the fourth hypothesis of this research is as follows:
Work-related social media has a positive effect on employees’ risk-taking behavior.
While no prior studies have explored how knowledge management, including knowledge sharing and acquisition, influences employees’ risk-taking behavior, this study contends that such a relationship exists within organizations, based on the SOR theory. The rationale behind this assertion lies in the fact that information and knowledge obtained by employees, whether from within or outside the organization, provide clarity on the situation at hand. This knowledge supports employees in fulfilling their responsibilities, thereby enabling them to take more risks in their tasks. It is important to note that risk-taking behavior, as previously discussed, stems from employees’ attitudes, which have internal origins (i.e. organism). External knowledge serves as a factor shaping individuals’ attitudes. Therefore, the fifth and sixth hypotheses of this research are as follows (Figure 1):
Employees’ knowledge sharing has a positive effect on employees’ risk-taking.
Employees’ knowledge acquisition has a positive effect on employees’ risk-taking.
2.7 Employees’ service innovation
In marketing research, it is widely acknowledged that innovation is essential for organizational success in dynamic environments (Kao, Pai, Lin, & Zhong, 2015). Innovative behavior involves creating and implementing new ideas to improve task performance (Dedahanov, Rhee, & Yoon, 2017). Service industries, like financial firms, rely on employees’ ability to generate innovative ideas to enhance customer satisfaction and loyalty (Dhar, 2016; Wu, Chen, & Wang, 2023). Employees' service innovation behavior, driven by knowledge sharing and acquisition within the organization, leads to customer-centric innovations. This is because innovation thrives on individuals' creativity, fueled by deep domain knowledge (Sigala & Chalkiti, 2015). Therefore, the seventh and eighth hypotheses of this study posit (Figure 1):
Employees’ knowledge sharing has a positive effect on their service innovation behavior.
Employees’ knowledge acquisition capability has a positive effect on their service innovation behavior.
In a competitive business environment, retaining customer loyalty is crucial. Firms prioritize improving employee service innovation behavior to enhance firm performance and benefit clients (Garg & Dhar, 2017). While previous studies have linked employees' risk-taking to service innovation behavior, quantitative research from the SOR theory perspective is lacking. This research aims to address this gap through a research hypothesis (Figure 1).
Employees’ risk-taking behavior has a positive effect on their service innovation behavior.
This study investigates the direct impact of workplace social media use on employees' service innovation behavior. Drawing from previous research indicating the positive influence of knowledge management on innovation behavior (Chen & Huang, 2009), the study hypothesizes that social media, acting as a knowledge management tool, positively affects service innovation behavior. However, the study acknowledges the need for further exploration and formulates this as the tenth hypothesis (Figure 1).
Employees’ work-related use of social media directly and positively affects their service innovation behavior.
Figure 1 shows the proposed model of this study which is supported by a well-formulated theory, namely the SOR model.
3. Research method
3.1 Measurement model development
The questionnaire of this research consists of 20 items, of which three were related to the evaluation of the use of social media at work (Olfat et al., 2023), four were related to knowledge sharing based on Ramadan, Dahiyat, Bontis, and Al-dalahmeh (2017), three were related to knowledge acquisition capability based on Kim and Lee (2010), four were related to employees’ risk-taking based on Neves and Eisenberger (2014) and six were related to employees’ service innovation behavior based on Wu et al. (2023) and Hoang, Luu, Du, and Nguyen (2023). All the items are exhibited inAppendix 2. The questions were answered on a Likert-type scale ranging from “1 –strongly disagree” to “5 –strongly agree.”
3.2 Data collection
The online questionnaire for this survey has been sent to 313 financial analysts and consultants from ten financial firms in Iran. Around 283 responses were collected, of which 241 were valid. The reason for choosing the financial industry as a research community is the fact that financial analysts are at high risk due to the nature of their jobs. They have to be at a high level of risk to succeed in buying and selling stocks in the capital market. This research tries to examine how the knowledge shared and gained through social media affects financial analysts’ risk-taking behavior. In addition, since financial analysts’ occupations are kinds of services, the opportunity was provided to measure the variable employees’ service innovation behavior.
