Content area
Purpose
This study investigates the relationship between telework frequency and knowledge management (KM) activity in Japan and the USA. By examining how telework impacts KM activity differently across these two countries, this study aims to provide insights into the design and implementation of effective telework policies tailored to specific cultural contexts.
Design/methodology/approach
Linear and quadratic regression analyses were conducted to explore the relationship between telework frequency and KM activity. Data was collected from samples in Japan and the USA, with respondents categorized based on prior telework experience. Various KM activities such as knowledge acquisition, sharing and application were measured using established scales.
Findings
In Japan, an inverted U-shaped relationship between telework frequency and KM activity was observed, with optimal activity occurring at four days of telework per week. Conversely, the USA demonstrated a linear relationship, indicating sustained KM activity across different telework frequencies. Notably, individuals with prior telework experience showed higher levels of KM activity compared to those without experience.
Originality/value
This study contributes to the understanding of how cultural differences influence the relationship between telework and KM activity, and ultimately learning. By highlighting the nuanced patterns observed in Japan and the USA, it emphasizes the importance of tailored telework policies and support mechanisms for effective KM in diverse cultural contexts. Future research could further explore additional explanatory variables and their impact on telework-related outcomes.
1. Introduction
As the COVID-19 pandemic unfolded, its disruptive impact prompted an unprecedented shift in work dynamics across the globe. With the imperative to ensure business continuity and protect employee health, many companies swiftly pivoted to telework arrangements. Governments responded with supportive measures, such as the enactment of the Coronavirus Aid, Relief, and Economic Security (CARES) Act in the USA in March 2020, which included provisions to facilitate businesses’ transition to telework. Similarly, in Japan, the government issued strong directives urging the population to stay indoors and encouraging companies to minimize commuter numbers by implementing telework policies (MHLW, 2020).
The dynamics of human interaction, communication and work have undergone a significant transformation due to the impact of the COVID-19 pandemic (Magnier-Watanabe, 2022). The fear of infection, the need for social distancing and the widespread adoption of telework have collectively reshaped the way people engage with each other and fulfill their professional responsibilities. This substantial shift from working in an office environment designed for organizational objectives to working from home, primarily intended for personal activities, inevitably influences how employees manage knowledge and, consequently, impacts their job performance (Magnier-Watanabe, 2023). It becomes crucial to explore the role of prior telework experience and the frequency of telework, as they have been identified as factors influencing knowledge management (KM) – the strategic handling of knowledge to fulfill individual responsibilities and contribute to organizational goals (Allen et al., 2015; Taskin and Bridoux, 2010; Jennex, 2008). Indeed, KM plays a crucial role in enhancing individual performance, which comprises task, contextual and adaptive performance within organizations, particularly in knowledge-intensive environments (Alavi and Leidner, 2001). Task performance, defined as the efficiency and effectiveness with which employees carry out their core work-related activities, is closely linked to the organization’s ability to manage and use knowledge effectively. Contextual performance, which refers to behaviors that contribute to the broader organizational environment − such as cooperation, helping colleagues and taking initiative − is also enhanced through effective KM practices that promote knowledge sharing and collaboration (Organ, 1988; Borman and Motowidlo, 1993). Additionally, adaptive performance, which involves employees’ ability to respond to change, learn new skills and handle unpredictable situations, is fostered by KM activities that enable continuous learning and knowledge application, making organizations more agile in dynamic environments (Pulakos et al., 2000).
In this context, it is imperative to investigate how knowledge creation, sharing and application continue to operate in the face of these new working conditions and to propose effective strategies to address potential challenges. This paper specifically delves into the impact of mandatory telework on the extent of KM activities, considering the variables of prior telework experience and telework frequency in the USA and Japan. The findings aim to provide insights that can guide both American and Japanese companies in adapting and enhancing their organizational work practices to elevate knowledge and worker productivity. This adaptation is crucial in meeting the demands imposed by the new realities of telework beyond the COVID-19 pandemic and facilitating a quicker recovery from the economic downturn caused by the pandemic.
This paper makes a significant contribution to the understanding of KM practices in the context of telework, beyond the COVID-19 pandemic. By investigating the impact of telework on knowledge creation, sharing and application, it addresses a crucial gap in existing literature regarding the long-term effects of remote work on organizational knowledge dynamics. The study’s focus on prior telework experience and frequency adds nuance to the analysis, providing insights into how varying levels of telework exposure influence KM activities. Moreover, by examining these factors in both the USA and Japan, the paper offers a comparative perspective, enriching the discussion with cultural and contextual insights. The findings of this research contribute to the development of effective strategies for organizations to adapt their work practices, enhance knowledge dissemination and improve worker productivity in the evolving landscape of telework beyond the pandemic.
2. Literature review
Telework from home
Telework from home, commonly known as home-based teleworking or home-working, refers to the practice of salaried employees performing their duties from their homes. The extent of telework can vary, ranging from just a few days per month to a full five-days workweek (Aguilera et al., 2016). Numerous studies have examined the factors that influence the uptake of home-based telework, focusing on aspects such as job characteristics, perceived benefits and drawbacks, and alignment with organizational or national cultural practices (Peters and Batenburg, 2015; Aguilera et al., 2016).
