Team Psychological Resource as a Critical Enabler for Innovation and Knowledge Creation
Abstract
Previous research has highlighted that psychological capital, as a constructive psychological resource, contributes to organizational behaviors, such as innovative work behavior and knowledge creation, which are inseparable yet distinct concepts. However, these positive relationships have not been consistently observed. High levels of hope, optimism and self-efficacy, which are dimensions of psychological capital, may lead to a decline in performance. Therefore, we develop a framework to investigate the joint effect of team psychological capital and localization of human resources on innovative work behavior and knowledge creation. We empirically assess this framework using questionnaires, and multi-source data from multinational enterprises in China. The results indicate that team psychological capital has a gradually slowing positive effect on innovative work behavior, while simultaneously exhibiting an inverted-U-shaped effect on knowledge creation. Moreover, we further find that the localization of human resources positively moderates the relationship between psychological capital and innovative work behavior. This study contributes to insights into the effects of team psychological capital on innovative work behavior and knowledge creation while offering managers implications on how to align their psychological resources to develop innovative activities.
1. Introduction
Innovation management is crucial in both theoretical academic research and practical enterprise operations. Innovative work behavior (IWB) has emerged as a critical driving force underpinning the growth and expansion of enterprises, which is considered a pressing need and a key source of a sustainable competitive edge (Honorato and de Melo, 2023) permeating various facets of organizational practices (Janssen, 2000; Asbari et al., 2019). Also, Hämäläinen and Inkinen (2019) emphasized that turning IWB into new harmonized functions fits in with the daily life of a company. Janssen (2000) defined IWB which is a complex behavior in the workplace consisting of three tasks: idea generation, idea promotion and idea realization. Since the establishment of the concept (Janssen, 2000), countless studies have investigated the antecedents and consequences of this behavior to explore the paradigm within organizations (Luthans et al., 2011; Abbas and Raja, 2015; Hsu and Chen, 2017; Sameer, 2018; Wu and Chen, 2018; Lei et al., 2020). Among them, there is much research on psychological capital as its antecedent (Bak et al., 2022; Kumar et al., 2022). Psychological capital is comprised of confidence (or self-efficacy), hope, optimism and resilience. Psychological capital transcends human capital and social capital as a core psychological element and has a significant relationship with innovation (Luthans et al., 2004). Prior research also has investigated the linear positive effects of psychological capital on innovation (Abbas and Raja, 2015; Sameer, 2018; Wang et al., 2021; Bak et al., 2022; Kumar et al., 2022). Yet few studies focus on the relationship between team psychological capital (TPC) and IWB at the organizational level (Bak et al., 2022; Kumar et al., 2022). Our study may have unexpected findings. Thus, one core question is what effect TPC has on IWB.
Subsequently, we decided to introduce knowledge creation (KC) as another dependent variable into our research model. KC, a dynamic and spiral process in which tacit and explicit knowledge mutually transform to create new knowledge (Nonaka, 1994), is significant for enterprises to gain advantage (Nonaka and Nishiguchi, 2001). The improvement of TPC not only stimulates knowledgeable employees’ willingness to share and learn (Wang et al., 2021), but it also has a proactive and positive impact on creation (Goswami and Agrawal, 2023). Even though non-knowledgeable employees may not be the primary contributors to KC, they can also adapt more efficiently to rapidly changing work environments, courtesy of improved TPC. These employees are then more willing to understand related knowledge, willingly assist and support the KC progress-even if learning, transforming, or crafting knowledge doesn’t fall directly within their realm of responsibility. However, KC is a long-term and spiral process that requires consistent and collaborative efforts by team members (Lee et al., 2008). When employees have a high psychological capital, the too-much-of-a-good-thing effect of three sub-dimensions (hope, self-efficacy and optimism) of psychological capital on KC may emerge (Luthans et al., 2007; Sweeny and Shepperd, 2010; Sharot et al., 2011; Lee et al., 2019; Li et al., 2020; Purol and Chopik, 2021; Chin et al., 2023). Yet there is limited research that examines the curvilinear effect of psychological capital on KC. Briefly, the exploration of the relationship between TPC and KC presents a valuable academic endeavor.
Additionally, We chose KC as another dependent variable due to its close connection with innovation, a trait which often causes confusion among researchers due to the apparent similarities between the two (Popadiuk and Choo, 2006). Nonaka (1994) regarded innovation as a key form of KC, Popadiuk and Choo (2006) suggested that innovation depends on KC. Whereas some scholars indicated that KC is the enabler and antecedent of innovation (Esterhuizen et al., 2012; Khedhaouria and Jamal, 2015; Sarwat and Abbas, 2021). This is contradictory. Even though previous studies have proposed a coupled model which associates innovation and KC to further distinguish between these two concepts (Popadiuk and Choo, 2006), this field of research is still fragmented and remains to be thoroughly investigated empirically. This limits our understanding and utilization of both concepts. Thus, our other key issue involves studying the contrasting impact of TPC on IWB and KC, to further differentiate between the two. While we must consider the environment that firms are confronting.
Almost every multinational company faces a critical decision: whether to localize or standardize. This strategic choice ultimately determines the company’s respective developmental path (Melewar and Saunders, 1999). Based on social cognitive theory, behavior is influenced by person and environment, that is, we need to pay attention to the interaction among the three elements (Bandura, 1986). Thus, we employed an environmental factor — localization of human resources (LHR) as a moderator role. The LHR refers to the process in which foreign managers are gradually replaced by local managers in the localization process of multinational corporations, thereby helping enterprises to establish and develop locally and enhance the competitiveness of subsidiaries (Wong and Law, 1999). The previous studies suggested that with the progress of LHR, first, the increasing number of local managers makes it easier for them to be accepted by local employees, which can motivate local employees to work harder and be more loyal to the company (Law et al., 2009). Second, after local managers enter the organization, they promote communication between foreign and local managers and the transfer of professional knowledge (Law et al., 2009), which further stimulates the emergence of IWB and KC. However, there is a lack of research on the moderating role of LHR (Wong and Law, 1999). Thus, given the pressing issue which multinationals and academia are facing, we propose LHR — a factor in the organizational environment, to serve as a moderator to improve our study according to the social cognitive theory.