4. Data analysis
4.1 Measurement model evaluation
In preparation for hypothesis testing, confirming the precision of our measurement model involves conducting confirmatory factor analysis (CFA), a statistical technique widely used in fields like psychometrics and social sciences. The CFA validates the measurement structure of observed variables by assessing their representation of latent constructs hypothesized by the researcher (Kock, 2015).
Internal consistency: Internal consistency, evaluated through Cronbach’s α (CA) and composite reliability (CR), measures how well items in a test gauge the same underlying construct. Both CA and CR values above 0.7 or 0.8 are typically deemed acceptable for research purposes (Olfat et al., 2019). This study demonstrates good internal consistency based on CA and CR values, as shown in Table 2.
Factor loadings: Factor loadings, which indicate the strength of the relationship between observed variables and latent factors, are crucial for confirming the validity of a measurement model. Factor loadings above 0.5 and significant at a confidence level of more than 99% ensure the reliability of the measurement model. As indicated in Table 1, all items exhibit significant factor loadings.
Convergent validity: Average variance extracted (AVE) assesses the variance captured by the latent construct compared to measurement error. AVE values above 0.50 suggest acceptable convergent validity, indicating that a larger proportion of the variance is due to the latent variable rather than measurement error (Fornell & Larcker, 1981). Table 2 shows that the AVEs for all latent variables exceed 0.5.
Discriminant validity: Discriminant validity ensures that each construct measures a unique aspect, preventing high correlations between constructs. Comparisons of the square root of the AVE with correlations between constructs help assess discriminant validity. The Fornell–Larcker criterion is a useful tool for this evaluation (Fornell & Larcker, 1981). Table 3 demonstrates that this study meets the criteria for discriminant validity.
4.2 Variance inflation factor (VIF)
The variance inflation factor (VIF) is a metric used in regression analysis to detect multicollinearity among independent variables. A high VIF indicates that a predictor variable can be predicted linearly from others, suggesting redundancy in the model. A common threshold for problematic multicollinearity is a VIF exceeding 5 (Kock, 2015), although this can vary depending on the context. As depicted in Table 2, this study is in a good state in terms of VIF.
4.3 Common method bias (CMB)
Common method bias (CMB) arises from systematic variance in survey responses attributed to the measurement method rather than the constructs under investigation. It often results from respondents consistently providing similar answers due to survey format or context, potentially compromising research validity (Ahmad et al., 2020). Researchers employ techniques like using different measurement methods or statistical controls to mitigate its impact. In this study, SPSS 27.0 software was used to address CMB through Harman’s single factor test, which involves factor analysis on all measured variables. The test results, with four components having eigenvalues higher than 1.0 and the first component explaining 38.25% of the total variance (below the 50% threshold), indicate a small presence of CMB (Ahmad, Ahmad, Islam, & Kaleem, 2020; Olfat, 2023).
4.4 Results
Table 4 and Appendix 3 depict the outcomes of structural equation modeling, revealing causal connections in various hypotheses. The standardized partial regression coefficients (β) in Table 4 demonstrate how latent variables impact each other. For example, the β coefficient for social media use at work influencing knowledge sharing is 0.565, indicating a 56.5% increase in knowledge sharing for a 100% rise in social media use. The p-values confirm the statistical significance of these coefficients. Notable findings include the positive and significant effect of social media at work on knowledge sharing (p < 0.001, β = 0.565) and its positive impact on knowledge acquisition capability (p < 0.001, β = 0.376). Additionally, knowledge sharing positively influences knowledge acquisition capability (p < 0.001, β = 0.258). Social media use at work positively affects employees’ risk-taking (p < 0.001, β = 0.376), and knowledge sharing has a positive effect on employees’ risk-taking (p < 0.001, β = 0.415). Knowledge acquisition capability also positively influences employees’ risk-taking (p = 0.007, β = 0.097). Both knowledge sharing and knowledge acquisition contribute positively to employees’ service innovation behavior (p < 0.001, β = 0.485 and 0.356, respectively). Furthermore, employees’ risk-taking significantly and positively influences service innovation behavior (p = 0.003, β = 0.317). Importantly, social media use at work does not directly influence employees’ service innovation behavior.