Teleworking from home is generally considered most suitable for highly skilled and independent workers who view it as beneficial for both their professional and personal lives. Prior research has identified three main categories of teleworkers − realistic, ambivalent and enthusiastic − each with varying perspectives on the pros and cons of telework. These distinctions are often shaped by sociodemographic factors, such as education level, income and the frequency of teleworking (Peñarroja, 2024). Telework tends to thrive in environments where it has widespread acceptance within the culture or organization (De Graaff and Rietveld, 2007).
In the USA, government publications have highlighted both the benefits and challenges of teleworking (OPM, 2020; GSA, 2021). Similarly, Japan's Ministry of Health, Labor, and Welfare has issued public guidelines on telework since 2004, with updates in 2020 that underscore its advantages and disadvantages (MHLW, 2008, 2020). From a KM perspective, telework serves as a critical tool for maintaining business continuity, especially in times of crisis, such as natural disasters or public health emergencies like the COVID-19 pandemic. However, telework also introduces challenges, including feelings of isolation due to reduced face-to-face interactions and communication delays that can slow down problem-solving and decision-making in remote work environments (MHLW, 2008, 2020). The rapid shift to telework during the COVID-19 pandemic further revealed gaps in teleworker training and preparedness.
Additionally, cultural factors tied to home life may significantly impact telework effectiveness. In Japan, smaller living spaces and the traditional separation of work and family roles can present challenges to teleworkers, particularly when it comes to establishing a dedicated workspace and maintaining focus (Nakane, 1972; Magnier-Watanabe et al., 2022). In contrast, in the USA, larger homes and a more integrated approach to work and home life may support more efficient teleworking conditions (Peters and Batenburg, 2015). These differences highlight how home environments and cultural expectations shape the teleworking experience, influencing both productivity and KM outcomes.
Knowledge management
KM embodies the systematic handling of both tacit and explicit knowledge within and beyond an organization’s boundaries, aiming to efficiently meet corporate goals (Magnier-Watanabe and Senoo, 2008). This concept underlines the twofold nature of knowledge (Polanyi, 1966), suggesting that remote work, which diminishes in-person interactions, may challenge KM due to a potential scarcity of tacit knowledge. KM’s significance stems from its proven role in enhancing individual and organizational performance, notably in industries reliant on knowledge (Abubakar et al., 2019). KM enables organizations to leverage their knowledge assets, such as skills, expertise, experience and innovation, to gain a competitive advantage in the market and create value for their stakeholders.
A prevalent view of KM is the knowledge value chain, delineating the firm’s knowledge activities into stages where employees contribute to shaping the organization’s competitive edge (Wong, 2004). While various models differ slightly in stages and terms (Chen and Chen, 2006), they generally encompass three key aspects: knowledge acquisition or creation, sharing or storage and application or usage (Heisig, 2009). These aspects represent the processes of generating, disseminating and using knowledge within the organization, as well as the outcomes of these processes in terms of improved performance, innovation and customer satisfaction.
Knowledge acquisition, including identification and creation (Heisig, 2009), involves acquiring fresh insights from within or outside the organization in either tacit or explicit form (Massa and Testa, 2009). Identification assumes existing knowledge, acknowledging and accepting it, while creation involves developing new knowledge for the organization. For example, identification can occur through scanning the external environment, such as competitors, customers or suppliers, to identify best practices, trends or opportunities. Creation can occur through research and development, experimentation or collaboration, to generate new ideas, solutions or products.
Knowledge sharing encompasses both formal and informal knowledge exchanges among individuals within an organization (Kianto et al., 2018). It involves transmitting and receiving knowledge, influenced by trust, motivation, organizational culture and leadership support (Van Den Hooff and De Ridder, 2004). Tacit knowledge, harder to express, presents challenges in sharing, unlike explicit knowledge, which is more easily communicable (Polanyi, 1966). For instance, tacit knowledge can be shared through mentoring, coaching, storytelling or observation, while explicit knowledge can be shared through documents, databases, reports or presentations.
Finally, knowledge application involves integrating obtained or developed knowledge into the organization’s products, services or practices to enhance its value (Massa and Testa, 2009). It is closely associated with learning, which can be either exploiting established paths or exploring new trajectories (March, 1991). Exploitative learning follows existing paths, while explorative learning ventures into new directions for the organization (Gupta et al., 2006). For example, knowledge application can involve applying existing knowledge to improve efficiency, quality or productivity, or applying new knowledge to create innovation, differentiation or growth.
Development of hypotheses
Prior telework experience and knowledge management activity.
The extent to which employees have previous exposure to telework is contingent on whether they received adequate training and practice in working remotely before the outbreak of the COVID-19 pandemic. Telework, which relies heavily on technology for facilitating remote communication, accessing documents and performing broader knowledge-related activities, presupposes a high level of proficiency in information and communication technology (Nakrošienė et al., 2019). Furthermore, the advent of technology-mediated remote workplaces has rendered telework less constrained by physical location (Messenger and Gschwind, 2016). As a result, employees who are skilled in modern technologies demonstrate a greater willingness to telework, while those who lack such expertise tend to be reluctant to work away from the conventional office environment (Dias et al., 2022).