Subsequently, this study seeks to address four key issues:
| (1) | Will TPC affect IWB? if so, how? | ||||
| (2) | Will TPC affect KC? if so, how? | ||||
| (3) | Does any contrast exist in the effects of TPC on KC as compared to IWB? | ||||
| (4) | When LHR serves in a moderating role, how do these two relationships fluctuate? | ||||
We seek to make several contributions to this study. First, we contribute to IWB research by clarifying the relationship between TPC and IWB. Most studies conducted to date have employed psychological capital at the personal level which ignores collective psychological resources — TPC (Tho and Duc, 2021). To address this gap, we serve TPC as the dependent variable.
Second, we employed KC as another dependent variable to further elucidate the distinction between innovation and KC from an empirical perspective. Undoubtedly, both are inseparable and close concepts. However, early research is at variance regarding the relationship between innovation and KC (Popadiuk and Choo, 2006). Therefore, in this study, we complement related research by incorporating IWB and KC to empirically compare the effects of TPC on the two dependents.
Ultimately, given that social cognitive theory postulates the reciprocal determinism of three determinants — person, environment and behavior — it is posited that behavior is not solely influenced by personal cognition, but also significantly shaped by the environment (Bandura, 1986; Zhang et al., 2023), we utilize LHR as the moderator role due to the pressing issue faced by both multinationals and academia. This issue, which includes the dilemma of standardization versus localization, holds considerable urgency for all involved parties (Law et al., 2009). Despite many studies that have investigated the antecedents and consequences of LHR (Latukha and Malko, 2019; Ouyang et al., 2019), we found that most studies have overlooked the moderating role of LHR. This is surprising as environmental factors are indeed important, but they are not the only decisive factors (Bandura, 1986). Accordingly, we propose that TPC with a high degree of LHR are more likely to drive IWB and KC.
We test our framework using data from China’s multinational enterprises. Given the competitive business environment created by globalization and evolving technologies, these entities are driven to innovate and create knowledge frequently (Ardito et al., 2020). Therefore, the sample we chose is particularly well-suited to address our research questions owing to its reliance on globalization trends. The present study is organized as follows. First, the study’s literature review and the hypothesis development are outlined. Then, the research method is presented. After that, the results are discussed. Research implications, conclusions and recommendations for future research are presented.
2. Literature Review and Hypotheses Development
2.1. Team psychological capital and innovative work behavior
IWB is defined as work roles (individual, group or organization) generating, promoting and realizing innovative ideas, transforming them into new products, processes, or services, which will bring tangible or intangible benefits to entities (Janssen, 2000). Some studies since then have drawn on the definition of IWB given by Janssen (2000) such as Bos-Nehles et al. (2017) and Sanz-Valle and Jiménez-Jiménez (2018). As for the measurement of IWB, a six-item scale of IWB was presented by Scott and Bruce (1994) that has been widely utilized and enhanced by other researchers. For instance, Lukes and Stephan (2017) incorporated some of the items, Rinki (2022) developed another scale consisting of 20 items based on it. Recent studies proposed that many factors contribute to IWB, mainly divided into two aspects, external and internal. Among the external factors, emerging technologies are commonly recognized as influential. Surabhi et al. (2022) found that AI-enabled job characteristics impact IWB, leading to a positive influence on job automatic IWB but a negative impact on skill variety. Wu and Yu (2022) explored the mechanisms that influence IWB in the field of information technology. Furthermore, the organizational and country context play significant roles in shaping interpersonal goals and IWB (Shanker et al., 2017; Francesco et al., 2021). Internal factors typically encompass various individual traits, such as emotional intelligence (Zhang et al., 2022), interpersonal goals (Montani et al., 2021) and psychological capital. Specifically, work requires more individual initiative and organizational behavior, rather than passive reactions (Frese and Fay, 2001). Personal proactivity has become increasingly crucial, as proactive work behavior is a prerequisite for individual IWB (Parker and Collins, 2010; Deng et al., 2022). Meanwhile, adequate internal support is crucial in generating the innovative momentum necessary for teams to develop and implement innovative ideas. Accordingly, psychological capital is extremely critical for both individual and organizational development and is considered a core psychological element beyond human capital and social capital (Luthans et al., 2004). Psychological capital consists of four dimensions: self-efficacy, hope, optimism and resilience. It is a positive psychological resource characterized by having the confidence to undertake challenging tasks and exert effort, an optimistic outlook on present and future success, pursuing goals persistently and being able to change the way to achieve them in a timely manner, and not giving up easily in the face of obstacles and being able to bounce back (Luthans et al., 2007). Yomna (2018) examined the positive effects of psychological capital on IWB at the individual level. Similarly, Xabier et al. (2020) suggested that high psychological capital is conducive for employees to improve their interpersonal networks. Besides, some studies have served psychological capital as the mediating and moderating roles to identify its antecedents and consequences (Guo et al., 2018; Raja et al., 2020; Hussain and Shahzad, 2022; Liu et al., 2023).
However, there are few studies that shifted their focus from individual psychological capital to TPC (Tho and Duc, 2021). TPC refers to the “consensus opinion among team members about the collectively perceived psychological capital of the team” (Dawkins et al., 2015). Currently, academia mainly focuses on its positive impact on IWB: On the one hand, when team members collaborate, higher psychological capital will make the entire team more likely to generate new ideas and opportunities, and more efficient in promoting the implementation of ideas (Thayer et al., 2018). On the other hand, when putting their new ideas into practice, teams might encounter setbacks and obstacles. Higher TPC signifies a better outlook and expectations for the future. When faced with setbacks, the team remains resilient and quickly regains its momentum (Huang and Luthans, 2015; Hsu and Chen, 2017). Also, according to social contagion theory, the behaviors, attitudes, or emotional states of team members will spread and influence each other within their groups (Degoey, 2000). Therefore, the positive effects caused by team members’ psychological capital may continuously influence each other within the team, making it easier for the whole team to produce innovative behaviors (Dawkins et al., 2018). Not surprisingly, the higher TPC, the more chances organizations possess to develop IWB.
