Team Leadership

IMPACT OF TEAM LEADERSHIP HABITUAL DOMAINS ON AMBIDEXTROUS INNOVATION

XINWEI YE, JUNWEN FENG, LEI MA, AND XIAOJING HUANG Nanjing University of Science and Technology

We studied the formation of leaders’ habitual behaviors and the impact of leaders’ existing and potential capabilities on ambidextrous (i.e., exploitative and exploratory) innovation activities. Leadership habitual domain (LHD) theory was applied from an endogenous perspective to analyze the impact mechanism of LHD on ambidextrous innovation via the mediating role of dynamic capabilities. We used structural equation modeling to test data collected from 205 team leaders in East China. Results showed that LHD was positively associated with both exploitative and exploratory innovation, and that dynamic capabilities mediated these relationships. Thus, team leaders should renew, reconfigure, and expand their LHD by sensing and seizing opportunities when implementing ambidextrous innovation.

Keywords: team leadership, habitual domains, ambidextrous innovation, exploitative innovation, exploratory innovation, dynamic capabilities.

In hypercompetitive environments, enterprises should establish a passive adaptation mechanism to cope with external pressure, and should also form a mechanism to innovate actively (Benner & Tushman, 2003). Jansen, Van Den Bosch, and Volberda (2006) argued that companies need to apply both exploitative and exploratory types of innovation, termed ambidextrous

SOCIAL BEHAVIOR AND PERSONALITY, 2018, 46(12), 1955–1966 © 2018 Scientific Journal Publishers Limited. All Rights Reserved. https://doi.org/10.2224/sbp.7323

1955

Xinwei Ye, School of Economics and Management and Centre for Innovation and Development, Nanjing University of Science and Technology; Junwen Feng, School of Economics and Management, Nanjing University of Science and Technology; Lei Ma, School of Public Affairs and Centre for Innovation and Development, Nanjing University of Science and Technology; Xiaojing Huang, School of Intellectual Property and Centre for Innovation and Development, Nanjing University of Science and Technology. This work was supported by the Project of the National Natural Science Foundation of China (71272164). Correspondence concerning this article should be addressed to Lei Ma, School of Public Affairs, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, Jiangsu 210094, People’s Republic of China. Email: maryma208@sina.com

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innovation, to obtain a long-term competitive advantage. O’Reilly and Tushman (2011, 2013) described the ability to sense and seize new opportunities as a key leadership challenge. Teams are playing an increasingly important role in organizational management, and the amount of research on team leadership reflects this trend (Braun, Peus, Weisweiler, & Frey, 2013; Hoch & Kozlowski, 2014; Morgeson, DeRue, & Karam, 2010). Rosing, Frese, and Bausch (2011) proposed the concept of ambidextrous leadership, that is, integration of opening and closing leader behaviors—which increase and reduce variance in follower behaviors, respectively—and suggested switching between the two to deal with the changing requirements of innovation. However, there has been little research into the formation of leaders’ habitual behaviors and the impact of existing and potential capabilities on innovation activities. In team leadership habitual domains (LHD; Ma, Hu, Ye, & Wang, 2014) theory a new research perspective has been put forward that addresses the leaders’ habitual behaviors and their potential abilities on the basis of their experience, knowledge, and capabilities.

An effective leader needs to foster both exploitation and exploration, and switch flexibly between them (Rosing et al., 2011). Dynamic capabilities are rooted in both exploratory and exploitative activities (Benner & Tushman, 2003). In combination with good strategic leadership, dynamic capabilities allow companies to produce the right products for the right market to satisfy consumers’ needs, and gain promising technologies and a competitive advantage (Teece, 2012). Researchers have explored the impact of leadership preferences on ambidextrous innovation, but neglected to examine whether leaders can change their existing abilities to satisfy a changing environment (Chang & Hughes, 2012; Jansen, Vera, & Crossan, 2009; Lin & McDonough, 2011; Zacher & Rosing, 2015). As flexibility is one characteristic of LHD (Ma et al., 2014), we introduced the mediator of dynamic capabilities to explore how team leaders’ LHD affects ambidextrous innovation, thereby allowing companies to obtain a long-term competitive advantage in a complex and changing environment.

