Academy of Strategic Management Journal (Print ISSN: 1544-1458; Online ISSN: 1939-6104)

Research Article: 2018 Vol: 17 Issue: 1

The Impact of Behavioral Complexity on Exploitative and Explorative Behavior among Owner-Managers of SMEs in Malaysia

Poon Wai Chuen, Multimedia University

Osman Mohammad, Multimedia University

Wan Fadzillah Wan Yusoff, Multimedia University

Keywords

Exploitative Behaviours, Explorative Behaviours, Behavioural Complexity, SMEs, Malaysia.

Introduction

Worldwide, SMEs has revolutionized business environment and often depicted as the main driver of the economy by creating wealth and providing jobs to the local community that they are situated in. SMEs should be viewed main contributor in stimulating long-term development of economy in many nations as SMEs accounts for more than 90 locally (Hashim, 2005; Tung & Aycan, 2008). Based on 2011 Malaysia economic census, SMEs in Malaysia consist of 97.3% from 662,939 units of total business establishment in the country (Department of Statistic Malaysia, 2017). SMEs in Malaysia recorded a significant double digit growth of 13.6% for 2014 and the share of SMEs to GDP raise significantly from 33.5% in 2014 to 36% for year 2014 (SME Annual Report 2014/2015). However, the contribution rate of SMEs to GDP of in Malaysia is relatively low as compared other nations. SMEs in Korea and Singapore contributes a total of 53% and 49% respectively, meanwhile in Thailand, SMEs contributes a total of 38% to the nation’s GDP (SME Annual Report 2009/2010). This indicates that the growth potential among Malaysian SMEs need to be further refine to enable for a larger contribution to the nation.

A growing number of researches highlighted the benefits of being exploitative and explorative both on the organization level and individual level (Junni et al., 2013). Such capabilities are extremely beneficial for SMEs as they often face multiple constraints from both internal and external resources. A shortage of resource forces owner-managers to be ambidextrous in managing challenges faced by the organization. Hence SMEs are more likely to be both exploitative and explorative (Cao et al., 2009) in order to address and overcome these shortcomings. March (1991) first introduced these two concepts exploitative and explorative behaviours. Exploitative relate behaviours in the refinement of existing competencies while explorative relate behaviours in searching for new knowledge or opportunity (March, 1991). These behaviours are seen as integral to a firm’s profitability and long-term sustainable (Cao et al., 2009). These contradictions have been positively linked to firm’s performance, innovation, sales growth and firm survival (Junni et al., 2013; O’Reilly & Tushman, 2013). Thus the development of exploitative and explorative behaviours is expected positively contribute to SMEs.

Individual of SMEs need to actively reconfigure available resources and capabilities, through new patterns of integration in producing new value to sustain growth and profit. Such complexity of organizing and managing resources demands owner-managers to be competent and capable in sensing and seizing new opportunities in a dynamic business environment (Teece et al., 2014). However, the reconfiguration of available resources and capabilities remains vague among SMEs, even more so on an individual level. The contradiction between exploitative and explorative behaviours compels owner-managers to behave erratically. These erratic behaviours force the individual to be competent in multiple skills. Therefore, to foster these dynamic behaviours, owner-managers must address the notion of behavioural complexity to inculcate the explorative and exploitative behaviours. In short, the purpose of this paper is to explore the relationship between behavioural complexity with explorative and exploitative behaviour.

Literature Review

Behavioural Complexity

Denison et al. (1995) define behaviour complexity as the ability for someone to “perform the multiple roles and behaviours that circumscribe the requisite variety implied by an organizational or environmental context. The notion of behavioural complexity traces back to Competing Value Framework (CVF). The framework attempts to measure organizational effectiveness. CVF is defined by two competing values: Flexible versus Stable structures and Internal versus external focus. Cameron et al. (2006) simplified the framework to compete, control, create and collaborate for easier adoption on organizational and individual level. The framework is often assumed to be mutually exclusive and neglect the dynamic context of an organization (Lawrence et al., 2009). As the internal and external environment rapidly changes, individuals who are able to manage opposing tensions are likely to retain greater adaptability and capacity (Weick, 2003) to manage multiple competing needs of the organization (Lawrence et al., 2009). Individual’s ability to integrate competing needs is best indicated by the performance of each role. Researchers argued that leaders who can balance or diversify their behaviours across the competing values dimensions are likely to have a high degree of behavioural complexity and better suited to different organizational demands (Hooijberg & Quinn, 1992).