Lee and Choi (2003) underscored the importance of collaboration, trust, learning, skills and IT support in the processes of knowledge creation and dissemination. These aspects, encompassing learning, skills and IT support, could be associated with prior telework experience, providing advantages through the development of specific competencies.
Individuals who have prior exposure to telework are likely to be familiar with the tools, platforms and techniques that enable effective remote work, in contrast to those who lack prior exposure, who could face difficulties in adapting to remote work (Blahopoulou et al., 2022). Studies indicate that employees who have prior telework experience adjust faster to remote work environments and exhibit higher levels of productivity. Therefore, previous telework experience is assumed to have a positive influence on KM in remote work situations. Consequently, we propose the following hypothesis:
Telework frequency and knowledge management activity.
Teleworking frequency hinges on the duration spent working remotely (Gajendran and Harrison, 2007; Pérez et al., 2003). Occasional or ad hoc teleworking occurs when an individual works from home on an as-needed basis, such as during illness or for unplanned childcare. Part-time or partial telework involves an employee intentionally and prearranging to work from home, the office or a client’s site part of the time. Conversely, full telework happens when a worker consistently operates from home or another non-office location, rarely visiting the company premises (Nakrošienė et al., 2019).
Taskin and Bridoux (2010) underscored the association between telework frequency and KM. They argued that the absence of face-to-face interaction, which typically enables nuanced communication via nonverbal and paralinguistic cues (Thatcher and Zhu, 2006), might diminish communication quality and depth. This reduction in communication richness, a notion previously emphasized in telework studies leveraging media richness theory, could lead to a decline in knowledge-oriented tasks (Magnier-Watanabe, 2022). Another downside of frequent telework is the potential detachment between teleworkers and non-teleworkers, restricting opportunities to establish trust due to fewer face-to-face encounters (Taskin and Bridoux, 2010).
While past research has established a positive connection between telework and productivity (Nakrošienė et al., 2019; Kazekami, 2020), it has also been linked to lower organizational knowledge creation and transfer (Bridoux and Taskin, 2005). Excessive telework frequency is believed to adversely affect social inclusion and relationships with colleagues and supervisors, resulting in reduced identification with the firm and, consequently, hampering knowledge creation and transfer (Szulanski, 1996). Thus, prior studies suggest that telework benefits KM up to a specific frequency. In fact, recent research in Japan has established that “higher telework frequency helps experienced teleworkers manage knowledge, as long as they go to the office once a week” (Magnier-Watanabe, 2023).
We therefore propose that, for seasoned teleworkers, telework frequency positively correlates with increased KM activity, provided they have weekly face-to-face interaction to access, share and apply tacit knowledge that cannot be transmitted remotely. The transition to mandatory telework has a lesser impact on KM practices among those already adept in that setting. We anticipate an inverted U-shaped relationship between telework frequency and KM activity among those with prior telework experience, where KM activity rises with telework frequency until it peaks at three of four days a week and decreases when telework is five days a week (Magnier-Watanabe, 2023). Telework frequency of three or four days a week ensures periodic in-person interaction, enhancing knowledge acquisition, sharing and application.
Conversely, employees lacking prior telework experience might face challenges in establishing effective remote work routines and communication practices. Telework presents a significant shift for inexperienced individuals, leading to the need to relearn working in isolation for several days a week, resulting in inefficiencies and reduced KM activity at any telework frequency (Stempel and Siestrup, 2022). Without previous telework experience, they might predominantly focus on adapting to the new remote work environment rather than optimizing KM activities. Hence, we posit the following hypotheses:
3. Methodology
Survey and sample
Data collection occurred in December 2021 in Japan and in October 2023 in the USA through an Internet survey firm. Only respondents engaged in telework for a minimum of one full day per week during the health emergency period under study were included. The Japanese sample consisted of 945 respondents, 370 with prior telework experience and 575 without. Most are male (73%) with a university degree (72%), with subordinates (62%) working in large corporations (49%) with ten or more years in the same company (53%) (Table 1). The American sample consisted of 957 respondents, 685 with prior telework experience and 272 without. Gender is rather evenly distributed (48% is male), most have at least a university degree (76%), with subordinates (91%) working in large corporations (40%) for two to five years (41%) (Table 2).
Measures
KM activity was assessed using three items for each KM mode: acquisition, sharing, and application. The measurement scales were adapted from Kianto et al. (2018) and rated on a seven-point scale. Participants were asked to indicate their level of agreement with statements about knowledge acquisition, sharing and application. Telework frequency ranged from one day to five days a week. Although identical measures were used for each country sample, they are analyzed separately to make comparisons possible, and shown sequentially starting with the Japanese and followed by the American respondents.
The constructs related to KM demonstrated high internal consistency, as evidenced by Cronbach’s alpha values of 0.893, 0.891 and 0.873, respectively in the Japanese sample, and values of 0.790, 0.802 and 0.810, respectively, in the American sample. Factor analysis revealed three distinct factors explaining 6%, 72% and 5% of the variance for acquisition, sharing and application, respectively, accounting for a total 83% of the variance in the Japanese sample, and 59%, 6% and 7% of the variance, respectively, culminating in a cumulative 72% in the American sample, thus affirming the convergent validity of the constructs. Although the KM constructs exhibited some collinearity surpassing 0.8 in the Japanese sample and 0.7 in the American sample, they were retained separately for individual evaluation, as outlined in Tables 3 and 4.