4.5 Stone–Geisser (Q2) test
The Q2 test, also known as the Stone–Geisser’s test, is a measure used in partial least squares structural equation modeling (PLS-SEM). It assesses the predictive relevance of a model by comparing the predicted values to the ones obtained by using only the mean of the dependent variable. Essentially, it helps evaluate how well the model predicts the observed data compared to a baseline model. A higher Q2 value suggests a better predictive capability of the model. Researchers often interpret Q2 values as follows: Q2 > 0 indicates that the model has predictive relevance, Q2 > 0.25 is considered good, Q2 > 0.50 is considered substantial and Q2 > 0.75 is considered excellent. Researchers use the Q2 test to evaluate the predictive performance of their structural equation models, particularly in fields like marketing and management where PLS-SEM is commonly applied (Kock, 2015) (Table 5).
4.6 Model fit
Table 6 presents several performance metrics for the structural equation model. The average of path coefficients (APC) is 0.335, which is considered meaningful with a high level of significance (error level <0.001). The average R2 is deemed meaningful, with an error level of 0.001, indicating the explanatory power of the model. Average adjusted R2 (AARS) is also meaningful with an error level of less than 0.001, providing an adjusted measure of the model’s explanatory capability. Average variance inflation factor (AVIF) is below the recommended threshold of 5 and even achieves the ideal state of being less than 3.3, as suggested by Kock (2015). This indicates acceptable levels of multicollinearity among variables. The goodness of fit (GOF) index should be greater than 0.4, and its achievement suggests a satisfactory fit of the model to the data. Simpson’s paradox ratio (SPR) is a measure of the discrepancy between the observed and predicted covariance matrices. A value greater than 0.7 is suitable, indicating a promising fit of the data, although the ideal state is 1. These metrics collectively suggest that the structural equation model performs well in explaining the relationships among variables, with good fit and predictive capabilities.
4.7 Sensitivity analysis
In the sensitivity analysis conducted in this study, variations in the dependent variable are determined by changes in the independent variables associated with it. The analysis averages the importance of employees’ work-related use of social media, knowledge sharing and knowledge acquisition capability and employee risk-taking in predicting employees’ service innovation behavior (Sharma, Dwivedi, Arya, & Siddiqui, 2021). According to the results, employees’ risk-taking emerges as the most influential independent variable, with a normalized importance ratio of 100%. This is followed by employees’ work-related use of social media (77.8%), knowledge sharing (63.6%) and knowledge acquisition capability (46.5%). The sensitivity analysis, conducted using SPSS 27.0 software, concludes that employees’ risk-taking is the most influential variable in predicting service innovation behavior due to its highest normalized importance ratio than other independent variables.
5. Discussion and implications
5.1 Key findings
The majority of hypotheses in our study have been supported, except for the tenth hypothesis. Our findings suggest that the work-related use of social media positively influences service innovation behavior among financial analysts, mediated by both knowledge management and employees' propensity for risk-taking. However, we did not find direct evidence of social media use impacting service innovation behavior. We have discussed each result in detail, referencing relevant literature to emphasize both similarities and differences with prior research, aiming to highlight the theoretical contributions of our study.
Employees’ work-related use of social media, knowledge sharing and acquisition capability. The initial findings of our study suggest that social media acts as a facilitative tool for knowledge management within organizations, particularly enhancing knowledge sharing and acquisition capabilities among employees. Knowledge sharing involves the intentional exchange of insights among employees, while knowledge acquisition refers to individuals assimilating shared knowledge from their peers (Ozlati, 2015). These results support the idea that social interaction among employees is essential for individual knowledge management, with social media serving as a platform for such interaction (Hu et al., 2017; Olfat, 2023). Our study demonstrates a positive impact of employees’ work-related social media usage on both knowledge sharing and acquisition capabilities. While consistent with previous research, our findings offer nuanced distinctions. Previous studies mainly focused on employees’ social media usage intensity, regardless of work-related motivation and its impact on knowledge management processes. In contrast, our study specifically examines the influence of employees’ work-related social media usage on individual-level knowledge management dynamics.