Literature Review and Development of Hypotheses

Leadership Habitual Domains Theory Yu (1980, 1991) stated that each person has a unique set of behaviors rooted

in his or her ways of thinking, forming memories, judging, responding, and handling problems, and that these behaviors tend to stabilize within a certain domain over time. This collection of habitual behaviors, along with its formation, dynamics, and the basis of experience and knowledge, is called one’s habitual domain. Therefore, leadership habitual domains (LHD) refer to an absence of stimuli causing a leader’s knowledge and experience to gradually stabilize within a certain domain, forming a pattern of habitual behavior over a period

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of time. In addition, leaders’ knowledge and experience constitute a potential capability to deal with environmental change (Ma et al., 2014), whereas activated or focused knowledge and experience constitute one’s existing capabilities and form observable habitual behaviors. Leaders can expand or reform their LHD by habitual perception, learning, reflection, and practice; pursuing new leadership skills until they reach a relatively stable state of LHD; and switching between different leader behaviors as needed.

Specific LHD, which guide leaders’ decision making and determine their different leadership styles and leadership behaviors, are formed from individual leaders’ heterogeneous knowledge and experience. For example, Ma Yun, the chief founder of Alibaba Group, originally engaged in translation work and wanted to establish an e-commerce company in China only after realizing the market potential of e-commerce, an idea that changed his LHD. Other Chinese entrepreneurs were not stimulated by the new concept of e-commerce, so it did not become part of their LHD, leading to their making different decisions. Thus, people who have the same leadership style do not necessarily have the same LHD. Leadership style is defined as the habitual behavior that leaders use when influencing subordinates, and is gradually formed through long-term leadership experience and practice. Thus, leadership style is the behavior model of leaders, reflecting and providing a way to understand LHD, whereas LHD comprise a leader’s own attributes.

LHD have three characteristics: flexibility, demonstration, and coadaptation (Ma et al., 2014). Flexibility means that when the internal or external environment changes, a team leader can make a quick response and an effective decision to deal with the change, then return to the original stability. Demonstration refers to the stability of the leader’s behavior often being learned and imitated by their subordinates, with proactive behavior being oriented toward team members. Coadaptation refers to the fact that, because habitual domains vary among team members and between team members and leaders, leaders absorb knowledge and experience from an uncertain external environment, then influence and adapt with team members mutually, constantly improving their own LHD and team members’ habitual domains.

Leadership Habitual Domains and Ambidextrous Innovation Exploitative innovation and exploratory innovation are significant features

of an ambidextrous organization (Jansen et al., 2006). Exploitative innovation is aimed at meeting the existing market and customer needs, and enhancing the organization’s existing skills, processes, and structure on the basis of existing knowledge. It leads to a slight improvement in quality and performance in products and processes, mainly affects the organization’s short-term gains, and makes the organization more competitive (Colombelli, Krafft, & Quatraro,

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2014). In contrast, exploratory innovation is aimed at meeting the emerging market and customer needs, carrying out new designs, developing new markets, or opening up new distribution channels relying on new knowledge. It broadens the breadth of knowledge, produces a series of product and process innovations, and improves the flexibility and diversity of the organization. Exploratory innovation may not be effective in the short term, and the results are not easily predictable (March, 1991). In the process of ambidextrous innovation, leaders should be able to weigh up the conflicts between exploitative and exploratory innovation, and constantly adjust their leadership style to match the required management activities (Jansen et al., 2006; O’Reilly & Tushman, 2011).

Researchers have explored the factors that influence ambidextrous innovation from the perspective of leadership. For example, Jansen et al. (2009) indicated that transformational leadership behaviors contribute significantly to exploratory innovation, whereas transactional leadership behaviors are associated with exploitative innovation. However, Lin and McDonough (2011) argued that the relationship between leadership and innovation is complex. Chang and Hughes (2012) found that risk-taking managers tend toward exploratory innovation and risk-intolerant managers tend toward exploitative innovation, and Zacher and Rosing (2015) verified that team leaders need to engage in both opening and closing behaviors to produce high levels of innovation. Therefore, we proposed the following hypotheses: Hypothesis 1: Leadership habitual domains will have a significantly positive impact on exploratory innovation. Hypothesis 2: Leadership habitual domains will have a significantly positive impact on exploitative innovation.

Leadership Habitual Domains and Dynamic Capabilities Dynamic capabilities (DC) allow a firm to sense and seize opportunities or

threats, and integrate, build, and reconfigure internal and external resources and routines to address rapidly changing environments (Barreto, 2010). DC are rooted in high-level routines and analytical methodologies that determine the speed and degree of aligning particular resources to modify or even transform continuously what the enterprise is doing, in order to match the requirements of the business environment (Teece, 2012; Winter, 2003). Essentially, DC are an adaptation mechanism that enables enterprises to acclimate to dynamic and complex circumstances.