4. Results
Differences in KM activity based on prior telework experience
Japanese sample.
To evaluate H1, a t-test was used to compare individuals with and without prior telework experience. The disparities in KM activity levels between these two groups were found to be highly significant (p < 0.001). Individuals with prior telework experience exhibited notably higher levels of KM activity in comparison to those without such experience, thereby corroborating H1. Specifically, in terms of knowledge acquisition, the mean scores were 4.699 (SD = 1.344) for individuals without prior telework experience and 5.150 (SD = 1.229) for those with prior experience, with a corresponding t-values of −4.855 (p = 0.000). Similarly, for knowledge sharing, the mean scores were 4.612 (SD = 1.396) and 5.170 (SD = 1.223), respectively, yielding a t-values of −5.847 (p = 0.000). Likewise, for knowledge application, the mean scores were 4.901 (SD = 1.255) and 5.254 (SD = 1.182), respectively, with a t-values of −4.026 (p = 0.000). Despite the observed differences, all modes of KM activity scored above 4.5, indicating generally positive evaluations across the board. Moreover, individuals with prior telework experience tended to rate KM activity higher than 5 on the seven-point scale, further emphasizing the positive impact of telework experience on KM activity.
American sample.
Significant differences in KM activity were observed between the two groups (p < 0.05 or p < 0.001). Individuals with prior telework experience exhibited higher levels of KM activity compared to those without such experience, thereby supporting H1. Specifically, regarding knowledge acquisition, the mean scores were 5.181 (SD = 1.359) for individuals without prior telework experience and 5.503 (SD = 1.194) for those with prior experience, with a corresponding t-values of −3.831 (p = 0.003). Similarly, for knowledge sharing, the mean scores were 5.100 (SD = 1.403) and 5.537 (SD = 1.187), respectively, yielding a t-values of −5.152 (p = 0.001). Likewise, for knowledge application, the mean scores were 5.374 (SD = 1.312) and 5.631 (SD = 1.179), respectively, with a t-values of −3.143 (p = 0.004). All modes of KM activity scored above 5.0, suggesting generally positive evaluations. Notably, individuals with prior telework experience tended to rate KM activity higher than 5.5 on the seven-point scale, indicating particularly positive evaluations in this group.
Relationship between telework frequency and KM activity
Japanese sample.
To assess H2, ANOVA analyses were used to investigate variations in KM activity based on telework frequency per week (Figure 1). Among respondents with prior telework experience (n = 284), significant discrepancies were observed in KM activity levels across various telework frequencies per week, supporting H2a. Specifically, significant differences were found in knowledge acquisition [F(4,279) = 6.735, p = 0.000], knowledge sharing [F(4, 279) = 5.624, p = 0.000] and knowledge application [F(4, 279) = 6.079, p = 0.000]. Post hoc comparisons using Tukey honest significant difference (HSD) tests revealed statistically significant differences in telework frequency, although not for every frequency. Nonetheless, a discernible trend emerged, with the highest levels of KM activity observed at four days a week, followed by a decline at five days a week. Conversely, among respondents lacking prior telework experience (n = 661), no statistically significant differences were observed in KM activity, supporting H2b.
To further support H2, both linear and quadratic regression analyses were performed, revealing nonlinear associations between telework frequency and KM activity, as well as between telework frequency among individuals with prior telework experience. These analyses consistently indicated significantly higher squared regression coefficients for quadratic equations (Table 5).
An increase in telework frequency from one to four days per week is associated with higher levels of KM activity. However, engaging in full telework (five days a week) is linked to decreased KM activity. This observation confirms an inverted U-shaped relationship, wherein telework frequency negatively impacts KM activity when employees telework every day without any in-office presence. In essence, while higher telework frequency facilitates proficiency with collaboration tools and enhances KM activity, regular face-to-face interactions with colleagues and partners at least once a week are essential. It is important to note that although our analyses confirm the inverted U-shaped relationship between telework frequency and KM activity, the low R2 value indicates that the model explains only a small portion of the variability. This suggests that additional explanatory variables, not included in this analysis, may play a significant role.
American sample.
Figure 2 illustrates the distribution of KM activity based on telework frequency per week. Among participants with prior telework experience (n = 593), significant differences in KM activity across various telework frequencies were observed for all three KM activity modes. Surprisingly, the highest levels of KM activity were reported among individuals teleworking five days a week, contradicting H2a: knowledge acquisition [F(4,588) = 14.936, p = 0.000]; knowledge sharing [F(4,588) = 11.684, p = 0.000]; and knowledge application [F(4,588) = 20.596, p = 0.000]. Additionally, post hoc comparisons using Tukey’s HSD tests revealed statistically significant differences in KM activity based on telework frequency, albeit not consistently across all frequencies. Nevertheless, discernible patterns emerged, indicating a direct, positive correlation between telework frequency and KM activity. Conversely, for respondents without prior telework experience (n = 364), no statistically significant differences in KM activity were found, lending support to H2b.
