Links Between Macro Facilitators of Organizational Empowerment and Ambidextrous Behaviors: An Empirical Study from Pakistan

Abstract

The goal of this study is to see if there's a relationship between employees' ambidextrous behaviors and macro facilitators of organizational empowerment (such as control over workplace decisions, dynamic structural framework, and fluidity in information sharing) (exploration and exploitation). To acquire data, this study uses survey methods. A cross-sectional survey was done to collect information from academics at five large public sector universities in Pakistan's Balochistan province. Control over workplace decisions boosts academics' engagement in exploration and exploitation, while a dynamic structural framework merely increases their engagement in exploration, according to the findings based on data from 240 respondents (n = 240). The findings also show that information sharing flexibility has little effect on exploration and exploitation behaviors. In conclusion, the results of this study imply that organizational empowerment is critical for academics' ambidextrous behaviors to thrive. As a result, specific organizational facilitators of empowerment (such as control over workplace decisions and a dynamic structural framework) are advised in higher education institutions. This research is significant because it develops and tests a model that explains hitherto unexplored connections between macro facilitators of organizational empowerment and employees' ambidextrous behaviors. In addition, the research provides important insights for managerial practice and research.

1. Introduction

Individual ambidexterity (IA) is defined as the constellation of two contradictory but complementary behaviors: exploration (activities such as searching for new systems and routines for the organization, experimenting with new approaches, reconsidering existing beliefs, and innovating) and exploitation (activities such as refining and using existing knowledge, improving and applying existing competencies, and elaborating on existing beliefs) (Mom et al., 2007, pp. 912–913) is critical to the competitive advantage and performance of organizations (Kustyadji et al., 2021; Mu et al., 2020; Sijabat et al., 2021). That is perhaps why it has gained a great deal of research attention in the past decade (Rosing & Zacher, 2017; Zhang et al., 2018). A significant body of literature is also available on IA, a brief account of which is provided in the following paragraphs.

First on the list are the pioneering works by Gupta et al. (2006) and Mom et al. (2007, 2009). While Gupta et al. (2006) highlighted the importance of employee ambidexterity for organizations, Mom et al. (2007, 2009) defined the IA construct and explained the characteristics of ambidextrous managers. These scholars also provided insights regarding certain organizational level antecedents of IA such as knowledge inflows, structural, and personal coordination factors. Another notable work on IA is that of Good and Michel (2013), who examined the effects of IA (as a formative construct) on the task adaptive performance in a sample of 181 respondents. This empirical work demonstrated that IA predicts performance on dynamic tasks that involve both exploration and exploitation behaviors. Tuncdogan et al. (2015) further expanded the literature by expounding the relationship between regulatory focus, exploration, and exploitation behaviors, and highlighting the boundary condition role of decision-making autonomy and ambiguity of environment in the regulatory focus− exploration/exploitation relationships. Caniëls and Veld (2016), Rosing and Zacher (2017), and Kobarg et al. (2017) enriched this growing body of literature by unraveling the role of employee ambidexterity in fostering innovative behaviors, innovation, and research performance. Caniëls et al. (2017) and Zhang et al. (2019) contributed to the literature by evaluating the effects of employee ambidexterity on perceived empowerment, intrinsic motivation, trust- building, and stress management. Schnellbacher et al. (2019) published a cross-level analysis in the IA literature, indicating that organizational architecture and organizational context are important factors in predicting IA and that the latter has positive implications for departmental effectiveness, efficiency, and team performance (Schnellbacher et al., 2019). IA can also be facilitated by variables such as inspirational leadership and organizational learning skills, according to research. (Vallina et al., 2019).