Researchers have pointed out that DC purposely change basic resources under managers’ leadership (Helfat et al., 2007; Teece, 2016). Teece (2016) stated that DC are reliant on the organization’s values, culture, and collective ability, which mainly result from past management efforts and are embedded in the organization’s habitual domains. Unlike ordinary capabilities, DC may

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be based on the skills and knowledge of leaders rather than on organizational routines. Accordingly, leadership skills are required to sustain DC (Teece, 2012). Improving and expanding LHD can enhance the flexibility of the enterprise to adapt to a changing environment (Ma, Zhang, & Han, 2002), which can also improve DC. Thus, we proposed the following hypothesis: Hypothesis 3: Leadership habitual domains will have a significantly positive impact on dynamic capabilities.

Dynamic Capabilities and Ambidextrous Innovation Strong DC are the key to success, especially when an innovative company

needs to develop new markets or new products (Teece, 2012). Taking the initiative to mobilize resource allocation, DC allow organizations to broaden existing knowledge boundaries and technical capabilities, and carry out creative searching activities in relation to new market boundaries, thereby permitting organizations to discover new business opportunities or markets (Pandza & Thorpe, 2009), which supports exploratory innovation. Meanwhile, DC can facilitate the communication, diffusion, and integration of existing knowledge and resources, thus reinforcing organizations’ existing technologies, processes, and products (Helfat & Eisenhardt, 2004), so as to promote exploitative innovation.

Castiaux (2007) found that large-scale enterprises can promote the use of existing knowledge and the development of new knowledge by linking with network members. Ellonen, Wikström, and Jantunen (2009) also found that DC can effectively improve the competitive means in existing markets and the development of new markets. Sheng (2017) stated that successful ambidextrous innovation requires a combination of DC and organizational sense-making, the process by which an organization senses and acts decisively to respond to opportunities and threats. Thus, we proposed the following hypotheses: Hypothesis 4: Dynamic capabilities will have a significantly positive impact on exploratory innovation. Hypothesis 5: Dynamic capabilities will have a significantly positive impact on exploitative innovation.

The Mediating Role of Dynamic Capabilities According to Morgeson et al. (2010), team leadership has 15 functions, such as

training and developing the team, sense-making, solving problems, and providing resources. For the team to be fully functioning, leaders need to mobilize their skills and resources to satisfy the team’s needs (O’Reilly & Tushman, 2008). During this process, leaders need to know each member’s habitual domains and have flexible LHD to use different leadership tactics for different members of the team (Ma et al., 2014). In addition, the critical tasks leaders need to perform

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are sensing opportunities and threats, seizing opportunities, and managing threats/transforming, which can effectuate better resource allocation in times of uncertainty (Teece, 2016), and enable firms to reconfigure existing assets and learn new ways to perform ambidextrous innovation (O’Reilly & Tushman, 2008, 2011). Thus, we proposed the following hypothesis: Hypothesis 6: Dynamic capabilities will mediate the relationship between leadership habitual domains and ambidextrous innovation.

Method

Participants and Procedure Participants were team leaders in Chinese companies that were located in

Jiangsu, Guangxi, Guangdong, and Shandong Provinces. All study procedures were conducted in accordance with the ethical standards of our institutional research committee and with the 1964 Declaration of Helsinki and its later amendments. We sent our survey forms to 309 team leaders, and 224 of them responded (response rate = 72.49%). After excluding 19 invalid responses, the final sample consisted of 205 respondents (valid response rate = 66.34%). Table 1 shows the demographic profiles of the respondents and their teams.

Table 1. Participants’ Demographic Characteristics

Characteristics Frequency Percentage (%)

Gender Male 119 58.0 Female 86 42.0 Level of education High school graduate 19 9.3 College graduate 49 23.9 Bachelor’s degree 94 45.8 Master’s degree 43 21.0 Age (years) Under 30 39 19.1 30–39 57 27.8 40–49 62 30.2 50–59 47 22.9 Team size 10 or fewer 125 61.0 11–20 42 20.5 21–30 8 3.9 31–40 13 6.3 41 or more 17 8.3

Measures Leadership habitual domains. LHD were measured using the 17-item scale

developed by Ma et al. (2014), which comprises three dimensions: flexibility (e.g., “I make plans in advance to deal with problems that may arise in future work”), demonstration (e.g., “My behavior in my work can be imitated and