Behavioural complexity represents a wide range of behaviours that a leader is capable of performing and these behaviours are summarized into four roles-compete, control, collaborate and create (Lawrence et al., 2009) (Table 1). Compete roles refer to planning, goal setting and productivity, that is characterized by an external focus (e.g. benchmarking to competitor performance and profitability) and structural controls (e.g. goal setting and process monitoring) (Quinn & Rohrbaugh, 1983; Lawrence et al., 2009). Collaborate roles refer to cohesion, morale and training, that is characterized by an internal focus (development of internal capability, specifically, human resource development) and a flexible management approach characterized by participative decision making, empathic relationships (Quinn & Rohrbaugh, 1983; Lawrence et al., 2009). Control roles refer to information management, stability and control, that is characterized by an internal focus (e.g. establishing routine, buffering against external disruption) and hierarchical control (e.g. having in place clear and immutable lines for reporting, approval and communication) (Quinn & Rohrbaugh, 1983). Create roles refer to adaptation and growth that is characterized by an external focus (e.g. market growth and competition) and flexible organizational structures (e.g. flat hierarchies, cross-functional teams) (Quinn & Rohrbaugh, 1983; Lawrence et al., 2009).

Table 1: Behvaioural Complexity (Lawrence et al., 2009)
  Focus Dimension
  Internal Structure External Structure
Structure Dimension Flexible Structure Collaborate
-Encouraging Participation
-Showing Concern
-Developing People
Create
-Initiating Significant Change
-Anticipating Customer Needs
-Inspiring People to Exceed Expectation
Stable Structure Control
-Expecting Accurate Work
-Controlling Projects
-Clarifying Policies
Compete
-Modelling A Hard Work Ethic
-Focusing on the Competition
-Emphasising Speed

Individuals must be able to engage multiple behavioural roles in addressing the dynamic changes in the business environment (Tsui, 1984). Behavioural complexity demand individuals to be loose and strict, creative and routine and formal and informal at the same time. Smith & Lewis (2011) suggested that managing paradoxical tensions helps individuals, groups and firms to be flexible and resilient, fostering more dynamic decision making. Researchers observe that individuals with balance competing roles have a higher likelihood to be more effective and achieve better performance (Bullis et al., 1992; Denison et al., 1995; Hooijberg, 1996; Hooijberg & Quinn, 1992) however what remains unclear, though, is ‘the degree to which behaviours from all quadrants need to be equally available’ (Lawrence et al., 2009).

Ambidexterity: Exploitative and Explorative Behaviours

Ambidexterity refers to the ability to explore new opportunities while simultaneously exploiting existing competencies (Kauppila & Tempelaar, 2016; Cao et al., 2009; Tushman & O’Reilly, 1996). The two concepts that embody ambidexterity are exploitation and exploration behaviours. The theory of dynamic capability stresses on the urgency to reconfigure existing competencies and establish new competencies in response to dynamic business environment. Implied in the theory is that owner-managers who form the backbone of the firm must be able to seamlessly carry out both exploitative and explorative behaviours. Both behaviours are not only distinct dimensional behaviours but are also mutually enabling (Farjoun, 2010; Holmqvist, 2004). When an individual explores, he/she simultaneously creates new opportunities to exploit, while when an individual is exploiting, he/she simultaneously refines their knowledge and expertise that contribute to exploration (Kauppila & Tempelaar, 2016).