Apart from the studies noted above, a plethora of research has been conducted on IA (Folger et al., 2021; Karani et al., 2021; Klonek et al., 2021; Lekhawichit et al., 2021; Mu et al., 2020; Pertusa-Ortega et al., 2021; Raiden et al., 2020; Zhang et al., 2021). This significant body of literature, however, has some notable gaps. For instance, research has yet to explore the links between the macro facilitators of organizational empowerment (control of workplace decisions (CWD), dynamic structural framework (DSF), and fluidity in information sharing (FIS) (Matthews et al., 2003) and IA. Unpacking the association between these constructs is not only important for theory building but is also useful for management practice. Hence, this study’s main research question (RQ) is, what effects do the macro facilitators of organizational empowerment (OE) have on academics’ IA?

Since IA is important to the competitive advantage of organizations (Mu et al., 2020), the answer to the above-noted question may be of central interest for organizational decision makers and management practice, e.g. the evidence-based knowledge produced from examining the macro facilitators of OE−IA relationships can inform organizational decision makers regarding the extent to which empowerment helps foster ambidexterity at employees’ level. Further, this study is important in that it introduces a new theory (i.e. the JD-R model (Bakker & Demerouti, 2007)) to the IA research.

2. Literature Review and Hypotheses

The JD-R model, which asserts that job demands (such as role ambiguity and work pressure) and job resources (such as autonomy and organizational support) are major predictors of important outcomes such as well-being, engagement, and performance, is used as a theoretical lens in this study. The JD-R model states that job demands can hinder crucial outcomes, while job resources can nurture them (Bakker & Demerouti, 2007). Similarly, we believe that all macro facilitators of OE (such as CWD, DSF, and FIS) are job resources that might promote IA. Based on these elements’ motivational potential (Matthews et al., 2003) and ability to influence outcomes such as employee engagement, performance, productivity, and turnover intentions, we presume this (López-Cabarcos et al., 2021; Nwachukwu et al., 2021; Sandhya & Sulphey, 2021). To better expound the relationship of CWD, DSF, and FIS with IA, an explanation is needed of these constructs. The concept of empowerment is not easy to grasp as various approaches, perspectives, and conceptualizations regarding this construct exist in the literature (Matthews et al., 2003, p. 315). However, in the business context, the relational (the extent to which power is shared with the lower levels in organizational hierarchy) and psychological (the belief that empowerment is a psychological state) perspectives are commonly followed to define/ conceptualize empowerment (Spreitzer, 1995). Based on the underpinnings of these perspectives, Matthews et al. (2003, p. 03) have defined OE in terms of the following factors:

i) CWD: allowing employees to input in all aspects of their careers.

ii) DSF: providing employees with very clear but changeable guidelines to facilitate decision making in dynamic work environments, and,

iii) FIS: sharing vital information with employees.

Matthews et al. (2003, p. 300–301) also provided examples of these factors. For instance, the activities such as allowing employees to set their goals, giving them the freedom to schedule their activities/tasks, and permitting them to participate in organizational decision-making, epitomize CWD. Similarly, providing employees with explicit guidelines on decision-making, asking for their inputs, and encouraging them to take risks and learn from their failures and new experiences represent DSF. Finally, sharing with employees the information regarding how the company/organization is functioning epitomizes FIS. Although a detailed discussion on ‘ambidexterity’ is not the major agenda of this research, a brief overview of this construct is, however, important. According to classical scholars, ambidexterity encompasses two main elements: exploration and exploitation. Whereas the first entails activities such as experimentation and risk-taking, the latter embodies efficiency and execution (Rosing & Zacher, 2017). Ambidexterity can be classified into two broad categories: individual ambidexterity and organizational ambidexterity (OA). As already described, the IA construct entails explorative (e.g. searching and creating new opportunities) and exploitative (e.g. selecting and implementing existing certainties) activities (Mom et al., 2007, 2009). OA has been referred to as “dynamic capability” which is based on the processes and activities that enable organizations to manage explorative and exploitative units (Jansen et al., 2009). Whereas exploration and exploitation are common features of OA and IA, the scope and level of analysis (i.e. organizational and individual) is the basic point of demarcation between these constructs.