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studied by team members”), and coadaptation (e.g., “I am able to effectively resolve team conflicts, coordinate relationships, and encourage team members to get along well”). Responses were rated on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Dynamic capabilities. DC were measured using a 14-item scale developed by Feng (2012), which consists of three dimensions: sensing capability (e.g., “We have various ways get to know the development status and trends of the industry”), integrating capability (e.g., “Information about the industry or market can be widely disseminated within the enterprise”), and reconfiguring capability (e.g., “We can adjust in a timely manner our internal and external networks and network communication”). Reponses were rated on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Ambidextrous innovation. We measured exploratory innovation and exploitative innovation using the 12-item scale developed by Jansen et al. (2006). As the original scales were developed in English, we conducted back- translation to ensure the equivalence of the Chinese version. A bilingual scholar and three postgraduate students with a management background were asked to read through the items and comment on the clarity of expression. A sample item for exploratory innovation is “Our company accepts demands that go beyond existing products and services,” and a sample item for exploitative innovation is “We frequently refine the provision of existing products and services.” Responses were rated on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Data Analysis We used SPSS version 22.0 to generate the descriptive statistics and the

means, standard deviations, correlations, and reliability results for the study variables. Then we used Amos version 21.0 to perform the confirmatory factor analysis. We conducted structural equation modeling to explore the relationships between LHD, DC, and ambidextrous innovation because this method allowed us to assess the relationships among the latent variables simultaneously, while ensuring statistical efficiency (Blunch, 2008). Finally, we performed mediation analysis using PROCESS version 3.0.

Results

The means, standard errors, reliability coefficients, and correlations among the study variables are presented in Table 2. We adopted Cronbach’s  coefficients to measure the internal consistency reliability of the scales, and all were greater than .80, indicating that the measures had a high level of reliability.

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Table 2. Means, Standard Deviations, and Correlations for Study Variables

Variables M SD 1 2 3 4

1. Leadership habitual domain 4.09 0.71 (.92) 2. Dynamic capabilities 3.99 0.83 .82** (.93) 3. Exploratory innovation 3.90 0.83 .33* .70** (.87) 4. Exploitative innovation 3.94 0.80 .63** .56** .87** (.88)

Note. N = 205. Cronbach’s alpha values are shown in parentheses. * p < .05, ** p < .01.

The confirmatory factor analysis results are shown in Table 3. The goodness- of-fit index did not reach but was close to the ideal value, and the other fit indices all exceeded the recommended minimums; thus, we believe that this model supports the distinctiveness of the constructs.

Table 3. Fit Indices for Confirmatory Factor Analysis Results

Fit indicator 2 df 2/df GFI CFI IFI TLI RMR RMSEA

Proposed model 236.736 130 1.821 .889 .963 .964 .957 .035 .063 Fit guideline – – < 3.0 > .90 > .90 > .90 > .90 < .08 < .08

Note. N = 205. GFI = goodness-of-fit index, CFI = comparative fit index, IFI = incremental fit index, TLI = Tucker–Lewis index, RMR = root mean square residual, RMSEA = root mean square error of approximation.

We conducted structural equation modeling to test the hypotheses. First, we tested the main effect of the independent variable (LHD) on the dependent variable (ambidextrous innovation). The results show that LHD had a positive influence on both exploratory innovation ( = .83, p < .001) and exploitative innovation ( = .86, p < .001). Thus, Hypotheses 1 and 2 were supported.

Second, we tested the correlations between the independent variable (LHD) and the mediator (DC), and between the mediator (DC) and the dependent variable (ambidextrous innovation). Results show that LHD was positively and significantly associated with DC ( = .82, p < .001). Thus, Hypothesis 3 was supported. In addition, DC had a significantly positive correlation with exploratory innovation ( = .93, p < .001) and with exploitative innovation ( = .80, p < .001). Thus, Hypotheses 4 and 5 were supported.