Explorative behaviours increases the breadth of knowledge, thus creating prospects for radical changes, while exploitative behaviours increases the depth of knowledge, which typically leads to incremental development and enhanced reliability (Benner & Tushman, 2003). Exploitative and explorative behaviours is often reflected in the decisions and routines made by owner-managers that would ultimately enable the firm to sense and seize new internal or external opportunities through reconfiguring of resources (O’Reilly & Tushman, 2013; Faizah, Hazlina & Osman, 2016). Gupta et al. (2006) acknowledged that at the individual level is the most difficult to attain both exploitative and explorative behaviours due to the contradicting demands faced by the individual (Kauppila & Tempelaar, 2016). Raisch et al. (2009) suggested that performing both explorative and exploitative action are heavily influenced by individual characteristics and ambidextrous individuals must manage contradictions and conflicting goals, engage in paradoxical thinking and fulfil multiple roles.

On an individual level, the person must be able to combine both exploitative and explorative behaviours in daily routines (Bledow et al., 2009; Mom et al., 2009; Kauppila & Tempelaar, 2016). Individual’s resources such as time and knowledge will limit (March, 1991) and restrict their pursuant of both exploitative and explorative activities adequately (Ambos et al., 2008; Gupta et al., 2006). Weick (2003) states that if an individual is able to combine both opposing behaviours, that individual would possess greater adaptability to shifting demands according to its environment. Finding the right balance between exploration and exploitation remains vague till these days, concentrating all effort alone on exploitation while neglecting exploration could benefit firms in the short run, while directing all effort on exploration alone could spell disastrous towards the long run survival of many organizations (March, 1991; O’Reilly & Tushman, 2013). There is a need for more empirical research on the development of exploitative and explorative behaviours at the individual level (Mom et al., 2009; Kauppila & Tempelaar, 2016) in the context of developing nations. Thus, in this study addresses this gap by examining the relationship between behavioural complexity (e.g. create, compete, control and collaborate behavioural roles) and exploitative and explorative behaviours which is depicted in Figure 1. The above discussion leads to the following hypotheses.

Figure 1.Research Framework.

H1: Collaborate roles positively influence exploitative behaviour.

H2: Collaborate roles positively influence explorative behaviour.

H3: Create roles positively influence exploitative behaviour.

H4: Create roles positively influence explorative behaviour.

H5: Control roles positively influence exploitative behaviour.

H6: Control roles positively influence explorative behaviour.

H7: Compete roles positively influence exploitative behaviour.

H8: Compete roles positively influence explorative behaviour.

Methodology

This study is cross sectional in nature. The respondents are managers and entrepreneurs of SMEs located in Klang Valley. A structured survey instrument is used to collect the data. This study adopted the 36 items measuring behavioural complexity comprising of four different roles (i.e., create, compete, collaborate and control) developed by Lawrence et al. (2009). The create roles subscale consisted of 9 items (α=0.78), the compete subscale consisted of 9 items (α=0.74), the collaborate subscale consisted of 9 items (α=0.68) and the control subscale consisted of 9 items (α=0.85). As for the explorative and exploitative behaviour-14 items were adopted from Mom et al. (2009). The explorative behaviour subscale consisted of 7 items (α=0.90) and the exploitative behaviour subscale consisted of 7 items (α=0.87). This study adopted statistical procedure by Lubatkin et al. (2009) where an additive index was used to conceptualize exploitative and explorative behaviours.

In determining the sample size, Kline (2005) recommended to estimate the minimum sample size by using G*Power 3.1 program (Faul et al., 2009). Using this software, the estimated sample size would be 98 respondents with the power at 95%, alpha at 0.05 with medium effect size of 0.15. SME Corporation Malaysia provided a list of 11,084 SMEs in Selangor and Kuala Lumpur. The directory provided by SME Corp was scanned to remove companies that have ceased to exist. A simple internet search reveals that a total of 4,623 SMEs have ceased its operation before October 2015. Thus these firms were removed from the directory and would not be considered for sampling. The remaining total of 6,461 firms was then keyed into SPSS vs. 23. A total of 1,000 randomly select cases were generated from SPSS which constitute the sample of the present study. The administration of this research was done through questionnaire distribution via email and a total of 183 useable responses were collected. Of 1000 owner-managers of SMEs that were invited via email to participate in this study, a total of 220 firms completed the survey which makes a total of 22% of response rate. A total of 37 responses were removed due to incomplete and non-variance responses. Table 2 gives the detail of the characteristics of the respondents.

