About the Author(s)


Edson Badarai symbol
Faculty of Economic and Management Sciences, Department of Industrial Psychology, University of the Free State, Bloemfontein, South Africa

Martina Kotze Email symbol
Faculty of Economic and Management Sciences, Department of Industrial Psychology, University of the Free State, Bloemfontein, South Africa

Petrus Nel symbol
Department of Industrial Psychology and People Management, School of Management, University of Johannesburg, Johannesburg, South Africa

Citation


Badarai, E., Kotze, M., & Nel, P. (2023). A leadership-organisational performance model for state-owned enterprises in emerging economies. South African Journal of Business Management, 54(1), a3148. https://doi.org/10.4102/sajbm.v54i1.3148

Original Research

A leadership-organisational performance model for state-owned enterprises in emerging economies

Edson Badarai, Martina Kotze, Petrus Nel

Received: 25 Jan. 2022; Accepted: 26 Sept. 2022; Published: 03 Mar. 2023

Copyright: © 2023. The Author(s). Licensee: AOSIS.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose: Poor leadership skills are often one reason for poor performance in emerging economies’ state-owned enterprises (SOEs). Research on transformational leadership’s (TL) effectiveness in the public sector is limited and sometimes contradictory and incomplete. The present study sought to develop and test a TL and organisational performance (OP) model that includes the role of soft influence tactics and leader–follower relationship quality.

Design/methodology/approach: Quantitative predictive research was used. The Multifactor Leadership Questionnaire-5X, Influence Behaviour Questionnaire, Leader-Member Exchange Questionnaire, and an adapted measure of OP were used to collect data from 302 staff members from 12 SOEs and government officials from line Ministries in an emerging economy. Variance-based structural equation modelling was used in data analysis.

Findings/results: The findings show that the relationship between TL and OP is complex. Although TL directly influences OP, it also does so through soft proactive influence tactics (sPIT) and leader–follower relationship quality. Transformational leadership, proactive influence tactics and leader–follower relationship quality combined explained 47% of the variance in OP.

Practical implications: State-owned enterprise leaders and management should take note that TL, proactive influence tactics and the quality of leader–follower relationships can be integrated to influence OP positively and significantly.

Originality/value: This research provides additional knowledge to the limited research available on SOEs in emerging economies. Furthermore, it reveals that sPITs and leader–follower relationship quality influence the relationship between TL and OP in these SOEs. This addresses a knowledge gap concerning the leadership-OP relationship.

Keywords: state-owned enterprises; organisational performance; transformational leadership; soft influence tactics; quality of leader–follower relationships; performance of SOEs.

Introduction

State-owned enterprises (SOEs) in developing countries are essential service providers in almost all sectors of the economy and play an increasing role in economic and socio-political matters (Donkor & Zhou, 2019; Kikeri, 2018; Masekoameng & Mpehle, 2018; Mbo, 2017; Sithomola, 2019). However, many SOEs in emerging economies often face corruption, poor leadership and loss-making (Desderio, 2016; Gallup, 2021; Sithomola, 2019). As organisational leadership is a significant determinant of goal attainment and daily operations management (Donkor & Zhou, 2019), effective leadership practices and competencies, together with employee performance are key factors contributing to organisational performance (OP) and growth (Almatrooshi et al., 2016; Asamoah, 2017). Leaders in SOEs deal with complex situations as they are accountable to a larger group of stakeholders, often having to ‘please as many people as possible whilst achieving results’ (Bezuidenhout, 2021, p. 2). Poor SOE performance and lack of effective leadership raise questions regarding the type of leadership needed to improve performance (Baxter et al., 2008; Zoogah, 2009).