| H1: | TPC is positively associated with IWB. | ||||
2.2. Team psychological capital and knowledge creation
Nonaka (1994) proposed dividing knowledge into two types: explicit and tacit. Organizations create new knowledge through the interaction between explicit and tacit knowledge, which is known as KC. There are four interactive modes: socialization (conversion of tacit knowledge to tacit knowledge), externalization (conversion of tacit knowledge to explicit knowledge), combination (conversion of explicit knowledge to explicit knowledge) and internalization (conversion of explicit knowledge to tacit knowledge). This is the SECI model. Effective KC depends on a favorable environment (Nonaka and Nishiguchi, 2001). Buzzwords like “artificial intelligence”, “big data” and “VUCA” (volatile, unprecedented, complex and ambiguous) illustrate a disruptive trend for firms. Digitalization is overturning traditional knowledge management processes (Xu et al., 2006; Zhang and Jasimuddin, 2015), offering opportunities for firms to mitigate risks and seize new prospects (Gong et al., 2023). A recent study by Polenghi et al. (2022) demonstrated the value of knowledge reuse during ontology development. However, previous research has overlooked the significant role played by internal mental resources in the knowledge process.
Furthermore, recent studies have shown increasing interest in the relationship between psychological capital and KC. For instance, Goswami and Agrawal (2023) found that ethical leadership and psychological capital promote KC. Similarly, Tho and Duc (2021) explored the potential of TPC in a team learning context, which is positively associated with the knowledge management processes, including KC.
But this may not be the case. First, according to the conservation of resources (COR) theory, when people possess more resources, they will continue to provide opportunities for people to obtain other resources (Hobfoll, 1989). Psychological capital is considered a core psychological element beyond human capital and social capital (Luthans et al., 2004). Teams possessing this resource may engage in activities such as KC to acquire knowledge, which is another type of resource. However, for non-knowledge-intensive employees, their ability to create knowledge may be limited due to their educational background (Fasco, 2001) and the constraint of their job demands (Axtell et al., 2000). Therefore, if they expend too many resources on creating knowledge, it may result in diminishing returns, thus it’s not worth the effort. Instead, they are likely to prioritize their assigned work, which could, in turn, reduce the resources available for KC. Second, psychological capital is comprised of four dimensions: hope, optimism, self-efficacy and resilience. Surprisingly, existing studies have suggested that these sub-dimensions are not always positively associated with KC (Luthans et al., 2007; Sweeny and Shepperd, 2010; Lee et al., 2019; Li et al., 2020; Purol and Chopik, 2021; Nguyen et al., 2022; Chin et al., 2023). For instance, when employees have high hope, they may adopt an unfavorable mindset in which the end justifies the means (Chin et al., 2023). This could lead them to be guided by unrealistic goal-oriented perspectives where they might choose paths that contradict their ethical values, sense of social responsibility, or obligations towards the organization and its stakeholders (Luthans et al., 2007). Similarly, optimism, another sub-dimension of TPC, possibly demonstrates an unfavorable impact on KC in certain conditions. On the one hand, optimism may lead to greater disappointment when the results fall short of expectations (Sweeny and Shepperd, 2010; Purol and Chopik, 2021). On the other hand, excessive optimism can lead organizations to underestimate the challenges and risks they face (Sharot et al., 2011; Purol and Chopik, 2021). Following this, certain researchers have discovered an inverted-U-shaped relationship between self-efficacy and variables closely related to KC, such as knowledge sharing (Nguyen et al., 2022) and creativity (Lee et al., 2019), among others. Furthermore, Chin (2023) has also elucidated the curvilinear association between self-efficacy and KC from a Yin–Yang dialectical perspective. Based on the discussion above, our hypothesis is proposed as:
| H2: | There is an inverted U-shaped effect of TPC on KC, that is, as the TPC increases, KC shows a curve of rising first and then declining. | ||||
2.3. Moderating role of localization of human resources
When enterprises expand internationally, they encounter a crucial dilemma: localization versus standardization. The implementation direction of this strategy determines the different development of the enterprise (Melewar and Saunders, 1999). Therefore, we select the LHR as a key moderating variable, filling the vital gap in the literature concerning this type of variable. LHR is defined as a process in which expatriate managers sent by multinational companies to subsidiaries or projects are gradually replaced by local managers in order to gain localization advantages (Wong and Law, 1999). During this process, as the number and proportion of local manager’s increase, the organization’s localization advantage gradually emerges, making it more adaptable to the local market, improving relationships with the government and gaining a better understanding of local consumers (Law et al., 2009).
First, LHR enables enterprises to actively allocate resources, such as psychological capital, towards activities like innovation, thereby preserving their competitive advantage during local development, rather than being compelled to invest resources in the localization process to assimilate into the local environment (Law et al., 2009). Moreover, changes in organizational structure like LHR may lead to corresponding changes in organizational cultural intelligence (Moon, 2010). LHR enhances the involvement of individuals with diverse cultural backgrounds within the organization, which can have a positive impact on the cross-cultural adaptation capability among organizational members, facilitating smoother communication between members from different cultural backgrounds. Finally, when individuals with different cultural backgrounds come together to work in the same team, the diversity among team members will encourage divergent thinking and trigger creative cognitive processes (West, 2002). Team members with diverse backgrounds increase the presence of minority viewpoints, making the team more likely to generate novel solutions (Khedhaouria and Jamal, 2015). All these aspects provide advantageous conditions for the relationship between TPC and IWB. Thus, the following hypothesis is proposed:
H3a: LHR positively moderates the positive relationship between TPC and IWB. | |||||
Additionally, we believe that the relationship between KC and IWB is inseparable. So, we wonder whether LHR can moderate TPC-KC as well as TPC-IWB. Correspondingly, Minbaeva et al. (2003) and Fang et al. (2007) have suggested that high LHR would obviously improve KC. On the one hand, LHR guides businesses to deepen their comprehension of the local market and effectively absorb local knowledge, thereby bolstering their ability for knowledge absorption and KC. On the other hand, local employees maintain effective communication and cooperation with every entity in the external environment (Zhang et al., 2022). This constant interaction provides robust support for knowledge innovation and sharing within the enterprise (Minbaeva et al., 2003; Fang et al., 2007). Following that, some researchers also indicated that LHR has a strong connection with knowledge-related factors (Harvey et al., 2001; Hong and Nguyen, 2009; Luo and Zhao, 2013). Hong and Nguyen (2009) have suggested that LHR promotes knowledge transfer and sharing, which is the foundation of KC. The implementation of LHR can also enhance the effectiveness of political strategies (Luo and Zhao, 2013), and contribute to maintaining a competitive advantage (Harvey et al., 2001). Based on these arguments, we propose:
H3b: LHR positively moderates the inverted-U-shaped relationship between TPC and KC. | |||||
The research model is presented in Fig. 1.