Table 2: Sample Characteristics
Profile   Frequency (%)
Gender Male 118 (64.5)
Female 65 (35.5)
Age 25 Years old & Below 27 (14.8)
26-35 Years old 109 (59.6)
36-45 Years old 31 (16.9)
46 Years old & Above 16 (8.8)
Ethnicity Malay 20 (10.9)
Chinese 144 (78.7)
Indian 12 (6.6)
Indigenous 7 (3.8)
Types of Industry Service 148 (80.9)
Manufacturing 13 (7.1)
Others 22 (12)
Position in The Firm Owner 73 (39.9)
Manager 110 (60.1)
No. of Fulltime Employees Less than 50 141 (77)
51-100 14 (7.7)
101-150 17 (9.3)
151-200 11 (6)
Year of Establishment Less than 5 Years 103 (56.3)
6-10 Years 29 (15.8)
11-15 Years 19 (10.4)
More than 15 Years 32 (17.5)

Data Analysis

The survey questionnaire was filled by key informants of the organizations, which means that there is a potential to have a common method variance (Malhotra & Birks, 2006). Common method bias or Common Method Variance (CMV) refers to the variance traceable to measurement method rather than to the construct or constructs purportedly represented by the measures (Podsakoff et al., 2003). In testing for CMV, Harmans Single factor test was run. The results returned a 14 factor with a total variance explained of 71.87% and the first factor explained 28.09% which indicated that there is no serious common method bias in this research (Podsakoff et al., 2003).

Assessment of Measurement Model

The proposed model was tested using Partial Least Square-Structural Equation Modelling (PLS-SEM) a second generation multivariate technique (Ringle et al., 2015), which evaluates both measurement and structural models to minimize error variance (Hair et al., 2013). The present study adopts second-order reflective-formative constructs for behavioural complexity, hence PLS-SEM is an appropriate tool to estimate the postulate the relationship hypothesized in the theoretical framework. In PLS-SEM model, the estimation follows a two-step approach which involves measurement and structural model (Henseler et al., 2009; Hair et al., 2016). In evaluating the composite reliability and indicator reliability of the measurement model, the rule of thumb for factor loadings should be above 0.5 (Hair et al., 2016), composite reliability should be above 0.7 (Hair et al., 2016) and Average Variance Extracted (AVE) should be above 0.5 (Henseler et al., 2009; Hair et al., 2016). The results indicated that the item loadings were ranged from 0.669 to 0.922, while composite reliability and AVE values were ranged from 0.831 to 0.922 and 0.596 to 0.798 (Table 3). As for the second order factor in this analysis, a repeat indicator approach was adopted in modelling the construct (Hair et al., 2016). All items met the minimum cut off value, thus indicating sufficient convergent validity. Both exploitative and explorative behaviours were modelled as a single item construct, thus validity and reliability assessments were not necessary.

Table 3: Items, Loadings, Average Variance Explained And Composite Reliability
First-Order Construct Items Loadings AVE CR
Exploitative Behaviour AB_Exploit SIC SIC SIC
Explorative Behaviour AB_Explore SIC SIC SIC
Encouraging Participation BC1 0.868 0.769 0.909
  BC2 0.858    
  BC3 0.905    
Developing People BC4 0.829 0.723 0.886
  BC5 0.881    
  BC6 0.839    
Acknowledging People's Needs BC7 0.890 0.788 0.918
  BC8 0.882    
  BC9 0.891    
Anticipating Customer's Needs BC10 0.856 0.683 0.866
  BC11 0.821    
  BC12 0.801    
Initiating Significant Change BC13 0.798 0.744 0.897
  BC14 0.920    
  BC15 0.865    
Inspiring People to Exceeds Expectations BC16 0.867 0.699 0.874
  BC17 0.885    
  BC18 0.750    
Clarifying Policies BC19 0.849 0.798 0.922
  BC20 0.922    
  BC21 0.908    
Expecting Accurate Work BC22 0.880 0.737 0.894
  BC23 0.847    
  BC24 0.849    
Controlling Projects BC25 0.669 0.596 0.815
  BC26 0.835    
  BC27 0.802    
Focussing on Competition BC28 0.885 0.721 0.885
  BC29 0.900    
  BC30 0.755    
Showing a Hard Work Ethic BC31 0.872 0.737 0.893
  BC32 0.886    
  BC33 0.816    
Emphasizing Speed BC34 0.838 0.725 0.888
  BC35 0.884    
  BC36 0.831    