It is argued that competent leadership is ‘built upon various variables and characteristics, including values, knowledge, intellectual drive, ethics, charisma, creativity, self-confidence, and courage’ and that a competent leader ‘has a purpose’ and ‘skills that can be used to put purpose behind deeds’ (Almatrooshi et al., 2016, p. 847). Previous studies found that employees achieve better results under transformational leadership (TL) than other leadership types (Dvir et al., 2002; İşcan et al., 2014; Peterson et al., 2009) and that transformational leaders can play a role in ensuring organisational success (Aziz et al., 2013). Some scholars suggest that TL is a means to drive SOE performance (Chinguruve, 2019; Desderio, 2016; Dvir et al., 2002; Mabasa, 2018) as it has been shown to enhance employee commitment in SOEs, and to increase work engagement, pro-social behavioural intentions and job and OP (Lai et al., 2020; Salim & Rajput, 2021). It is also effective in uncertain environments (Nemanich & Keller, 2007) and across different cultures (Avolio et al., 2009). Transformational leadership’s emphasis on the organisation’s mission and outcomes makes this model relevant to the public sector (Wright et al., 2012).

However, there is no clear understanding of TL’s influence on OP as the association between TL and OP is often indirect. Several authors found that TL is correlated with employee performance through leader–follower relationship quality (Carter et al., 2013; Mayfield & Mayfield, 2009). Others emphasise the importance of soft influence tactics in this relationship (Bochenko et al., 2015). Mehta and Krishnan (2004) showed that although managers make use of a combination of tactics to achieve a certain outcome, transformational leaders mostly apply soft influence tactics. These tactics include rational persuasion, consulting with employees and making inspirational and personal appeals to followers, leading to the perception that a leader is motivational and appealing (Bochenko et al., 2015; Falbe & Yukl, 1992; Mehta & Krishnan, 2004).

Some researchers highlight that soft influence tactics and the quality of leader–follower relationships (qLFR) in the TL-OP nexus are often ignored and understudied (Lapierre & Hackett, 2007; Sparrowe et al., 2006; Yukl & Tracey, 1992). Previous studies on the effect of soft influence tactics and leader–follower relationship quality have not investigated its influence as part of the TL and OP relationship, but rather independently and separately – highlighting the need to develop a comprehensive model of these factors (Atmojo, 2015; Lo et al., 2009). Furthermore, research on TL’s effectiveness in the public sector is limited, and at times, contradictory and incomplete, with calls to re-examine the leadership–performance relationship (Chinguruve, 2019; Cristina & Ticlau, 2012). This study aimed to develop a theoretically defensible, predictive TL and OP model for SOEs in an emerging economy that goes beyond these separate dual relationships. The proposed model depicts TL’s influence on OP from a broader perspective by including the role of soft influence tactics and leader–follower relationship quality.

Literature review

Organisational performance and transformational leadership

Organisational performance has been defined as the outcome of work, which links organisational strategic goals with customer satisfaction and economic contributions (Salem, 2014). It denotes whether an organisation does well in its administration and operational functions and producing outputs towards fulfilling the mission (Asencio, 2016; Kim, 2004). It can also be defined as ‘internal and external outcomes of work in pursuit of the organisation’s vision and how well the outcomes fulfil the various stakeholders’ expectations’ (Badarai, 2020, p. 9). In SOEs specifically, OP needs to be measured to make better management decisions and for government to stay informed of the effectiveness of these enterprises for society (Burksiene & Dvorak, 2020).

There are several frameworks for measuring OP. These include the use of qualitative data (e.g. employee morale and innovation) and quantitative data (e.g. quantities produced) (Verbeeten, 2008) – some objective and others more subjective. Some of the most common measures include benchmarking (Erdil & Erbiyik, 2019; Zope et al., 2019), financial performance measures (e.g. profitability, asset management, sales and investors’ ratios) and the Balanced Score Card (Krajewski et al., 2010; Mendes et al., 2012). The public sector’s focus includes financial and social objectives and is affected by various internal and external stakeholders with different perspectives. These perspectives determine how organisational outcomes are interpreted – often leading to competing demands and expectations (Lindquist & Marcy, 2014; Martz, 2008). As multiple goals are pursued simultaneously, multi-dimensional performance evaluation systems are more effective in the case of public organisations (Mihaiu, 2014). The Competing Values Framework (Quinn & Cameron, 1983) is such an OP framework that recognises that organisational goals are ‘simultaneously pulled in opposite directions by the expectations of multiple constituencies’ (Lee, 2004, p. 22).