Fig. 1. Model of the framework.
3. Research Methodology
3.1. Data collection and sample characteristics
We conducted a questionnaire to collect data and information for testing the hypotheses. The sample includes multinational enterprises from China in various industries such as tourism, hotels, tobacco and engineering. Previous research has examined the antecedents and consequences of psychological capital at the individual level (Hsu and Chen, 2017; Bak et al., 2022; Kumar et al., 2022; Chin et al., 2023; Goswami and Agrawal, 2023). However, our research focuses on the organization level rather than a specific group within the individual level. To ensure the validity of the data and avoid any misunderstanding caused by translation from English into Chinese, we first conducted a pilot study of 30 students as a small sample, to ensure an accurate understanding of each questionnaire item. This small data sample was eliminated from the final data. After revising the items, with the assistance of the Yunnan Chamber of Commerce directories and the Department of Commerce of Yunnan Province, the questionnaires were sent to 1156 employees who work in China’s multinational enterprises, such as Power China and Yunnan Provincial Overseas Investment Co., Ltd. The survey used a 5-point Likert scale for measurement (1=strongly disagree; 5=strongly agree). The survey was conducted over a span of 107 days, from January 2023 to early April 2023. We ultimately collected 298 questionnaires, resulting in a response rate of 25.78%. After removing any invalid questionnaires, the final count of usable responses amounted to 206 (Table 1). In our questionnaire data, the majority of respondents have education levels below undergraduate, constituting 48.1% of the total. Regarding job positions, employees other than managers and R&D personnel are the most common, comprising 41.7%. In terms of industry type, the secondary industry, apart from the primary industry and the tertiary industry, is the most common, making up 60.2%.
| Variables | Classification | N | Percent |
|---|---|---|---|
| Gender | Male | 127 | 61.7 |
| Female | 79 | 38.3 | |
| Age | <25 | 40 | 19.4 |
| 26–30 | 36 | 17.5 | |
| 31–40 | 50 | 24.3 | |
| 41–50 | 46 | 22.3 | |
| 51–60 | 18 | 8.7 | |
| >60 | 16 | 7.8 | |
| Education | Below degree | 99 | 48.1 |
| Undergraduate degree | 67 | 32.5 | |
| Postgraduate | 29 | 14.1 | |
| Doctorate | 11 | 5.3 | |
| Industry type | Primary industry | 14 | 6.8 |
| Secondary industry | 124 | 60.2 | |
| Tertiary industry | 68 | 33.0 | |
| Type of company | State-owned enterprise | 105 | 51 |
| Private enterprise | 32 | 15.5 | |
| Foreign enterprise | 37 | 18 | |
| Government or institution | 18 | 8.7 | |
| Others | 14 | 6.8 | |
| Position | Directors or managers | 46 | 22.3 |
| R&D staff | 74 | 35.9 | |
| Others | 86 | 41.7 | |
| Company size | 1–99 | 14 | 6.8 |
| 100–499 | 52 | 25.2 | |
| 500–1999 | 80 | 38.8 | |
| 2000–4999 | 38 | 18.4 | |
| More than 5000 | 22 | 10.7 |
3.2. Measures
3.2.1. Innovative work behavior
The scale we employed to measure IWB was adapted from González-Romá et al. (2009) innovation scale. To enhance the response rate of the questionnaires, we followed the approach of Le Blanc et al. (2021), where we selected three items from the scale. These items include: “In my team, individuals utilize their knowledge and skills to implement new working methods, new services, or new products”, “In my team, individuals frequently put forth new ideas to enhance the quality of their work results” and “In my team, we regularly experiment with novel ideas and methods”. Respondents were provided with five response options ranging from 1 (totally false) to 5 (totally true). The construct’s Cronbach’s alpha coefficient was calculated at α=0.946.
3.2.2. Knowledge creation
We utilized the renowned scale developed by Nonaka (1994) within the realm of KC. This scale comprises 20 items across four dimensions, and respondents used a 5-point Likert scale (ranging from 1, “totally false” to 5, “totally true”) for their assessments. The calculated Cronbach’s alpha coefficient for the construct is α=0.938.
3.2.3. Team psychological capital
The measurement of TPC comprises sixteen items organized into four dimensions, evaluated using a 5-point Likert scale ranging from 1 (totally false) to 5 (totally true). These four dimensions encompass team self-efficacy, team hope, team resilience and team optimism, each consisting of four items, resulting in a total of 16 items. For instance, items include: “Our team is confident in analyzing long-term problems to find solutions” (team self-efficacy) and “If our team encounters challenges at work, we can generate numerous strategies to overcome them” (team hope). The calculated Cronbach’s alpha coefficient for the construct is α=0.954.
3.2.4. Localization of human resources
We employed the LHR scale developed by Law et al. (2009), comprising seven items and employing a 5-point Likert scale (ranging from 1, “totally false” to 5, “totally true”). The calculated Cronbach’s alpha coefficient for the construct is α=0.929.
3.3. Controls
Six sets of control variables were included in the analysis. We controlled for gender, age, position, education, type of company and type of industry, as these factors are likely to influence IWB (Fasco, 2001). The data was collected from the demographic section of the survey. The responses were coded as follows: Gender [1=male, 2=female]; Age [1=<25, 2=26–30, 3=31–40, 4=41–50, 5=51–60, 6=>60]; Position [1 = Directors or managers, 2=R&D staff, 3=Others]; Education [1=below degree, 2=undergraduate degree, 3=postgraduate, 4=Doctorate]; Type of company [1=state-owned enterprise, 2=private enterprise, 3=foreign-funded and joint-capital enterprises, 4=Government or public institution]; Type of industry [1=primary industry, 2=secondary industry, 3=tertiary industry].