Note: SIC: Single Item Construct, AVE: Average Variance Extracted, CR: Composite Reliability

As for reflective-formative second order organizational context, collinearity test on the index indicates minimal collinearity with the Variance Inflation Factor (VIF) below the cut-off value of 5 (Hair et al., 2016). Hence collaborate, create, control and compete does not correlate perfectly and exhibits discriminant validity, which is desirable because high multicollinearity would challenge assessments of component validity (Diamantopoulos & Winklhofer, 2001). Since PLS-SEM does not assume a normal distribution (Hair et al., 2016), the researcher applies bootstrapping routine to determine the level of significance of each indicator weight. The components weights for encouraging participation was 0.454, developing people were 0.463 and acknowledging personal need was 0.300 suggests that each component is an important determinant of collaborate. The components weights for anticipating customer needs was 0.375, initiating significant change was 0.394 and inspiring people to exceed expectations was 0.410 suggests that each component is an important determinant of create. The components weights for clarifying policies was 0.382, expecting accurate work was 0.387 and controlling projects was 0.410 suggests that each component is an important determinant of control. The components weights for focusing on competition was 0.353, showing a hard work ethic was 0.450 and emphasizing speed was 0.560 suggests that each component is an important determinant of compete. As for the significant, all the 12 variables were significant to their respective construct ranging from 8.577 to 19.006. The results were summarized in Table 4.

Table 4: Variance Inflation Factor And Outer Weights For Second-Order Construct
Second-Order Construct First-Order Construct Weights T-Value VIF
Collaborate Encouraging Participation 0.454 12.823 1.434
Developing People 0.463 16.554 1.595
Acknowledging Personal Needs 0.300 10.265 1.565
Create Anticipating Customer Needs 0.375 15.350 1.588
Initiating Significant Change 0.394 19.009 2.111
Inspiring People to Exceed Expectations 0.410 14.479 1.782
Control Clarifying Policies 0.382 11.585 1.346
Expecting Accurate Work 0.387 10.757 1.468
Controlling Projects 0.469 14.486 1.734
Compete Focussing on Competition 0.353 8.577 1.224
Showing a Hard Work Ethic 0.450 12.728 1.470
Emphasizing Speed 0.560 14.051 1.542

Subsequently, the discriminant validity was assessed. It was observed that all constructs fulfill Fornell-Larcker criterion, where discriminant validity is established if a latent variable accounts for more variance in its associated indicator variables than it shares with other constructs in the same model (Fornell & Larcker, 1981) (Table 5). A new and alternative method, Heterotrait-Monotrait (HTMT) was introduced and found to be more suitable as compared to Fornell-Larcker criterion (Henseler et al., 2015). The evaluation for HTMT is to observe whether the ratio approaches 1.0, which if it so, would indicate an issues with discriminant validity (Voorhees et al., 2015). Henseler et al. (2015) suggested a cut off value of 0.85 or 0.90 in determining any issues with discriminant validity. Using a cut off value of 0.85, all constructs were below 0.85 thus fulfilling HTMT criterion (Table 6).