Transformational leadership is usually conceptualised as consisting of four dimensions: charisma or idealised influence, inspirational motivation, intellectual stimulation and individualised consideration through which leaders ensure individuals transcend personal interests and pursue the interests of the organisation and group (Bass & Riggio, 2006; Cavazotte et al., 2013). Transformational leaders foster innovation that boosts performance and motivates followers to go beyond self-interest and focus on higher organisational goals (Noruzy et al., 2013; Obiwuru et al., 2011). In both developed and developing countries, a positive relationship between TL and OP is apparent (Desderio, 2016; İşcan et al., 2014; Koech & Namusonge, 2012; Omira, 2015; Peterson et al., 2009). Studies in the private and public sector in Iran (Noruzy et al., 2013) and Saudi Arabia (Mutahar et al., 2015) show that TL has a strong positive correlation with OP, while findings from a study in Malaysian public universities observed that TL can positively influence OP through improved knowledge-sharing among followers (Wahab et al., 2016).

Edoka (2012) emphasises the need for African countries to adopt TL to improve employee performance. Two studies conducted in Kenya in SOEs and state corporations concluded, respectively, that idealised influence and inspirational motivation positively and significantly increase staff performance in SOEs, and that all TL behaviours have a strong positive correlation with OP (Koech & Namusonge, 2012; Ngaithe et al., 2016). A South African study in SOEs and parastatals (Dhanphat et al., 2015) indicated that TL positively influenced employee performance and, consequently, OP. This happens because transformational leaders develop plans and goals for empowered followers, build trust, and motivate them to perform well, thereby promoting behaviour that assists in achieving organisational goals. A study in two Zimbabwean SOEs concluded that transactional and transformational democratic leadership styles encourage employees to have a sense of belonging, to embrace more responsibility with less supervision, and to achieve organisational efficiency (Chinguruve, 2019).

Based on previous research, the following hypothesis was formulated:

Hypothesis 1: Transformational leadership has a statistically significant influence on organisational performance.

Transformational leadership and soft proactive influence tactics

Influence over others is an indicator of power in relationships (Bochenko et al., 2015). Leaders often apply proactive influence tactics to persuade followers to comply with immediate requests (Yukl & Michel, 2006). Yukl et al. (2008) identified 11 proactive influence tactics that leaders can use: rational persuasion, inspirational appeals, consultation, collaboration, apprising, personal appeals, ingratiation, exchange, legitimating, pressure and coalition. A distinction is made between hard influence proactive tactics (e.g. pressure, assertiveness, upward appeals, legitimating and coalition), soft influence proactive tactics (e.g. rational persuasion, consultation, inspirational appeal, collaboration and personal appeal) (Yukl et al., 2008) and ‘influence tactic ambidexterity’ (the frequent use of both soft and hard influence tactics) (Kapoutsis et al., 2016, p. 3). Soft proactive influence tactics (sPITs) are affective and involve friendly, polite tactics, whereas hard proactive influence tactics are more coercive, direct and controlling, and focus more on cognitive factors (Kapoutsis et al., 2016; Van Knippenberg & Steensma, 2003). Some researchers argue that the potential strain placed on the leader–follower relationship often encourages leaders to choose soft influence over hard influence tactics (Van Knippenberg & Steensma, 2003).

Regarding soft influence tactics, rational persuasion is where leaders use logic and facts to convince followers that requests are in line with organisational goals and values and that the request would produce good results (Barbuto & Warneke, 2014; Yukl & Tracey, 1992). Consultation is when leaders invite followers to contribute to planning, decision making and assessing complex situations. Inspirational appeals are when a leader ignites enthusiasm in followers through appeals to ideals, values and goals (Barbuto & Warneke, 2014; Yukl & Tracey, 1992). Such appeals can enhance followers’ confidence in carrying out a task. With collaboration, leaders offer to support followers in carrying out requests and provide enough resources (Charbonneau, 2004; Yukl & Michel, 2006). Personal appeals refer to when leaders appeal to followers’ emotions (Barbuto & Warneke, 2014; Yukl & Tracey, 1992).