We build on the underpinnings of the JD-R paradigm (i.e. job resources can encourage employee attitudes and behaviors (Bakker & Demerouti, 2007)) and the concept that “empowerment could act as a means to a goal” when it comes to the relationship between macro facilitators and IA (Matthews et al., 2003, p. 317). Employees who are allowed to participate in decision-making (CWD), encouraged to take risks and learn from their experiences (DSF), and have access to relevant information (FIS) are more likely to feel encouraged and stimulated. As a result, they may engage in more exploration and exploitation. Caniëls et al. (2017) also found that offering employees autonomy and access to strategic resources is crucial to encouraging exploration and exploitation behaviors. Thus, it may be asserted that CWD, DSF, and FIS will be positively related to exploration and exploitation. Many studies have found that the combination of these elements (i.e. OE) is positively related to a variety of desired outcomes. For example, López-Cabarcos et al. (2021) discovered that OE can help employees perform better at work. This is because empowerment functions as an energizer or motivator, encouraging employees to not only take initiative and exert effort but also to persevere in the face of adversity. We also believe that CWD, DSF, and FSI can motivate employees to participate in additional exploration and exploitation activities. Therefore, we propose that:

Figure 1: Research Model

H1: Control of workplace decisions (CWD) is positively related to employees’ explorative and exploitative behaviors.

H2: Dynamic structural framework (DSF) is positively related to employees’ explorative and exploitative behaviors.

H3: Fluidity in information sharing (FIS) is positively related to employees’ explorative and exploitative behaviors.

The hypothesized relationships have been displayed in Figure 1.

3. Research Methods

3.1 Context and Procedure

This study was conducted in academia – a context where the phenomenon of managerial/individual ambidexterity has been explored (Raiden et al., 2020). We chose to study the association between macro facilitators of OE and IA in academia/higher education institutions (HEIs) because academic roles entail both exploration (creative in research and teaching) and exploitation (adherence to set guidelines) activities/behaviors (Raiden et al., 2020).

Data was collected by one of the authors from academics based in five public sector universities in the Balochistan province in Pakistan, using the convenience sampling method (Saunders et al., 2009). According to the Higher Education Commission (HEC) Pakistan, there are 10 universities in Balochistan, of which 09 are public sector universities. Due to access constraints, the data were collected from only 05 public sector universities. Since an accurate list of the target population was unavailable, 500 self-reported paper-pencil surveys were distributed to the academics personally and through the mail. A covering letter containing necessary information (e.g. information regarding the nature and purpose of this study, assurances of the anonymity and confidentiality of respondents’ information, and necessary instructions) was also attached with the survey. To avoid the possibility of social desirability bias, respondents were requested to rate survey items honestly (Anjum et al., 2021; Podsakoff & Organ, 1986; Podsakoff et al., 2003). After the stipulated time of one week, respondents were approached and requested for completed surveys. Those who did not complete the survey within the stipulated time were allowed to return the filled survey after one week. The final sample for this study comprised 240 workable surveys, indicating a response rate of 48%, which is similar to the average response rate criteria suggested by Baruch and Holtom (2008).

The sample size for this study is also consistent with the minimum sample size requirement for multiple regression – the analytical technique used in this study (For a detailed discussion on sample size requirements for multiple-regression see, Hair et al., 2010). Of the 240 respondents, 127 (52.9%) were male and 113 (47.1%) were female. The average age and work experience of respondents were 30.34 years (SD = 5.38), and 5.15 years (SD = 4.34), respectively. 33 (13.8%) respondents had a Bachelor’s degree (the equivalent of 16 years), 66 (27.5%) had a Master’s degree, 137 (57.1%) had an M. Phil degree, and 04 (1.7%) had a Ph.D. degree. The designation wise detail of respondents was as follows: Lecturer = 198 (82.5%), Assistant Professor = 34 (14.2%), Associate Professor = 06 (2.5%), and Professor = 02 (.80%).