Table 5: Fornell-Larkcer Criterion
1 2 3 4 5 6
1. Collaborate 0.738          
2. Compete 0.464 0.673        
3. Control 0.465 0.640 0.690      
4. Create 0.631 0.608 0.554 0.676    
5. Exploitative Behaviour 0.405 0.465 0.529 0.372 1.000  
6. Explorative Behaviour 0.612 0.376 0.278 0.641 0.416 1.000
Table 6: Htmt Output
1 2 3 4 5 6
1. Collaborate            
2. Compete 0.527          
3. Control 0.528 0.767        
4. Create 0.711 0.705 0.625      
5. Exploitative Behaviour 0.425 0.505 0.567 0.401    
6. Explorative Behaviour 0.646 0.398 0.291 0.687 0.416 -

Assessment of Structural Model

To evaluate the structural model, the path analysis was conducted to test the ten hypotheses outlined in this study. R2 indicates the amount of variance explained by the exogenous variable. The R2 for explorative behaviour was 0.448 which indicates that 44.8% of the variance is explained by behavioural complexity, whereas for exploitative behaviour, the . R2 value was 0.262 which indicates 26.2%. Using bootstrapping techniques of 5000, the path estimates and t-statistics were then examined the hypothesized in this study.

7 shows a summary of structural modal analysis. From the analysis, it was found that collaborate (β=0.190, p<0.05) and control (β=0.354, p<0.05) were positively related to exploitative behaviour, while collaborate (β=0.377, p<0.01), control (β=-0.182, p<0.05) and create (β=0.494, p<0.01) were positively related to explorative behaviour. Effect size in the present study was examined based on Cohen (1988) guideline which states that f2 is small effect ranging from 0.02-0.14, medium effect ranging from 0.15-0.34 and large effect ranging from more than 0.35 on the exogenous variable to endogenous variable. The findings are summarized in Table 7. As for the predictive relevance (Q2 ), which is assessed through blindfolding procedure? This procedure is relevant for endogenous model with reflective items and single item construct, which examine the capabilities of the exogenous variables in predictive relevance of the endogenous variable (Hair et al., 2016). The findings indicated that the predictive validity for all exogenous variables were ranged between 0.262 to 0.448 with all the predictive values of exogenous variables are greater than zero. Therefore, the model is considered to have predictive validity (Hair et al., 2016).

Table 7: Std. Beta, std. Error, t-value, effect size, variance explained and predictive relevance
ß Std. Error T-Value Decisions f2 Effect R2 Q2
Collaborate ->Exploitative Behaviour 0.190 0.090 2.109* H1 Supported 0.031 Small 0.329 0.262
Collaborate ->Explorative Behaviour 0.377 0.063 5.952** H2 Supported 0.167 Medium 0.504 0.448
Compete ->Exploitative Behaviour 0.185 0.108 1.720 H3 Not Supported        
Compete ->Explorative Behaviour 0.017 0.082 0.207 H4 Not Supported        
Control ->Exploitative Behaviour 0.354 0.112 3.167* H5 Supported 0.100 Small    
Control ->Explorative Behaviour -0.182 0.073 2.491* H6 Supported 0.036 Small    
Create ->Exploitative Behaviour -0.057 0.114 0.496 H7 Not Supported        
Create ->Explorative Behaviour 0.494 0.084 5.907** H8 Supported 0.226 Medium    

**p<0.01, *p<0.05, f2=Effect Size, R2=Variance Explained, Q2=Stone-Geisser Predictive Relevance (Bootstrapping=5000, Omission Distance, D=7).

Discussion

The main objective of this study is to examine the relationship between behavioural complexity (e.g. create, compete, control and collaborate roles) with exploitative and explorative behaviours. Surprisingly, the study reveals that compete and create roles had no significant impact on exploitative behaviours. Collaborate and control roles were found to be significant on exploitative behaviours. Compete roles had no significant impact on explorative behaviours. However, collaborate, control and create roles have significant impact on explorative behaviours.

The data indicates no significant relationship between both competes and creates roles and exploitative behaviour. A plausible explanation is that an existing organization’s routines and process would have already been established even as owner-managers exploit them. Create roles seem unnecessary to be carried out in achieving exploitative behaviour where the process of accomplishing mundane day to day tasks would have been established in the firm. These activities can be run without any guidance or input from anyone. Notably, exploitative behaviours focus on efficiency of task and utilizing existing resources. If changes were to occur, individuals in the firm would need to adapt and adjust to the changes thus, reducing their overall efficiency. As for compete roles, competing with fellow members in the firms are often times counter-productive for SMEs. Teamwork is more evident among SMEs due to their lacking of manpower and resources. Collaborate and control roles are closely aligned with the concept of exploitative behaviours. Developing members in the organization, insisting on work to be done correctly and focusing on speed are the hallmarks of improving efficiency in an organization. Thus, performing both collaborate and control roles are vital in cultivating exploitative behaviours.