3.4. Validity and reliability tests
Table 2 displays the findings related to the reliability and validity of our scales. Within this table, each measurement model exhibits a composite reliability (CR) exceeding 0.9, and the average variance extracted (AVE) is higher than 0.43, signifying an acceptable level of convergent validity.
| Construct | Item | Standardized estimate | Unstandardized estimate | S.E. | T-value | P | SMC | C.R. | AVE | Cronbach’s α |
|---|---|---|---|---|---|---|---|---|---|---|
| TPC | TPC1 | 0.841 | 1 | 0.311 | 0.955 | 0.573 | 0.954 | |||
| TPC2 | 0.813 | 0.971 | 0.043 | 22.796 | *** | 0.683 | ||||
| TPC3 | 0.801 | 0.921 | 0.042 | 22.074 | *** | 0.642 | ||||
| TPC4 | 0.826 | 0.976 | 0.042 | 23.467 | *** | 0.66 | ||||
| TPC5 | 0.82 | 0.988 | 0.043 | 23.026 | *** | 0.708 | ||||
| TPC6 | 0.835 | 1.016 | 0.043 | 23.9 | *** | 0.553 | ||||
| TPC7 | 0.791 | 0.916 | 0.043 | 21.366 | *** | 0.625 | ||||
| TPC8 | 0.744 | 0.86 | 0.045 | 19.07 | *** | 0.696 | ||||
| TPC9 | 0.746 | 0.874 | 0.046 | 19.142 | *** | 0.672 | ||||
| TPC10 | 0.74 | 0.893 | 0.047 | 18.855 | *** | 0.45 | ||||
| TPC11 | 0.715 | 0.851 | 0.048 | 17.817 | *** | 0.524 | ||||
| TPC12 | 0.722 | 0.837 | 0.046 | 18.12 | *** | 0.504 | ||||
| TPC13 | 0.71 | 0.829 | 0.047 | 17.655 | *** | 0.521 | ||||
| TPC14 | 0.724 | 0.865 | 0.047 | 18.229 | *** | 0.511 | ||||
| TPC15 | 0.671 | 0.8 | 0.049 | 16.194 | *** | 0.547 | ||||
| TPC16 | 0.558 | 0.644 | 0.052 | 12.466 | *** | 0.556 | ||||
| IWB | IWB1 | 0.957 | 1 | 0.916 | 0.949 | 0.862 | 0.946 | |||
| IWB2 | 0.956 | 0.99 | 0.022 | 44.299 | *** | 0.914 | ||||
| IWB3 | 0.868 | 0.918 | 0.03 | 30.117 | *** | 0.754 | ||||
| KC | KC1 | 0.712 | 1 | 0.507 | 0.939 | 0.438 | 0.938 | |||
| KC2 | 0.693 | 0.997 | 0.072 | 13.862 | *** | 0.48 | ||||
| KC3 | 0.733 | 0.843 | 0.058 | 14.557 | *** | 0.537 | ||||
| KC4 | 0.642 | 0.685 | 0.054 | 12.739 | *** | 0.413 | ||||
| KC5 | 0.628 | 0.666 | 0.054 | 12.454 | *** | 0.394 | ||||
| KC6 | 0.685 | 0.72 | 0.053 | 13.601 | *** | 0.469 | ||||
| KC7 | 0.655 | 0.695 | 0.053 | 13.007 | *** | 0.429 | ||||
| KC8 | 0.706 | 0.77 | 0.055 | 14.042 | *** | 0.498 | ||||
| KC9 | 0.663 | 0.751 | 0.057 | 13.153 | *** | 0.439 | ||||
| KC10 | 0.739 | 0.866 | 0.059 | 14.648 | *** | 0.546 | ||||
| KC11 | 0.658 | 0.707 | 0.054 | 13.033 | *** | 0.432 | ||||
| KC12 | 0.711 | 0.788 | 0.056 | 14.122 | *** | 0.506 | ||||
| KC13 | 0.69 | 0.764 | 0.056 | 13.718 | *** | 0.476 | ||||
| KC14 | 0.734 | 0.792 | 0.054 | 14.582 | *** | 0.539 | ||||
| KC15 | 0.674 | 0.747 | 0.056 | 13.379 | *** | 0.455 | ||||
| KC16 | 0.682 | 0.72 | 0.053 | 13.519 | *** | 0.465 | ||||
| KC17 | 0.605 | 0.686 | 0.057 | 12.025 | *** | 0.367 | ||||
| KC18 | 0.605 | 0.619 | 0.052 | 11.998 | *** | 0.366 | ||||
| KC19 | 0.535 | 0.579 | 0.055 | 10.594 | *** | 0.286 | ||||
| KC20 | 0.398 | 0.446 | 0.056 | 7.895 | *** | 0.159 | ||||
| LHR | LHR1 | 0.671 | 1 | 0.45 | 0.929 | 0.653 | 0.929 | |||
| LHR2 | 0.77 | 1.167 | 0.081 | 14.364 | *** | 0.593 | ||||
| LHR3 | 0.84 | 1.336 | 0.087 | 15.297 | *** | 0.706 | ||||
| LHR4 | 0.86 | 1.355 | 0.087 | 15.497 | *** | 0.74 | ||||
| LHR5 | 0.865 | 1.364 | 0.088 | 15.444 | *** | 0.748 | ||||
| LHR6 | 0.846 | 1.358 | 0.089 | 15.185 | *** | 0.716 | ||||
| LHR7 | 0.788 | 1.246 | 0.087 | 14.342 | *** | 0.621 |
3.5. Descriptive statistics and correlations
We performed a statistical analysis of the questionnaire, and the findings are presented in Table 3. Within Table 3, you can find the means, standard deviations and correlations among the variables. Based on the Pearson correlation test, significant correlations exist among TPC, IWB, LHR and KC (P<0.01). Consequently, preliminary support for the relationships among the dependents, independent, and moderating variables in this study has been established, allowing for further hypothesis testing.