There are few studies on the relationship between TL and sPITs. A study conducted in various industries in Malaysia found a positive and significant relationship between TL and soft influence tactics (Lian & Tui, 2012). The study highlighted that TL positively and significantly predicted inspirational appeal and consultation. These soft influence tactics encourage a more satisfactory, cooperative and stable relationship between leaders and followers (Lian & Tui, 2012; Yukl et al., 2008). Inspirational appeal influence tactics, which encompass requests based on ideals, values and aspirations, stimulate followers’ emotions through vivid discussions and symbols (Charbonneau, 2004; Lian & Tui, 2012). It also ignites followers’ emotional responses, leading them to carry out tasks with enthusiasm, while increasing their self-confidence (Lian & Tui, 2012; Yukl, 2002). Previous research in the Canadian manufacturing industry and military supports the relationship between TL and soft influence tactics (Charbonneau, 2004; Clarke & Ward, 2006). It was observed that rational persuasion and other soft influence tactics are frequently used by transformational leaders and are more effective than hard tactics (Clarke & Ward, 2006). A link was found between consultation tactics (engaging followers in decision making) and TL. The intellectual stimulation dimension, whereby leaders use followers’ ideas to complete challenging tasks, is linked to the consultation tactic.

In the light of the above, the following hypothesis was formulated:

Hypothesis 2: Transformational leadership has a statistically significant influence on soft proactive influence tactics.

Transformational leadership and quality of leader–follower relationships

As organisations struggle to thrive in a complex world, leader–follower relationships are crucial (Martin, 2015). The development of these relationships is not incidental but based on the degree of exchange and mutual influence (Walthall & Dent, 2016). The leader–follower relationship can be viewed as a collaboration or direct one-to-one relationship involving mutual trust, respect and influence (May-Chiun et al., 2015). This relationship can be explained by the Leader-Member Exchange Theory (LMX) (Dansereau et al., 1975). This theory is concerned with the ‘dyadic relationship between the leader and follower’ and assumes that leaders form individualised relationships with each follower (Walthall & Dent, 2016, p. 8). It proposes that leader–follower relationship quality depends on trust, interaction level, support and reward, and that high-quality relationships are characterised by mutual trust, support, loyalty, professional respect, work contribution and understanding (Jyoti & Bhau, 2015). Followers benefit from effective communication and leader’s support, and they gain the leader’s trust and approval. Furthermore, followers have autonomy in decision making and receive favourable assignments (Graen & Uhl-Bien, 1995). In exchange for these benefits, followers often reciprocate with higher performance (Liden et al., 1997).

Various researchers posit that TL significantly and positively affects leader–follower relationship quality, which consequently positively affects job performance (Jyoti & Bhau, 2015; Wang et al., 2005). Previous studies found a positive relation between leader–follower relationship quality and TL and note that transformational leaders who emanate trustworthiness and fairness promote positive attitudes towards the leader and foster good relationships (Carter et al., 2013; Martin, 2015; Yukl et al., 2008). Furthermore, TL fosters mutual stimulation and advancement that converts followers into leaders (Burns, 1978). A study in the U.S. public and private sector found transformational leaders support, recognise, develop and consult with followers and delegate effectively through the individual consideration dimension (Yukl et al., 2008). Followers reciprocate with a positive attitude towards the leader, and become committed to and build trust with the leader, improving the relationship (Graen & Uhl-Bien, 1995). A study in Indian government colleges found that ‘idealised influence, intellectual stimulation, inspirational motivation and individual consideration’ positively influenced the development of leader–follower relationships (Jyoti & Bhau, 2015, p. 8). Intellectual stimulation helps followers think creatively and find new ways of solving problems. Transformational leadership improves leader–follower relationship quality by ensuring people become their best self. These leaders are friendly, provide individualised attention (individualised consideration dimension), and motivate and satisfy followers (inspirational motivation). Individualised consideration helps in solving followers’ work- and life-related problems, consequently building high-quality leader–follower relationships and resulting in satisfied followers (Bodla & Nawaz, 2010). Some authors suggest that TL can be the foundation of the leader–follower dyad to develop shared leadership, as TL approaches ‘assist in the development of leader–follower relationships where followers take charge, challenge their leaders’ viewpoints and engage in leadership behaviours themselves’ and that shared leadership then ‘emerges through reciprocation of leadership between fellow team members’ (Hernandez et al., 2011, pp. 1177–1178).