3.2. Measures

Employees’ perceptions regarding the macro facilitators of OE were measured using a 19-item Organizational Empowerment Scale (OES), which comprises three subscales: CWD (07 items), DWF (07 items), and FIS (05 items) (Matthews et al., 2003). Five response options (strongly disagree = 1 to strongly agree = 5) were provided. Sample items included: “Employees in your organization have a say in defining their job responsibilities” (CWD); “Thinking out of the box” behavior is appreciated in your organization” (DSF); “Your organization provides employees with information on the organization’s clients/stakeholders” (FIS). The Overall/ composite reliability coefficient for 19 items in this study was 0.853. The reliability coefficients for the subscales were as follows: CWD = 0.687, DSF = 0.745, and FIS = 0.609.

The extent of respondents’ ambidexterity (exploration and exploitation) was assessed using a slightly adapted 11-item scale (Mom et al., 2007). Specifically, respondents’ exploration behavior was measured with 05 items, while their exploitation behavior was assessed with 06 items. Sample items were: “During last year in your job, to what extent did you search for new possibilities with respect to services, processes or study programs” (exploration), and “During last year in your job, to what extent did you engage in activities of which a lot of experience has been accumulated by yourself” (exploitation). The anchors for this scale ranged from “to an extremely small extent (1)” to “to an extremely large extent (5)”. The composite reliability coefficient for the IA scale was 0.875. The reliability coefficients for exploration and exploitation were 0.802, and 0.846, respectively, which are similar to that reported by Mom et al. (2007).

3.3. Control Variables

Consistent with Mom et al. (2009), the effects of respondents’ age, work experience, and level of education were controlled.

4. Results

Because the data was cross-sectional and single- source, Harman’s single factor test via exploratory factor analysis was conducted to check for common method bias/variance (CMB/CMV). The percentage of the single factor’s cumulative variance was 24.59 (Eigenvalue = 7.37), which is less than the threshold of 50%, indicating no serious issue of CMB/CMV (Podsakoff et al., 2003). Inter construct correlations were then analyzed (Table 1). As expected, the relationships between all macro facilitators of OE and IA dimensions were positive and significant [CWD and exploration: r = 0.360, p < 0.01; CWD and exploitation: r = 0.359, p < 0.01; DSF and exploration: r = 0.365, p < 0.01; DSF and exploitation: r = 0.333, p < 0.01; FIS and exploration: r = 0.235, p < 0.01; FIS and exploitation: r = 0.239, p < 0.01], providing preliminary support for the hypotheses.

Following correlation analysis, a two-step regression analyses was performed (see Table 2 and Table 3). Age (β = 0.051, p>0.05), work experience (β = −0.035, p > 0.05), and education (β = 0.083, p > 0.05) has nonsignificant relationship with exploration behavior (Table 2). However, CWD and DSF both accounted for statistically significant variation in exploration (CWD → Exploration: β = 0.217, p < 0.05; DSF → Exploration: β = 0.211, p < 0.05). Contradictory to our expectations, FIS failed to explain any variation in exploration behavior (β = −0.013, p > 0.05). The overall model summary results were as follows: R = 0.410, R = 0.168, ΔR = 0.147, F = 7.849, p = 0.000, Durbin-Watson = 1.746. Collinearity statistics were reported as: CWD_(Tolerance) = 0.520, CWD_(VIF) = 0.1.925; DSF_(Tolerance) = 0.518, DSF_(VIF) = 0.1.931; FIS_(Tolerance) = 0.619, FIS_(VIF) = 1.616.

Table 1: Bivariate Correlations

n = 240, **p < 0.01.

Table 2: Regression Analysis (Model 1)

Dependent variable = exploration.

Table 3: Regression Analysis (Model 2)

Dependent variable = exploitation.