| Mean | SD | Gender | Age | Education | Industry type | Type of company | Position | TPC | KC | LHR | IWB | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Gender | 1.410 | 0.493 | 1.000 | |||||||||
| Age | 3.180 | 1.470 | 0.306** | 1.000 | ||||||||
| Education | 1.770 | 0.887 | −0.039 | −0.243** | 1.000 | |||||||
| Industry Type | 2.270 | 0.578 | −0.080 | −0.100* | 0.089 | 1.000 | ||||||
| Type of company | 2.070 | 1.294 | 0.033 | 0.092 | 0.489** | −0.117* | 1.000 | |||||
| Position | 2.200 | 0.789 | −0.221** | −0.100* | −0.092 | 0.138** | 0.014 | 1.000 | ||||
| TPC | 2.992 | 0.897 | −0.061 | −0.089 | 0.068 | 0.078 | −0.079 | 0.034 | 1.000 | |||
| KC | 2.734 | 0.601 | 0.097* | 0.022 | −0.041 | −0.091 | 0.059 | −0.067 | −0.173** | 1.000 | ||
| LHR | 2.989 | 0.909 | −0.147** | −0.213** | 0.073 | 0.037 | 0.021 | 0.021 | 0.091 | 0.026 | 1.000 | |
| IWB | 3.031 | 1.141 | 0.112* | 0.033 | −0.041 | −0.079 | 0.045 | −0.061 | −0.312** | 0.856** | 0.062 | 1.000 |
4. Results
Hierarchical regression is a multiple regression analysis method which builds the model in a step-by-step manner while incorporating independent variables into the model (Cohen et al., 2013). This method could be applied in our study to explore interactions among multiple variables.
The regression outcomes are displayed in Table 4. In the preliminary phase of the regression analysis (Model 1), we encompassed all control variables, including gender, age, and so on. In the ensuing stage (Model 2), we introduced the independent variable TPC. Moreover, to delve into the association between the independent variable and the dependent variable IWB, we integrated the squared term of TPC into Model 3. Ultimately, for scrutinizing the moderating impact of LHR, we integrated both the interaction term between the independent variable and LHR, and the second-order interaction term.
The results revealed that both TPC (β=3.771, p<0.001) and TPC2 (β=−3.202, p<0.001) exerted a significant influence on IWB. Furthermore, LHR significantly moderated the relationship between the independent and dependent variables. Both the first-order interaction term between the independent variable and LHR (β=−4.616, p<0.01) and the second-order interaction term (β=3.860, p<0.01) displayed obvious effects. As depicted in Fig. 2, TPC exhibited a positive effect on IWB, thereby supporting H1. Illustrated in Fig. 3, a considerable degree of moderating influence from LHR transformed the connection between TPC and IWB. It changed from a gradually slowing positive effect to a regularly positive effect, offering support for H3a.
Fig. 2. The gradually slowing positive relationship between TPC and IWB.
Fig. 3. LHR moderates the relationship between TPC and IWB.
| IWB | KC | |||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | |||||||||||||||||
| Variables | β | t | p | β | t | p | β | t | p | β | t | p | β | t | p | β | t | p | β | t | p | β | t | p |
| Gender | 0.064 | 0.864 | 0.388 | −0.003 | −0.051 | 0.960 | −0.001 | −0.019 | 0.985 | −0.008 | −0.148 | 0.883 | 0.100 | 1.361 | 0.175 | 0.042 | 0.730 | 0.467 | 0.034 | 0.636 | 0.526 | 0.028 | 0.522 | 0.603 |
| Age | −0.022 | −0.287 | 0.775 | 0.006 | 0.107 | 0.915 | 0.007 | 0.134 | 0.894 | 0.012 | 0.220 | 0.826 | −0.009 | −0.114 | 0.909 | 0.015 | 0.263 | 0.793 | −0.024 | −0.431 | 0.667 | −0.022 | −0.396 | 0.692 |
| Education | 0.067 | 0.766 | 0.445 | −0.009 | −0.149 | 0.882 | −0.009 | −0.142 | 0.887 | 0.004 | 0.063 | 0.950 | 0.038 | 0.433 | 0.665 | −0.029 | −0.430 | 0.668 | −0.026 | −0.413 | 0.680 | −0.021 | −0.334 | 0.738 |
| Industry type | −0.010 | −0.142 | 0.887 | −0.024 | −0.472 | 0.637 | −0.023 | −0.461 | 0.645 | −0.020 | −0.398 | 0.691 | −0.040 | −0.551 | 0.582 | −0.052 | −0.923 | 0.357 | −0.043 | −0.821 | 0.413 | −0.044 | −0.856 | 0.393 |
| Type of company | −0.084 | −0.975 | 0.331 | 0.022 | 0.366 | 0.715 | 0.020 | 0.332 | 0.740 | −0.006 | −0.092 | 0.927 | −0.023 | −0.263 | 0.793 | 0.071 | 1.055 | 0.293 | 0.071 | 1.147 | 0.253 | 0.060 | 0.966 | 0.335 |
| Position | 0.029 | 0.396 | 0.693 | 0.031 | 0.615 | 0.539 | 0.032 | 0.626 | 0.532 | 0.035 | 0.705 | 0.481 | 0.025 | 0.341 | 0.734 | 0.027 | 0.477 | 0.634 | 0.019 | 0.368 | 0.714 | 0.022 | 0.428 | 0.669 |
| TPC | 0.726*** | 14.574 | 0.000 | 0.851** | 3.428 | 0.001 | 3.771*** | 4.204 | 0.000 | 0.639*** | 11.548 | 0.000 | 2.120*** | 8.378 | 0.000 | 3.583*** | 3.840 | 0.000 | ||||||
| TPC2 | −0.130 | −0.523 | 0.601 | −3.202*** | −3.369 | 0.0009 | −1.499*** | −5.912 | 0.000 | −3.173* | −3.210 | 0.002 | ||||||||||||
| LHR | 0.012 | 0.215 | 0.830 | 1.411** | 3.145 | 0.002 | −0.071 | −1.293 | 0.198 | 0.517 | 1.108 | 0.269 | ||||||||||||
| TPC*LHR | −4.616** | −3.338 | 0.001 | −2.182 | −1.517 | 0.131 | ||||||||||||||||||
| TPC2*LHR | 3.860** | 3.382 | 0.001 | 2.001 | 1.685 | 0.094 | ||||||||||||||||||
| R2 | 0.010 | 0.522 | 0.523 | 0.550 | 0.012 | 0.409 | 0.504 | 0.513 | ||||||||||||||||
| Adjusted R2 | −0.020 | 0.505 | 0.501 | 0.524 | −0.018 | 0.388 | 0.482 | 0.485 | ||||||||||||||||
| ΔR2 | 0.010 | 0.513 | 0.001 | 0.027 | 0.012 | 0.398 | 0.095 | 0.008 | ||||||||||||||||
| F | 0.320 | 30.91*** | 23.870*** | 21.513*** | 0.386 | 19.602*** | 22.169*** | 18.555*** | ||||||||||||||||
Subsequently, we proceeded to examine the effect on KC. In the initial stage (Model 5), all control variables were introduced. Following that (Model 6), TPC was integrated. Next, to explore the possibility of an inverted U-shaped relationship between the independent variable and KC, we incorporated TPC, TPC2 and the moderating role into Model 7. Finally, to test H3b, we added the interaction terms to Model 8.