Contrary to expectations, quadratic equations did not provide a better fit to the data compared to linear equations, thus indicating the presence of a linear relationship and refuting H2a (Table 6).
5. Discussion and conclusion
Discussion
The Japanese sample’s results indicate an inverted U-shaped relationship between telework frequency and KM activity for employees with prior telework experience. Knowledge acquisition, sharing and application show statistically significant differences, with peak activity at four days a week. However, for those without prior telework experience, there are no significant differences. The quadratic regression analyses support a nonlinear relationship, emphasizing the importance of a balanced telework frequency. Beyond teleworking four days a week, diminishing returns and a decline in KM activity are observed, suggesting the need for minimum in-person collaboration.
In contrast, the American sample shows a positive linear relationship between telework frequency and KM activity for employees with prior telework experience. Knowledge acquisition, sharing and application exhibit statistically significant differences, with the highest levels at five days a week. The results are at odds with the inverted U-shaped relationship found in Japan. For respondents without prior telework experience, there are no significant differences in knowledge activities. Quadratic regression analyses further refute the presence of an inverted U-shaped curve, emphasizing a straightforward positive relationship.
Japanese sample: balancing tacit and explicit knowledge.
In the Japanese context, the results suggest a nuanced relationship between telework frequency and KM activity. The peak in KM activity at four days of telework indicates a delicate balance between tacit knowledge sharing through face-to-face interactions and explicit knowledge transfer through digital means. This aligns with Japan’s collaborative work culture, emphasizing the value of teamwork and shared experiences (Magnier-Watanabe, 2022). However, the decline in KM activity at five days a week implies that, in Japan, the traditional work style, emphasizing in-person collaboration, remains crucial for effective KM (Gajendran et al., 2015).
Indeed, Japanese companies, renowned for their emphasis on collective harmony and long-term employment relationships, face a critical need for effective tacit knowledge sharing. Tacit knowledge, often deeply embedded in individuals’ experiences, insights and skills, plays a pivotal role in sustaining organizational competitiveness and fostering innovation (Nonaka and Takeuchi, 1995). This is particularly relevant in the context of Japanese work culture, which traditionally values implicit communication and interpersonal relationships (Nakane, 1972). Recognizing the value of tacit knowledge sharing aligns with the broader global discourse on KM. Hislop (2003) highlights the integration of human resource management and KM, emphasizing the importance of commitment in fostering effective knowledge-sharing practices. In the Japanese organizational context, where loyalty and commitment are highly valued, understanding and leveraging tacit knowledge can contribute significantly to sustained success.
For those with prior telework experience in Japan, the results align with expectations, showing that experience enhances the ability to manage knowledge effectively remotely. Conversely, the lack of significant differences for those without prior telework experience may indicate a learning curve or adaptation period for novices (Golden et al., 2008). The impact of information and communication technology (ICT) on KM activity is a possible culprit, with increased telework frequency positively influencing ICT proficiency. Nevertheless, the decline in KM activity at five days suggests that, in the Japanese work context, technology alone cannot replace the value of face-to-face interactions (Taras et al., 2013).
In addition to the workplace cultural differences between Japan and the USA, the role of home life and living conditions must also be considered in understanding telework outcomes. Cultural norms in Japan, for example, often involve smaller living spaces and a more traditional separation between work and home life. Japanese homes are typically designed for family activities, which may not be conducive to extended periods of telework, potentially affecting the ability to engage in KM activities. Moreover, strong cultural expectations around family roles, such as caregiving responsibilities, can add pressures that influence an employee's ability to focus on work tasks (Nakane, 1972; Magnier-Watanabe et al., 2022).
American sample: embracing digital collaboration.
In contrast, the American context presents a more straightforward relationship between telework frequency and KM activity. The positive linear relationship across various telework frequencies indicates a work culture comfortable with digital collaboration, valuing virtual interactions. This flexible approach contrasts with Japan’s preference for four days of telework per week (Gajendran et al., 2015). For American employees with prior telework experience, consistent positive outcomes suggest a sustained ability to manage knowledge effectively, irrespective of frequency. Like in Japan, the lack of differences for those without prior telework experience may indicate a learning curve or adaptation period (Golden et al., 2008).
In the USA, the linear relationship emphasizes a high level of digital proficiency, where employees effectively use ICT tools to manage knowledge across different telework frequencies. Unlike in Japan, the absence of a decline in KM activity at full telework frequency suggests that sustained virtual collaboration remains effective in the USA (Alsharo et al., 2017).
In contrast, American homes are generally larger, providing more physical space for dedicated home offices, which can facilitate better conditions for remote work. Additionally, cultural norms in the USA tend to promote a more flexible blending of work and home life, allowing employees to integrate their professional and personal responsibilities more fluidly (Cohen and Bailey, 1997). These environmental and cultural differences could help explain the variance in KM activity and the effectiveness of telework between the two countries, with American teleworkers potentially benefiting from a more conducive home environment and greater acceptance of remote work practices (Peters et al., 2016).
Past research among American workers has highlighted the effectiveness of remote work without the need for a shared office space, cementing the primacy of explicit knowledge sharing. As early as 1997, Cohen and Bailey (1997) found that, in dispersed or remote teams, explicit communication channels and documentation become crucial for achieving common goals. Watson-Manheim et al. (2002) further emphasized a shift in knowledge sharing practices in virtual work settings in the USA, indicating an increased reliance on explicit knowledge due to the limitations of face-to-face interactions.