Third, we used bootstrapping to examine the mediating effects, following Preacher and Hayes’ (2004) recommendation. Bootstrap estimates (based on 5,000 resamples) indicated that the indirect effect of LHD on exploratory innovation through DC was significant (p < .05), with a point estimate of 0.74 and a 95% confidence interval (CI) excluding zero [0.61, 0.87], and the direct effect of LHD on exploratory innovation after controlling for DC also had a 95% CI excluding zero [0.01, 0.29]. These results indicate that DC mediated the

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relationship between LHD and exploratory innovation. Furthermore, the indirect effect of LHD on exploitative innovation through DC was significant (p < .001), with a point estimate of 0.63 and a 95% CI interval excluding zero [0.46, 0.78], and the direct effect of LHD on exploitative innovation after controlling for DC also had a 95% CI excluding zero [0.12, 0.40]. These results indicate that DC mediated the relationship between LHD and exploitative innovation. Thus, Hypothesis 6 was supported.

Discussion

In this study we found that LHD had a positive effect on DC, exploitative innovation, and exploratory innovation, and that DC mediated the effect of LHD on both types of innovation. Moreover, DC was found to be positively related to both exploitative innovation and exploratory innovation.

Theoretical and Practical Implications First, we found that LHD had a positive effect on both exploitative innovation

and exploratory innovation, which supports previous research findings on the relationship between LHD and ambidextrous innovation (Ma et al., 2014; Tang, Ye, Chen, & Feng, 2004; Ye, Feng, & Ma, 2016). Whereas Ye et al. (2016) provided a framework to explore the relationship between firms’ technology habitual domains and innovation strategies, in this study we moved to a personal perspective by focusing on team leaders who are responsible for satisfying team members’ needs and achieving team goals (Morgeson et al., 2010). Leaders’ habitual behaviors, which reflect LHD, are formed by stabilized knowledge and experience that accumulate over time, and LHD contribute to leaders’ existing and potential capabilities. Meanwhile, the inertia of LHD supports exploitative innovation by refining existing products and gaining efficiency, but when a leader is stimulated by new information, LHD becomes unstable and is reconfigured or even transformed to a new domain, thus supporting exploratory innovation (Ye et al., 2016). Our analysis of the antecedents of ambidextrous innovation from an endogenous viewpoint has filled a gap in the existing research and enriched HD theory by providing a new perspective with which to study leadership theory.

Second, our results confirm that DC mediates the relationship between LHD and ambidextrous innovation, which supplements existing knowledge of this influence process. According to the innovation dynamics framework proposed by Yu and Chen (2012), firms need to consider how to solve a set of problems by using existing or acquired competencies, and also need to create value. By introducing the mediator of DC, we revealed the influence mechanism of firms’ strategic renewal and transformation of existing knowledge and competencies in LHD to satisfy the need for ambidextrous innovation.

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Moreover, we found that LHD can affect DC. As coadaptation is a characteristic of LHD, LHD can affect leaders’ ability to adapt to the environment (Ma et al., 2014). Combined with the heterogeneous knowledge, experience, and capabilities provided by LHD, specific LHD give enterprises their distinct DC to respond to turbulence. As high-level competencies, DC determine the speed and degree of aligning particular resources to match the requirements of the business environment (Teece, 2012). As we also verified that DC are positively related to ambidextrous innovation, which is consistent with existing research findings (Castiaux, 2007; Ellonen et al., 2009; Sheng, 2017), our results also explain the differences in developing and executing innovation strategies between enterprises through LHD theory.

In addition, although habitual domain theory has been applied to various research areas (e.g., decision making, innovation management), there is still a shortage of empirical studies using this theory. In this study we applied structural equation modeling to analyze the effect of LHD on ambidextrous innovation, providing objective and reliable results and enriching the extant literature on habitual domain theory.

Practically, we can conclude from our results that team leaders need to broaden the depth and width of leadership knowledge and experience, because the more they are stabilized in LHD, the more likely the firm is to succeed in ambidextrous innovation. In addition, to avoid the rigidity of LHD, leaders need to constantly renew, reconfigure, and transform their existing capabilities, structures, and processes, to maintain efficiency in the short term by using exploitative innovation and pursue profit in the long term through exploratory innovation.

Limitations and Future Research Directions This study has certain limitations. First, we adopted a horizontal data collection

method, which may not be able to reveal accurately the dynamic changing impact of LHD on DC and ambidextrous innovation. Therefore, future researchers could apply longitudinal analysis to trace the relationships between LHD, DC, and ambidextrous innovation. Second, some variables may have been strongly correlated owing to the relatively small sample size in this empirical study. Future researchers could increase the sample size to avoid multicollinearity. Third, we focused only on one mediator in the relationship between LHD and ambidextrous innovation. Future researchers could include more mediators and/ or moderators, to improve interpretation of the model.

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