Employees’ work-related use of social media and employees’ risk-taking behavior. The influence of employees’ work-related social media usage on increasing their propensity for risk-taking can be attributed to social media’s role in enhancing social interaction within workplace communities, which strengthens perceived organizational support (POS) among employees (Schmidt et al., 2016). Social media use for work-related purposes improves factors like prosocial behavior within the workplace, fostering a sense of peer support among employees (Olfat et al., 2020). This perceived organizational support creates an environment where individuals feel more secure in taking risks (Neves & Eisenberger, 2014). Employees trust both their managers and coworkers to support them in case of mistakes, encouraging them to take risks. Our study quantitatively demonstrates the significance of this effect, marking a pivotal finding. To the best of our knowledge and based on a comprehensive literature review (Appendix 1), this study is the first to systematically assess the relationship between employees’ work-related social media usage and their propensity for risk-taking behavior. This finding not only enhances our understanding of organizational dynamics but also underscores the innovation and novelty of our study model.
Knowledge sharing and knowledge acquisition capability. The research highlights a significant and positive relationship between knowledge sharing and knowledge acquisition capability among employees, attributing it to the reciprocal nature of knowledge exchange facilitated by social interaction within organizations. This finding aligns with the conservation of resources (COR) theory and social exchange theory, which suggest that employees seek and exchange resources within the workplace, enhancing knowledge acquisition capability through reciprocal resource exchange (Ahmad, Nawaz, Ishaq, Khan, & Ashraf, 2023). This study offers a novel perspective by combining COR and social exchange theory to investigate the impact of knowledge sharing on knowledge acquisition, marking a pioneering contribution to the field compared to previous studies primarily focused on individual outcomes of employee behaviors (e.g. Sigala & Chalkiti, 2015).
Knowledge sharing, knowledge acquisition and employees’ risk-taking. The fifth and sixth findings of the study confirm that both knowledge sharing and acquisition positively impact employees' propensity for risk-taking. Based on the SOR theory and the hypothesis development section, shared and acquired knowledge in the workplace plays a crucial role in supporting employees, leading to an increased willingness to take risks. This effect arises from the idea that knowledge management provides employees with support by accessing comprehensive information from coworkers, encouraging risk-taking behavior. In professions like financial analysis, where individuals encounter risky situations regularly, taking calculated risks is essential for success and positive outcomes for firms. Enhanced knowledge about market trends, prices, competitors and other relevant factors enables employees to process information more effectively, empowering them to take more risks. Importantly, this study pioneers the investigation into the roles of knowledge sharing and acquisition in influencing employees’ risk-taking behavior, filling a significant gap in existing research as demonstrated by the comprehensive literature review presented in Appendix 1 and section 2.
Knowledge sharing, knowledge acquisition and employees’ service innovation behavior. The seventh and eighth findings of this research validate the positive effects of both knowledge sharing and acquisition capability on employees’ service innovation behavior. Viewed through the lens of the SOR theory, knowledge management functions as an external factor capable of shaping employees’ internal states (organism), notably their creativity. Creativity serves as a fundamental element in fostering innovative behavior and efficient knowledge management within the organization, facilitated by employees’ work-related use of social media, enables the cultivation of creativity. By sharing and acquiring knowledge effectively, employees are better equipped to generate novel ideas and solutions, thereby driving service innovation within the organization. This perspective underscores the critical role of knowledge management in nurturing a culture of innovation, with social media serving as a conduit for facilitating knowledge exchange and collaboration among employees.
Employees’ risk-taking behavior and employees’ service innovation behavior. The ninth result of this study supports the idea that employees’ risk-taking behavior is positively associated with their service innovation behavior. From the perspective of the SOR model, when employees can take more risks, they are experiencing new methods to solve organizational problems that have not been previously used in the workplace. Thus, employees’ risk-taking, which is an internal state (organism), can improve employees’ service innovation behavior. This result is also a novel finding of this study and has not been studied previously.