This shift challenges traditional notions of knowledge sharing, leaning toward a reliance on explicit knowledge rather than tacit knowledge. The findings stress the fact that American employees demonstrate significant productivity and effectiveness while working remotely, dispelling concerns that physical presence in a shared office is crucial for successful collaboration (Grant, 1996). This trend aligns with a growing preference for explicit knowledge, emphasizing codified, documented and easily transferable information.
Ever more sophisticated remote work technologies and digital communication tools have played a pivotal role in facilitating this transition (Hertel et al., 2005). The ability to share explicit knowledge through virtual platforms has become a cornerstone of efficient collaboration, allowing employees to access information regardless of their physical location. For instance, enterprise social media platforms can help remote and hybrid workers develop relational confidence, or the confidence that one has a close enough relationship to a colleague to ask and get needed knowledge (Keppler and Leonardi, 2023).
Conclusion
In conclusion, this study sheds light on the complex interplay between telework frequency and KM activity, particularly within the cultural contexts of Japan and the USA. By examining data from both countries, we have revealed nuanced patterns that underscore the importance of tailoring telework policies to cultural preferences and organizational needs.
Our findings indicate that in Japan, where traditional work culture emphasizes face-to-face interactions and tacit knowledge sharing, a balanced telework frequency of four days per week appears optimal for maintaining high levels of KM activity. Beyond this threshold, diminishing returns are observed, highlighting the continued importance of in-person collaboration for effective KM. Conversely, in the USA, where digital proficiency and virtual collaboration are more prevalent, a linear relationship between telework frequency and KM activity is evident. Employees with prior telework experience demonstrate sustained levels of KM activity across different telework frequencies, suggesting a greater reliance on explicit knowledge sharing and digital tools. In either country sample, workers with no prior telework experience displayed consistently lower levels of KM activity and furthermore, they did not show any significant change in KM activity at any level of telework frequency. This suggests that training and education in telework have the potential to improve KM in both countries.
Implications for research and practice
First, the findings highlight the importance of tailoring telework policies to cultural preferences, especially concerning the balance between tacit and explicit knowledge sharing. Organizations need adaptive ICT strategies that align with cultural norms to ensure effective KM in diverse work environments (Jiménez et al., 2017). Providing training and support for employees transitioning to telework, particularly in cultures where face-to-face interactions hold significant value, can enhance KM outcomes (Gajendran et al., 2015). Recognizing the flexibility of work styles and prior telework experience in influencing KM, organizations should adopt flexible policies that accommodate diverse employee backgrounds and preferences (Abrams, 2019; Peñarroja, 2024). This could be achieved by establishing virtual mentorship initiatives that pair experienced employees with less experienced ones. This would allow for the transfer of tacit knowledge that is often lost in remote settings. Through regular virtual meetings and discussions, mentors can provide guidance, share insights and support the learning and development of their mentees.
Second, the contradictory findings between Japan and the USA highlight the importance of considering culture in interpreting the impact of telework on KM activities (Peters et al., 2016). Cultural, organizational and contextual factors may contribute to the observed differences. Encouraging a balanced telework frequency may be essential to maximize the benefits of KM activities in diverse cultural settings. These insights emphasize the need for context-specific approaches when designing telework strategies. Understanding how telework frequency influences KM activities differently in Japan and the USA is crucial for effective policy design. Companies could use data analytics and KM tools to create personalized learning paths for employees based on their cultural attributes, job roles, skills and previous learning experiences.
Finally, future research could explore additional explanatory variables and their influence on telework-related outcomes. Furthermore, ongoing efforts to enhance digital collaboration tools and support mechanisms can help organizations adapt to the evolving telework landscape and foster a culture of effective knowledge sharing and collaboration, regardless of geographical or cultural boundaries.
Funding: This work was supported by a Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (KAKENHI #21K01696).
Conflict of interest: The author declares that there are no conflicts of interest.
Figure 1.KM during mandatory telework (Japan)
Figure 2.KM during COVID-19 related telework (USA)
Table 1.