Interestingly, the data indicates no significant relationship between compete roles and explorative behaviours. Compete roles includes focusing on competition, showing a hard work ethic and emphasizing on speed, which are not concurrent with explorative behaviours. Emphasizing on speed in exploring for new opportunities outside the organization and competing among owner-managers would be counterproductive. It might be years for an organization to explore and develop new opportunities domestically or abroad. Owner-managers are more likely to take decision cautiously in order to minimize chance for failure. Emphasis on teamwork in SMEs would result in more positive results. When focusing on exploration, behaviours like mimicking your competitors, completing the task as quick as possible or heavily emphasizing on work ethics are not suitable as seeking external opportunities as it requires time to develop, flourish and often this cannot be rush (Teece et al., 2014). Thus, performing collaborate, control and create are vital in cultivating explorative behaviours.

The ability to balance and be skilled in all four quadrants is more likely to exhibit a wide array of role strengths and may lead to an improvement of performance as the situations demands (Lawrence et al., 2009). Managers must have an internal balancing mechanism to switch from one role to the next depending on whether the task at hand requires exploitative or explorative behaviours. Owner-managers’ ability to perform and regulate these behaviours is fundamental as the business environment changes rapidly for SMEs. Findings from this study provide evidence to attest that owner-managers’ behavioural complexity significantly impacts on exploitative and explorative behaviours. Embarking in exploitative and explorative behaviours simultaneously, will result in securing both short and long term competitive advantages. This concerted effort will ultimately bring more profit into the firm. Firms will benefit through exploiting of existing resources in the short run while in the long run, exploration will benefit the firm by ploughing and developing new and upcoming trends for the consumers.

Limitations and Future Research

This research is not without its limitations which some of it, suggest avenues for future study. Firstly, further studies would need to be carried out to understand how these complexity and contradiction changes over time. While a cross-sectional research is useful, a more dynamic perspective in a mixed method study would provide deeper insight. Collecting interviews of respective owner-managers in combination with a longitudinal research would enable researchers to better appreciate the context of these complexity and contradictions. Secondly, since the sample of this study focused on owner-managers of firms located in Selangor and Kuala Lumpur, Malaysia, the generalizability of the result somewhat limited. Thus, there is a need for this study to be repeated in other developed and developing nations to more systematically investigate how behavioural complexity impacts exploitative and explorative behaviours.

This study is limited as it only investigates the dimension of behavioural complexity and exploitative and explorative behaviours. Hence, this paper provides an incomplete perspective on roles affecting exploitative and explorative behaviours. Therefore, more studies that look into additional aspects such as leadership, cultures and firm’s performance management systems and their impact on ambidextrous behaviours would provide a more holistic picture in shaping individual behaviours. Furthermore, the data regarding the extent to which individual exploitative and explorative behaviours were self-reported. Future research should combine both managers and direct report employees in evaluation of exploitative and explorative behaviours. As these contradictions often times do not affect owner-managers only, a more integrated approach would need to be adopted to provide a more comprehensive picture.

Conclusion

The results indicated that Malaysian SMEs owner-managers are not generating sufficient returns due to their current competitive capabilities. SMEs need to sense, seize and reconfigure their assets to maintain their competitive capabilities that would ultimately enable them to compete both locally and internationally. Capabilities such as exploitative and explorative behaviours would, in the long run, bolster the growth and performance of SMEs.

SMEs are constantly challenged with limited resources coupled with a hostile dynamic environment. These challenges further emphasize and motivate firms to establish dynamic capability with minimal impact on their resources. Based on the study, behavioural complexity has significant impact on exploitative and explorative behaviours, which leads to the establishment of ambidextrous behaviours. Hence it is important for the entire firm as a whole in formulating and cultivating different behavioural roles as a method of establishing exploitative and explorative behaviours to increase the chances for long term survival among SMEs.

References