Therefore, the following hypothesis was formulated:

Hypothesis 3: Transformational leadership has a statistically significant influence on leader–follower relationships.

The relationship between soft proactive influence tactics and quality of leader–follower relationships

A study in the public and private sector on the association between soft influence tactics and leader–follower relationship quality showed that in high-quality relationships, the most frequently used sPITs are rational persuasion, consultation, inspirational appeals and collaboration (Yukl & Michel, 2006). These findings were confirmed in a later study, where it was highlighted that the impact of using different proactive influence tactics is observable in a leader–follower relationship (Yukl et al., 2008). Certain proactive influence tactics applied by the leader may affect the future relationship with the follower. The quality of existing relationships can affect the leader’s choice of proactive influence tactics applied in the relationship, pointing to a possible reciprocal relationship between leader and follower (Lo et al., 2009; Sparrowe et al., 2006). When leaders use collaboration as a sPIT, they support followers by providing adequate resources, helping followers execute tasks, and thereby increasing the positive affect of followers towards the leader (Yukl & Michel, 2006). This could result in the followers reciprocating with supportive gestures, which cultivate a good relationship. With inspirational appeals, leaders make a request based on ideals and values, communicate a vision of a better future and invoke emotions in followers. This tactic can ignite enthusiasm in followers, especially where the ideals and values being pursued align with those of the followers. The leader and follower have a common vision, which helps build high-quality leader–follower relationships. When rational persuasion (reasoning) is applied in influencing a follower to carry out a task, such influence may face less or no resistance, and it helps build good relationships as the leader is believed to make informed decisions rather than ones based on speculation and hearsay (Yukl & Michel, 2006). Several other studies in the private and public sector support the relationship between leaders’ soft proactive tactics and leader–follower relationship quality (Cerado & Rivera, 2015; Lee et al., 2017; Lo et al., 2009). A study in the banking environment in Spain and Italy examined the influence of ‘constructive dissensus’ – ‘a situation of harmony that emerges from the mutual regulation of feelings and behaviours that reduces negative emotions’ – on leader–follower relationships, highlighting the importance of considering interactions in leader–follower relationships that provide necessary openness and optimism (Salas-Vallina, 2020, pp. 1, 6).

In light of the above, the following hypothesis was formulated:

Hypothesis 4: Soft influence tactics have a statistically significant influence on leader–follower relationships.

The relationship between quality of leader–follower relationships and organisational performance

Research suggests that leader–follower relationship quality influences employee and OP (Walthall & Dent, 2016). A study in a Malaysian financial services company observed that leader–follower relationship quality positively and significantly contributed to OP (May-Chiun et al., 2015). This was mainly because, in high-quality relationships, followers are comfortable with their leader, and the leader treats followers in ways that fit specific followers as individuals. Another study in various Nigerian organisations in different sectors identified mutual trust, confidence, commitment, open communication, respect, reward and recognition between leaders and followers in high-quality relationships, leading to improved OP (Gilbert et al., 2013). For organisations seeking to enhance performance, leaders and followers must build high-quality relationships. A related study by Tariq et al. (2014) in the home appliance industry in Pakistan showed that leader–follower relationship quality improved OP by 48%. The leaders provided support to followers, which enhanced job satisfaction and OP.