Similar to that of Model 1, all control variables had nonsignificant relationships with exploitation (age: β = −0.007, p > 0.05; work experience: β = 0.023, p > 0.05; education: β = 0.081, p > 0.05). Of the three macro facilitators of OE, only CWD accounted for a statistically significant variance in exploration (β = 0.237, p < 0.05). The overall model summary results were as follows: R = 0.392, R² = 0.154, ΔR² = 0.132, F = 7.061, p = 0.000, Durbin- Watson = 1.873. Collinearity statistics for the Model 2 were follows: CWD_(Tolerance) = 0.520, CWD_(VIF) = 0.1.925; DSF_(Tolerance) = 0.518, DSF_(VIF)= 0.1.931; FIS_(Tolerance) = 0.619, FIS_(VIF)= 1.616.

In partially summary, H1 was supported, H2 was partially supported, and H3 was not supported.

5. Discussion and Implications

The current study looked into the interaction between the macro facilitators of OE and IA, which was an interesting gap in the empowerment and ambidexterity literature (exploration and exploitation). The correlation analysis found that all of the variables had statistically significant and predictable correlations. Regression analyses, on the other hand, yielded different results. CWD was expected to have a positive relationship with exploration and exploitation (H1). This hypothesis was supported by both correlation and regression studies, showing that employees’ perceptions of CWD are a significant component in their exploration and exploitation behaviors. Based on this conclusion, higher education institutions (HEIs) should provide the CWD to their employees (academics), i.e. HEIs should allow their staff to participate in choices that affect their jobs. Although allowing employees to control their workplace decisions and careers is difficult in public sector universities due to a highly bureaucratic and formalized structure, decision-making authorities (e.g. Chairpersons and Deans) may be able to do so, allowing academics to participate in decisions such as class scheduling and rescheduling. They may also be given the flexibility to complete their academic coursework and/ or other academic tasks on their own time. Academics may feel ‘empowered’ as a result of such initiatives, leading them to engage in greater exploration and exploitation activities.

DSF would be positively related to exploration and exploitation, according to H2. Although DSF was found to be positively associated both with exploration and exploitation behaviors in correlations analysis, DSF only had a statistically significant association with exploration in the subsequent analysis (i.e. regression); thus, H2 was partially supported. DSF’s non-significant relationship with exploitation is a bit surprising. Because DSF entails both top-down (relational) and organic (bottom-up) elements of empowerment and occurs when organizations provide clear but modifiable guidelines, it should have encouraged employees to engage in more exploitation because exploitation activities in academia require adherence to some explicit guidelines (e.g. compliance with quality assurance; Raiden et al., 2020). The non-significant relationship between DSF and exploitation, on the other hand, merits further research. The fact that DSF has a strong positive link with exploration is in line with the underlying characteristics of its organic element (e.g. risk-taking and learning from the failures and new experiences). We propose that DSF be strengthened to foster exploration in academia based on this finding.

Finally, FIS did not explain for significant variation in exploration and exploitation, contrary to our expectations (H3). Based on the findings, it can be confidently stated that disclosing critical knowledge about the organization’s operations has no effect on academics’ exploratory and exploitative actions. Future researchers are invited to confirm these findings by collecting samples from different parts of Pakistan.

6. Conclusion and Limitations

The study of direct linkages between macro facilitators of OE and IA emphasizes the important role of academic empowerment. The findings are susceptible to several limitations that necessitate further investigation. First, the scope of this study is constrained since the model it evaluates is relatively narrow, i.e., it only examines the direct relationship between CWD, DSF, FIS, and IA, and key elements that might underpin or impact those putative relationships were not examined. As a result, researchers are urged to investigate the CWD/DSF/FISIA interactions’ moderating and mediating processes. We suggest looking into the mediating role of psychological empowerment (Siswanti & Muafi, 2020) and motivation, as well as the moderating role of personality traits and organizational culture in this nexus (Siswanti & Muafi, 2020). Second, the data used in this investigation was cross-sectional, limiting the ability to draw causal inferences about the associations between CWD, DSF, and FIS and IA. This constraint, however, can be overcome by getting time-lagged and multi-source data. Third, convenience sampling was used to collect data for this study from only five public colleges. The findings’ generalizability is hampered as a result. Researchers are advised to do a replication of the current work using samples from other Pakistani universities.