Sample demographics (Japan)
| Indicator | N | % | Indicator | N | % |
|---|---|---|---|---|---|
| Gender | Function | ||||
| Men | 686 | 72.6 | General employee | 522 | 55.2 |
| Women | 259 | 27.4 | Section chief | 194 | 20.5 |
| Manager | 105 | 11.1 | |||
| Age | Senior manager | 33 | 3.5 | ||
| Under 25 | 36 | 3.8 | Top management | 43 | 4.6 |
| 25–29 | 178 | 18.8 | CEO | 16 | 1.7 |
| 30–39 | 156 | 16.5 | Other | 32 | 3.4 |
| 40–49 | 231 | 24.4 | |||
| 50–59 | 201 | 21.3 | Company size | ||
| Over 60 | 143 | 15.1 | <10 | 29 | 3.1 |
| 10–49 | 91 | 9.6 | |||
| Education | 50–249 | 223 | 23.6 | ||
| High school | 64 | 6.8 | 250–499 | 144 | 15.2 |
| Professional school | 50 | 5.3 | 500+ | 458 | 48.5 |
| Associate degree | 28 | 3.0 | |||
| University degree | 684 | 72.4 | Tenure | ||
| Master degree | 93 | 9.8 | 2−5 yrs | 265 | 28.0 |
| PhD degree | 24 | 2.5 | 5−10 yrs | 175 | 18.5 |
| Other | 2 | 0.2 | 10 yrs+ | 505 | 53.4 |
| Occupation | Prior telework exp. | ||||
| Top management | 223 | 23.6 | None | 575 | 60.8 |
| Profess. / tech. work. | 230 | 24.3 | 1–3 times per month | 86 | 9.1 |
| Office worker | 332 | 35.1 | 1 day a week | 66 | 7.0 |
| Sales staff | 83 | 8.8 | 2 days a week | 79 | 8.4 |
| Service staff | 31 | 3.3 | 3 days a week | 59 | 6.2 |
| Other | 46 | 4.9 | 4 days a week | 54 | 5.7 |
| 5 days a week | 26 | 2.8 | |||
| Subordinates | |||||
| 0 | 356 | 37.7 | Telework frequency | ||
| 1–5 | 262 | 27.7 | None | 0 | 0 |
| 6–10 | 124 | 13.1 | 1–3 times per month | 0 | 0 |
| 11–30 | 102 | 10.8 | 1 day a week | 74 | 7.8 |
| 31+ | 101 | 10.7 | 2 days a week | 179 | 18.9 |
| 3 days a week | 230 | 24.3 | |||
| 4 days a week | 188 | 19.9 | |||
| 5 days a week | 274 | 29.0 |
Source: Author’s own work
Table 2.
Sample demographics (USA)
| Indicator | N | % | Indicator | N | % |
|---|---|---|---|---|---|
| Gender | Function | ||||
| Men | 462 | 48.3 | Entry level | 62 | 6.5 |
| Women | 495 | 51.7 | Intermediate | 189 | 19.7 |
| First-level mgt. | 126 | 13.2 | |||
| Age | Middle mgt. | 272 | 28.4 | ||
| Under 25 | 35 | 3.7 | Senior mgt. | 308 | 32.2 |
| 25–29 | 126 | 13.2 | |||
| 30–39 | 427 | 44.6 | Company size | ||
| 40–49 | 216 | 22.6 | <10 | 23 | 2.4 |
| 50–59 | 119 | 12.4 | 10–49 | 100 | 10.4 |
| Over 60 | 34 | 3.6 | 50–249 | 247 | 25.8 |
| 250–499 | 207 | 21.6 | |||
| Education | 500+ | 380 | 39.7 | ||
| High school | 94 | 9.8 | |||
| Professional school | 21 | 2.2 | Tenure | ||
| Associate degree | 113 | 11.8 | 2−5 yrs | 393 | 41.1 |
| University degree | 328 | 34.3 | 5−10 yrs | 348 | 36.4 |
| Master degree | 263 | 27.5 | 10 yrs+ | 216 | 22.6 |
| PhD degree | 133 | 13.9 | |||
| Other | 5 | 0.5 | Prior telework exp. | ||
| None | 272 | 28.4 | |||
| Occupation | 1–3 times per month | 92 | 9.6 | ||
| Entry level | 62 | 6.5 | 1 day a week | 112 | 11.7 |
| Intermediate | 189 | 19.7 | 2 days a week | 126 | 13.2 |
| First-level mgt | 126 | 13.2 | 3 days a week | 93 | 9.7 |
| Middle management | 272 | 28.4 | 4 days a week | 137 | 14.3 |
| Senior management | 308 | 32.2 | 5 days a week | 125 | 13.1 |
| Entry level | 62 | 6.5 | |||
| Telework frequency | |||||
| Subordinates | None | 0 | 0 | ||
| 0 | 90 | 9.4 | 1–3 times per month | 0 | 0 |
| 1–5 | 167 | 17.5 | 1 day a week | 75 | 7.8 |
| 6–10 | 219 | 22.9 | 2 days a week | 144 | 15.0 |
| 11–30 | 287 | 30.0 | 3 days a week | 144 | 15.0 |
| 31+ | 194 | 20.3 | 4 days a week | 201 | 21.0 |
| 5 days a week | 393 | 41.1 |
Source: Author’s own work
Table 3.