The results revealed that both TPC (β=3.583, p<0.001) and the square of TPC (β=−3.173, p<0.05) exhibited significant effects on KC, indicating an inverted U-shaped relationship between TPC and KC (Fig. 4). This finding supported H2. However, in contrast to IWB, LHR did not act as a moderator in the relationship between TPC and KC. As indicated in Table 4, neither LHR nor the interaction term of TPC demonstrated any impact on KC, leading to the rejection of H3b.
Fig. 4. The inverted-U-shaped relationship between TPC and KC.
5. Discussion and Implications
5.1. Discussion
To identify the associations among TPC, KC and IWB, this study explored the moderating roles of LHR in China’s multinational enterprises. Apart from H3b, all other hypotheses are supported, which is an unexpected yet gratifying discovery. In detail, first, we examined the gradually slowing positive effect of TPC on IWB rather than the linear positive effect of prior studies. Second, we first identified the inverted-U-shaped relationship between TPC and KC, which was considered as positive relationship previously. Additionally, the different effects on IWB and KC prove the difficulty in uniting knowledge and practice. Finally, we introduced the LHR as a moderator, highlighting its previously overlooked role in influencing the relationship — an essential environmental factor. However, we are supposed to discuss the following questions: To begin with, the gradually slowing positive relationship between TPC and IWB is first identified, so why does the relationship between the two slow down? Following that, why does TPC have different effects on two dependent variables? Ultimately, why does LHR have no moderating effect on the relationship between TPC and KC?
First, the relationship between psychological capital as a positive psychological resource and IWB has consistently shown a positive association in previous studies (Huang and Luthans, 2015; Hsu and Chen, 2017; Thayer et al., 2018). However, our research has revealed a distinctive finding: the relationship manifests as a gradually diminishing positive effect. This indicates that psychological capital may not always be universally advantageous for IWB. The following aspects we propose may influence the effect. First of all, innovation is a high-risk process and people who are involved in generating novel and useful ideas often fail (Carmeli and Schaubroeck, 2007). While optimism is a dimension of psychological capital, excessive optimism can lead entities to underestimate the risks and challenges inherent in the innovation process. This can lead groups to be unprepared when confronted with obstacles and difficulties, ultimately impacting their innovation performance (Sharot et al., 2011; Purol and Chopik, 2021; Chin et al., 2023). Moreover, upon reviewing our sample characteristics, we observed that the most common participants are employees other than managers and R&D staff, constituting 41.7% of the sample. This group might lack the necessary authority to implement innovative behavior. It is known that the promotion and implementation of innovative ideas face fewer internal obstacles when accompanied by the appropriate power (Axtell et al., 2000). Subsequently, a significant portion of the respondents, comprising 48.1% of the total, possess education levels below undergraduate. This demographic possibly encounters limitations in their ability to innovate due to their educational backgrounds (Fasco, 2001). Additionally, these employees possibly have comparatively less exposure to innovation activities when compared to managers and R&D staff. Furthermore, the type of organizations may also play a significant role in influencing this relationship (Anderson and West, 1998). In our sample, 50% of the respondents are employed by state-owned companies. China’s state-owned enterprises tend to exhibit a lower innovation vitality in comparison to private businesses, as the latter must cultivate competitive advantages within a competitive market (Aihua et al., 2021; Jiang et al., 2022b; Zeng et al., 2022). Thus, this dynamic could potentially hinder the innovation progress within state-owned companies.
Second, we posit that KC and concepts related to innovation are interconnected yet distinct. Taking into account the distinct focuses of these two concepts, KC plays a more favorable role in generating innovation (Al-Omoush et al., 2020). However, IWB comprises idea generation, idea promotion and idea implementation, all of which are vital to IWB (Janssen, 2000). Our intention is not to suggest that KC is a subset of IWB, but rather to illustrate that the connection between KC and IWB can be better described as an intersection. Additionally, as the TPC increases in the early stages, the employees’ work engagement increases (Sweetman et al., 2011; Alessandri et al., 2018). Thus, we suggest that performance is the employees’ primary goal, and the creation of new knowledge could be viewed as an additional result of diligently completing their work rather than the primary goal. That is to say, as ordinary employees increasingly focus on job performance, they might decrease their engagement in KC activities, particularly if their roles are not inherently knowledge-intensive. And with the further improvement of TPC, they may approach their tasks more diligently. However, the situation is distinct for innovation, as these ordinary employees might contribute to supporting and assisting innovation efforts through their completion of routine tasks, even if they are not positioned in knowledge-intensive roles.