Employees’ work-related use of social media and employees’ service innovation behavior. The tenth result highlights a lack of a significant relationship between employees' work-related use of social media and their service innovation behavior. This rejection of the hypothesis is attributed to the understanding that social media platforms primarily serve as tools for knowledge acquisition, information sharing and social interactions. While social media can indirectly influence service innovation behavior through its roles in knowledge management and fostering social interactions, it does not directly impact service innovation behavior. Employees typically use social media for tasks, knowledge acquisition, and social connections in their work environment, which does not inherently lead to changes in service innovation behavior. This finding differs from previous studies focusing on the relationship between social media use and innovation capability, as our study specifically examines observable behavior rather than internal states. Therefore, it is concluded that social media usage alone does not directly influence employees' service innovation behavior.
5.2 Theoretical implications
Our research on social media use, service innovation behavior and the mediating roles of knowledge management and employees’ risk-taking in the financial services sector has significant theoretical implications. By highlighting knowledge management and risk-taking as crucial mediators, we underscore the importance of knowledge processes in fostering innovation. This enhances theoretical frameworks in both knowledge management and innovation research. Additionally, our sector-specific focus offers insights into how social media, knowledge management and risk-taking collectively impact innovation in the financial services industry, acknowledging its unique challenges and opportunities. Furthermore, our findings contribute to organizational learning theories by emphasizing how social media facilitates learning and knowledge dissemination among employees, fostering adaptive behaviors. Lastly, our study highlights social media’s role as a communication tool that drives knowledge sharing and innovation within organizations, contributing to communication theories.
5.3 Practical implications
This study provides practical recommendations for managers based on the findings. Firstly, implementing training programs to enhance employees’ social media skills and awareness can empower them to leverage social platforms effectively, reducing negative consequences like interpersonal conflicts. Secondly, developing and communicating clear social media policies can guide employees toward appropriate and constructive use, addressing potential risks while ensuring alignment with organizational values and industry regulations. Thirdly, cultivating a leadership culture that supports and encourages risk-taking behavior can foster an environment where employees feel empowered to explore innovative ideas without fear of negative consequences. Furthermore, investing in technology that supports collaboration and knowledge sharing, such as enterprise social media (ESM), and integrating social media and collaborative platforms can facilitate cross-functional communication and the exchange of innovative ideas among employees. While ESM offers similar functionalities to public social media, its usage in the workplace can be monitored and administered by managers, potentially leading to different outcomes. However, it is important to note that public social media, being free of charge, can offer additional benefits if managed effectively by organizations. Lastly, the study emphasizes that employees’ work-related use of social media should not be banned in the workplace, as it offers advantages such as knowledge sharing, mutual support and increased innovation.
5.4 Limitations and research implications
This study, like other studies, suffers from its limitations. The main limitation of this study is related to the data collection procedure. The purpose of this study was to investigate its model in the financial industry of Iran. Thus, it was necessary to ask many financial firms to send our questionnaire link to their financial analysts. The arrangement took a lot of time and the managers were unwilling to do this since they believed this study was seeking to measure the organizational risk-taking in different organizations make comparisons and specify which firm has more risk-taking financial analysts. In developing countries, a lack of trust between people is as usual. Nevertheless, the authors convinced them that it was only a survey for scientific purposes and finally, ten financial firms agreed to fill out a limited number of questionnaires.
To show that there is still room to fill for working on work-related use of social media, knowledge management, employees’ risk-taking and employees' service innovation behavior, this study has provided the following recommendations for future studies. Future studies can conduct longitudinal research to capture the dynamics of social media use, knowledge management, risk-taking and service innovative behavior over time. This approach can offer a more nuanced understanding of how these factors evolve and interact within organizations. Future studies are recommended to examine potential moderating variables that may influence the observed relationships. Factors such as organizational culture, leadership styles or technological infrastructure could moderate the impact of social media on knowledge management, risk-taking and innovation. Finally, future studies are recommended to test the same model for ESM implementation in organizations and identify the existing differences.