Means, standard deviations and correlations of study variables (Japan)
| Variable | Mean | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. Gender | 1.274 | 0.446 | 1 | |||||||||
| 2. Age range | 6.330 | 2.559 | −0.293** | 1 | ||||||||
| 3. Education | 3.817 | 1.019 | 0.001 | −0.055 | 1 | |||||||
| 4. Tenure | 4.254 | 0.867 | −0.229** | 0.614** | −0.022 | 1 | ||||||
| 5. Subordinates | 2.291 | 1.349 | −0.045 | 0.014 | 0.087** | 0.137** | 1 | |||||
| 6. Company size | 3.964 | 1.176 | −0.106** | 0.152** | 0.129** | 0.223** | 0.148** | 1 | ||||
| 7. Telework frequency | 5.433 | 1.295 | 0.013 | 0.022 | 0.052 | −0.039 | −0.110** | 0.041 | 1 | |||
| 8. ACQ | 4.835 | 1.326 | 0.074* | −0.275** | 0.072* | −0.221** | 0.165** | −0.006 | 0.043 | 1 | ||
| 9. SHARE | 4.780 | 1.370 | 0.060 | −0.251** | 0.061 | −0.194** | 0.190** | 0.010 | 0.056 | 0.829** | 1 | |
| 10. APPLY | 5.007 | 1.243 | 0.082* | −0.241** | 0.075* | −0.176** | 0.152** | 0.034 | 0.078* | 0.806** | 0.861** | 1 |
Notes:Gender: 1 = male; 2 = female
Age range: 2 = 20–24; 3 = 25–29; 4 = 30–34; 5 = 35–39; 7 = 40–44; 7 = 45–49; 8 = 50–54; 9 = 55–59; 10 = 60–64
Education: 1 = High school; 2 = Prof. school; 3 = Associate degree; 4 = University; 5 = Master degree; 6 = PhD; 7 = Other
Tenure: 3 = 2−5 yrs; 4 = 5−10 yrs; 5 = 10 yrs+
Subordinates: 1 = 0; 2 = 1–5; 3 = 6–10; 4 = 11–30; 5 = 31+
Company size: 1=<10; 2 = 10–49; 3 = 50–249; 4 = 250–499; 5 = 500+
Telework freq.: 3 = one day a week; 4 = two days a week; 5 = three days a week; 6 = four days a week; 7 = five days a week
KM: seven-point Likert scale;
*p < 0.05; **p < 0.001
Source: Author’s own work
Table 4.
Means, standard deviations and correlations of study variables (USA)
| Variable | Mean | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. Gender | 1.517 | 0.500 | 1 | |||||||||
| 2. Age range | 3.376 | 1.087 | 0.028 | 1 | ||||||||
| 3. Education | 4.112 | 1.399 | −0.017 | 0.018 | 1 | |||||||
| 4. Tenure | 3.815 | 0.776 | 0.012 | 0.396** | 0.066* | 1 | ||||||
| 5. Subordinates | 3.343 | 1.243 | −0.146** | −0.092** | 0.178** | 0.156** | 1 | |||||
| 6. Company size | 3.858 | 1.127 | −0.046 | 0.144** | 0.220** | 0.225** | 0.114** | 1 | ||||
| 7. Telework frequency | 5.724 | 1.339 | 0.127** | 0.171** | 0.016 | 0.085** | −0.125** | 0.130** | 1 | |||
| 8. ACQ | 5.380 | 1.268 | −0.069* | −0.018 | 0.151** | −0.020 | 0.089** | 0.055 | 0.170** | 1 | ||
| 9. SHARE | 5.371 | 1.291 | −0.109** | −0.058 | 0.135** | −0.022 | 0.142** | 0.032 | 0.105** | 0.750** | 1 | |
| 10. APPLY | 5.533 | 1.237 | −0.034 | 0.025 | 0.157** | −0.026 | 0.058 | 0.049 | 0.206** | 0.701** | 0.740** | 1 |
Notes:Gender: 1 = male; 2 = female
Age range: 2 = 20–24; 3 = 25–29; 4 = 30–34; 5 = 35–39; 7 = 40–44; 7 = 45–49; 8 = 50–54; 9 = 55–59; 10 = 60–64
Education: 1 = High school; 2 = Prof. school; 3 = Associate degree; 4 = University; 5 = Master degree; 6 = PhD; 7 = Other
Tenure: 3 = 2−5 yrs; 4 = 5−10 yrs; 5 = 10 yrs+
Subordinates: 1 = 0; 2 = 1–5; 3 = 6–10; 4 = 11–30; 5 = 31+
Company size: 1=<10; 2 = 10–49; 3 = 50–249; 4 = 250–499; 5 = 500+
Telework freq.: 3 = one day a week; 4 = two days a week; 5 = three days a week; 6 = four days a week; 7 = five days a week
KM: seven-point Likert scale;
*p < 0.05; **p < 0.001
Source: Author’s own work
Table 5.
Linear and quadratic regressions between telework frequency and KM activity for those with prior telework experience (Japan)
| Variable | ANOVA | Regression equation (linear) |
|---|---|---|
| Knowledge acquisition | F(1,282) = 0.374, p = 0.542 |
Y = 0.037x + 4.944; |
| Knowledge sharing | F (1,282) = 1.597, p = 0.207 |
Y = 0.077x + 4.746; |
| Knowledge application | F(1,282) = 5.041, p = 0.026 |
Y = 0.131x + 4.530; |
Source: Author’s own work
Table 6.
Linear and quadratic regressions between telework frequency and KM activity for those with prior telework experience (USA)
| Variable | ANOVA | Regression equation (linear) |
|---|---|---|
| Knowledge acquisition | F(1,591) = 52.050, p = 0.000 |
Y = 0.252x + 4.089; |
| Knowledge sharing | F(1,591) = 32.301, p = 0.000 |
Y = 0.201x + 4.412; |
| Knowledge application | F(1,591) = 60.747, p = 0.000 |
Y = 0.267x + 4.133; |
Source: Author’s own work
© Emerald Publishing Limited.