Finally, we propose that the reason why the moderating effect of LHR on TPC-KC is not significant is that the positive moderating effect and the negative moderating effect cancel each other out. Although LHR supports the growth of entities by deepening their understanding of the local market and facilitating effective communication and cooperation with external entities (Minbaeva et al., 2003; Fang et al., 2007), the heterogeneity of team members such as country and culture may lead to more severe internal conflicts within the team (Zhang et al., 2022). People from different cultural backgrounds tend to categorize themselves and exhibit out-group biases (Ashforth and Mael, 1989), which hinders communication and knowledge sharing (Zhang et al., 2023). Another important factor to consider is the basis of communication. The diversity of languages spoken when the team members are from different countries may also cause obstacles in information dissemination (Greenberg, 1956; Li et al., 2017), further exacerbating the difficulty of KC.
5.2. Theoretical implications
From a theoretical perspective, first, we identified the gradually slowing positive effect of TPC on IWB. Previous studies suggested that psychological capital has a linear effect on IWB (Abbas and Raja, 2015; Sameer, 2018; Wang et al., 2021; Bak et al., 2022; Kumar et al., 2022), whereas this is not always the case in some situations. Our study indicated a distinct viewpoint, which enhances the possibility of further research on innovation from psychological capital perspective.
Following that, we have further enriched the study of team-level psychological capital and filled the void left by the team-level’s absence. Previous studies have primarily focused on individual-level psychological capital (Abbas and Raja, 2015; Tang et al., 2019; Lei et al., 2020; Wang et al., 2021). However, in many cases, individuals are integral members of teams, working collaboratively to create value. Furthermore, we have found that this variable has different impacts on IWB and KC than previously reported. This contributes significantly to enriching the academic understanding of psychological capital.
Moreover, there is a widespread belief that psychological capital, as a constructive psychological resource, can consistently provide advantages for KC (Goswami and Agrawal, 2023). Although prior research also proposed that high hope, optimism and self-efficacy, as three critical components of psychological capital, have a curvilinear relationship with KC (Luthans et al., 2007; Sweeny and Shepperd, 2010; Sharot et al., 2011; Lee et al., 2019; Li et al., 2020; Purol and Chopik, 2021; Chin et al., 2023), no research probe into the effects of TPC on KC. Thus, we first identified the curvilinear relationship between TPC and KC, which indicates high TPC may not promote KC. This contributes to insights into the unexpected effects of psychological capital as a construct.
Additionally, the different effects on IWB and KC prove the difficulty in uniting knowledge and practice. We empirically compared IWB with KC which are inseparable concepts from a mental resource perspective. The relationship between the two has always puzzled scholars (Popadiuk and Choo, 2006). By combing through the literature, academics are mainly divided into two schools of thought regarding the relationship between KC and innovation. One school of scholars believed that the relationship between KC and innovation is interactive and complementary (Popadiuk and Choo, 2006; Essmann, 2009; Esterhuizen et al., 2012). The other school of scholars believed that knowledge constitutes the basis of innovation and is one of the antecedents of innovation (Esterhuizen et al., 2012; Khedhaouria and Jamal, 2015; Sarwat and Abbas, 2021). However, no previous study has considered both as dependent variables simultaneously. Therefore, we utilized the different influence of TPC on them to further distinguish the differences between them.
Finally, based on social cognitive theory, we focused on the moderating role of LHR, which positively moderates the relationship between TPC and IWB. These findings advance research in this field and provide valuable insights into the localization process, enriching Bandura’s reciprocal determinism from an empirical perspective.
5.3. Practical implications
From a practical perspective, this study also provides implications for enterprises and team managers. First, the differential effects of TPC on IWB and KC suggest that attention should be paid not only to the driving force of KC but also to the promotion and implementation of ideas after their generation (Axtell et al., 2000). It is strategic to maintain an encouraging attitude towards the proposal of new ideas and improve the feasibility evaluation of these ideas. Furthermore, the findings indicate that LHR has a significant stimulating effect on IWB. This suggests that LHR is not only promotive for building local relationship networks in the host country and improving organizational performance (Wall, 1990; Law et al., 2009), but also plays a crucial role in organizational innovation.
6. Conclusion, Limitations and Future Research
This paper aimed to investigate whether TPC act as an enabler for IWB and KC and explore the potential dissimilarity between the two dependents from the empirical perspective.
First, we identified a gradually slowing positive effect of TPC on IWB, which differs from the results of previous studies. Furthermore, we revealed that TPC has an inverted-U-shaped role in KC, which is supported by prior research (Chin et al., 2023). Additionally, since TPC has different effects on IWB and KC, we differentiated between the two from an empirical perspective. Moreover, this study explored the stimulating role of LHR in the relationship between TPC and IWB. This unique research serves as a foundation and guide for future studies.
This study inevitably has several limitations in data collection but provides opportunities for further research. The questionnaire did not specify the types of companies (private or state-owned) or the positions of employees. To explore further variations in this relationship, future research may focus on ordinary employees. Additionally, the data sets in this study mainly come from China’s multinational corporations. Future research could include multinational corporations from other countries as subjects of the study. Furthermore, based on our research, we propose some future perspectives. First, LHR is a significant organizational transformation (Wong and Law, 1999), which may impact performance in the host country and even the management and operation of the parent company. Exploring the boundary conditions for stimulating innovation through LHR and examining other factors in the context of rapid regional economic integration have meaningful implications. Moreover, the innovation of ordinary employees is crucial (Axtell et al., 2000), and exploring how to promote and implement their innovation is also worthwhile. Lastly, although the literature has systematically distinguished the concepts and relationships between KC and innovation (Popadiuk and Choo, 2006), our study found different impacts of TPC on KC and IWB, as well as the moderating effects of LHR. This suggests that further differentiation of the related variables of KC and innovation as well as empirical research under different backgrounds are needed.
Acknowledgments
This study was supported by the National Natural Science Foundation of China (Award Nos.: 71972165, 61763048, 72271214 and 71762033), Key Projects of Basic Research for Science and Technology Foundation of Yunnan Province (No. 202001AS070031), the Central Government’s Special Program for Guiding Local Science and Technology Development (No. 202307AB110009) and the Yunnan Philosophy Social Science Foundation (YNPOPSS Award No.: ZD202213). The assistance of Elizabeth Dunlop from CSU of Australia, and Dr. Li Shen from Juniata in America is gratefully acknowledged.