Figures
Figure 1
Research model
Loadings and cross-loadings for latent variables
| Variables | Items | 1 | 2 | 3 | 4 | 5 | p-values |
|---|---|---|---|---|---|---|---|
| Social media | Q1 | (0.933) | 0.253 | 0.052 | −0.075 | −0.093 | <0.001 |
| Q2 | (0.946) | −0.216 | −0.092 | 0.087 | 0.137 | <0.001 | |
| Q3 | (0.986) | −0.032 | 0.038 | −0.012 | −0.044 | <0.001 | |
| Knowledge sharing | Q1 | −0.031 | (0.772) | −0.010 | 0.215 | −0.100 | <0.001 |
| Q2 | 0.060 | (0.717) | 0.034 | 0.400 | 0.028 | <0.001 | |
| Q3 | 0.165 | (0.743) | 0.218 | −0.497 | 0.146 | <0.001 | |
| Q4 | −0.165 | (0.863) | −0.208 | −0.097 | −0.060 | <0.001 | |
| Knowledge acquisition capability | Q1 | −0.127 | −0.002 | (0.963) | 0.104 | 0.015 | <0.001 |
| Q2 | 0.170 | −0.050 | (0.969) | −0.043 | −0.035 | <0.001 | |
| Q3 | −0.044 | 0.051 | (0.978) | −0.059 | 0.020 | <0.001 | |
| Risk-taking behavior | Q1 | −0.145 | 0.045 | 0.019 | (0.852) | −0.093 | <0.001 |
| Q2 | 0.200 | 0.119 | 0.016 | (0.833) | 0.097 | <0.001 | |
| Q3 | 0.010 | −0.190 | 0.023 | (0.901) | 0.153 | <0.001 | |
| Q4 | −0.059 | 0.039 | −0.058 | (0.865) | −0.161 | <0.001 | |
| Service innovation behavior | Q1 | −0.104 | 0.060 | −0.184 | 0.063 | (0.836) | <0.001 |
| Q2 | −0.170 | 0.137 | −0.176 | 0.118 | (0.875) | <0.001 | |
| Q3 | 0.083 | −0.288 | 0.051 | 0.047 | (0.869) | <0.001 | |
| Q4 | −0.181 | −0.152 | 0.039 | 0.139 | (0.704) | <0.001 | |
| Q5 | 0.099 | −0.052 | 0.083 | −0.142 | (0.866) | <0.001 | |
| Q6 | 0.311 | 0.354 | 0.249 | −0.261 | (0.658) | <0.001 |
Note(s): Loadings are shown within parentheses, loadings are un-rotated and cross-loadings are oblique-rotated; p-values refer to loadings
Source(s): Table by the author
Reliabilities
| Variables | CA | CR | AVE | VIF |
|---|---|---|---|---|
| Social media | 0.952 | 0.969 | 0.912 | 1.569 |
| Knowledge sharing | 0.777 | 0.857 | 0.602 | 1.714 |
| Knowledge acquisition capability | 0.972 | 0.982 | 0.947 | 1.347 |
| Risk-taking behavior | 0.886 | 0.921 | 0.745 | 1.613 |
| Service innovation behavior | 0.889 | 0.917 | 0.650 | 1.287 |
Note(s): CA: Cronbach’s alpha coefficient for latent variable, CR: composite reliability coefficient for latent variable, AVE: average variances extracted and variance inflation factors (VIFs) obtained through a full collinearity test. A VIF lower than 5 suggests no collinearity between a variable and other variable
Source(s): Table by the author
Discriminative validity
| Variables | Mean | SD | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|---|---|
| Social media | 3.466 | 1.229 | (0.955) | ||||
| Knowledge sharing | 3.037 | 0.691 | 0.330 | (0.776) | |||
| Knowledge acquisition capability | 2.783 | 1.228 | 0.451 | 0.341 | (0.973) | ||
| Risk-taking behavior | 3.187 | 0.771 | 0.460 | 0.521 | 0.210 | (0.863) | |
| Service innovation behavior | 2.816 | 0.856 | 0.036 | 0.444 | 0.152 | 0.268 | (0.806) |
Note(s): Square roots of average variances extracted (AVEs) shown on diagonal in italic
Source(s): Table by the author
Results
| H | Path | β | p-value | Supported? |
|---|---|---|---|---|
| H1 | Social media → Knowledge sharing | 0.565 | <0.001 | Yes |
| H2 | Social media → Knowledge acquisition | 0.376 | <0.001 | Yes |
| H3 | Knowledge sharing → Knowledge acquisition | 0.258 | <0.001 | Yes |
| H4 | Social media → Risk-taking | 0.376 | <0.001 | Yes |
| H5 | Knowledge sharing → Risk-taking | 0.415 | <0.001 | Yes |
| H6 | Knowledge acquisition → Risk-taking | 0.097 | 0.007 | Yes |
| H7 | Knowledge sharing → Service innovation behavior | 0.485 | <0.001 | Yes |
| H8 | Knowledge acquisition → Service innovation behavior | 0.356 | <0.001 | Yes |
| H9 | Risk-taking → Service innovation behavior | 0.317 | 0.003 | Yes |
| H10 | Social media → Service innovation behavior | −0.101 | 0.152 | No |
Source(s): Table by the author
R-squared, adjusted R-squared and Q-squared for latent variables
| Variables | R2 | Adjusted R2 | Q2 |
|---|---|---|---|
| Social media | – | – | – |
| Knowledge sharing | 0.320 | 0.317 | 0.317 |
| Knowledge acquisition capability | 0.289 | 0.283 | 0.288 |
| Risk-taking behavior | 0.517 | 0.511 | 0.509 |
| Service innovation behavior | 0.751 | 0.747 | 0.737 |
Source(s): Table by the author
Model fit indices
| APC | ARS | AARS | AFVIF | GOF | SPR |
|---|---|---|---|---|---|
| 0.335*** | 0.469*** | 0.464*** | 1.506 | 0.602 | 0.900 |
Note(s): ***p < 0.001; APC, Average path coefficient; ARS, Average R squared, AARS, Average adjusted R squared; AFVIF, Average full collinearity VIF, acceptable if ≤5, ideally if ≤3.3 and SPR, Simpson’s paradox ratio, acceptable if ≥0.7, ideally if = 1.000
Source(s): Table by the author
Appendix
The supplementary material for this article can be found online.
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Further reading
Kwayu, S., Abubakre, M., & Lal, B. (2021). The influence of informal social media practices on knowledge sharing and work processes within organizations. International Journal of Information Management, 58, 102280. doi: 10.1016/j.ijinfomgt.2020.102280.
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Olfat, M., Nasir, M., Shokoohyar, S., & Shokouhyar, S. (2024). Bloggers’ interactive practices and their followers’ purchase intentions: The mediating roles of perceived credibility and followers’ para-social interactions. Journal of Promotion Management, 30(3), 390–415.
Olfat, M., Rezvani, A., Khosravi, P., Shokouhyar, S., & Sedaghat, A. (2020). The influence of organisational commitment on employees’ work-related use of online social networks: The mediating role of constructive voice. International Journal of Manpower, 41(2), 168–183.
Tabarsa, G., Shokouhyar, S., & Olfat, M. (2018). The paradoxical influence of job satisfaction on destructive employees’ voice, considering the mediating role of social network sites and organizational commitment. Journal of Public Administration, 10(2), 311–332.
Tabarsa, G. A., Olfat, M., & Shokouhyar, S. (2019). A model for evaluating the paradoxical impacts of organizational members’ social use of SNSs on destructive voice. Journal of Indian Business Research, 11(3), 244–262.
Corresponding author
About the author
Mohammad Olfat is Information Systems Researcher at Shahid Beheshti University in Iran. He has published several studies in the top international journals, including International Journal of Manpower, Journal of Enterprise Information Management, Online Information Review, Kybernetes, Corporate Communications: An International Journal, Journal of Indian Business Research, Journal of Promotion Management, Aslib Journal of Information Management and so forth. Mohammad’s research interests are information systems, social media marketing and human–computer interaction. He also reviews for the journals, including Kybernetes, International Journal of Electronic Marketing and Retailing, Asia-Pacific Journal of Business Administration and so on. Mohammad is 2020 Emerald Literati Award Winner for the Outstanding Paper and he is Editorial Board Member in Journal of Promotion Management.