Other studies in Indian and Chinese SOEs found a connection between leader–follower relationship quality and OP (Chaurasia & Shukla, 2013; Loi et al., 2011). It is suggested that in high-quality leader–follower relationships, there is more latitude in decision making, resource provision, motivation enhancement, support from the leader and feedback (Loi et al., 2011). These factors contribute to followers working hard, thereby enhancing OP. Psychological support offered by transformational leaders to followers enhances leader–follower relationship quality, which in turn improves OP (Ng, 2017). Encouraging followers to take on more responsibilities and be proactive and committed eventually improves OP (Tariq et al., 2014).

In light of the above, the following hypothesis was formulated:

Hypothesis 5: The quality of leader–follower relationships has a statistically significant influence on organisational performance.

Based on the above hypotheses, a conceptual model was developed (Figure 1).

FIGURE 1: Proposed transformational leadership and organisational performance conceptual model.

Based on the conceptual model, the following hypothesis was formulated:

Hypothesis 6: The conceptual transformational leadership and organisational performance model demonstrates predictive validity in SOEs.

Methodology

Sample

A convenience sample of 302 non-managerial and managerial staff at 12 SOEs and government officials from line Ministries in Zimbabwe were included. The SOEs represented the energy, health, financial, transport, petroleum, power and telecommunications sectors. Ethical clearance was granted by the ethics committee of the Faculty of Economic and Management Sciences (University of the Free State). The committee suggested that no biographical data be obtained from the sample to protect participants. Therefore, ethical clearance was only granted for participants to complete the survey questions. Participation was voluntary and anonymous.

Measurement instruments
Multifactor leadership questionnaire

The multifactor leadership questionnaire-5X (MLQ-5X) measured five dimensions of TL: idealised influence (attributed), idealised influence (behaviour), stimulation, individualised consideration and inspirational motivation (Bass & Avolio, 2004; Cavazotte et al., 2013). Only 20 out of 45 questions on TL dimensions (four items per dimension) from the MLQ were used. This approach is consistent with other TL studies (Alsayed et al., 2012; Altahayneh & Wezermes, 2008; Barnes et al., 2013; Hemsworth et al., 2013; Moore & Rudd, 2006). The rating of the above dimensions is based on a five-point Likert scale with the following rating scale: ‘Not at all’ (0), ‘Once in a while’ (1), ‘Sometimes’ (2), ‘Fairly often’ (3), and ‘Frequently, if not always’ (4). Examples of questions include; ‘The person I am rating provides me with assistance in exchange for my effort’, and ‘The person I am rating is absent when I need him/her’.

Influence behaviour questionnaire

To quantify followers’ perceptions of their leaders’ use of soft proactive tactics, the influence behaviour questionnaire (IBQ-G) was used (Yukl et al., 2008). The IBQ-G evaluates the use of five sPITs (four items per scale): rational persuasion, consultation, inspirational appeal, personal appeal and collaboration. The response scale varies from ‘I can’t remember him/her ever using this tactic with me’ to ‘He/she uses this tactic very often with me’ (Alshenaifi & Clarke, 2014). Mean scores for each proactive influence tactic were calculated.

Leader-member exchange theory 7 questionnaire

The LMX-7 Questionnaire measured leader–follower relationship quality. It has seven items rated on a five-point Likert scale: ‘Rarely’ (1), ‘Occasionally’ (2), ‘Sometimes’ (3), ‘Fairly often’ (4) and ‘Very often’ (5). Examples of items include, ‘How does your leader recognise your potential?’ (Graen & Uhl-Bien, 1995).

Organisational performance

An adapted measure of 85 items was utilised to measure OP (Eydi, 2013; Minvielle et al., 2008). The adapted measure takes into account how organisations cater for various stakeholders’ competing interests and values, rather than using single performance criteria, such as financial measures only. Four performance criteria were measured: rational goals (22 items), human relations (23 items), open systems (22 items) and internal processes (18 items) (Eydi, 2013; Minvielle et al., 2008). Questions were adapted to make them more relevant to the SOE context. A seven-point Likert scale was used to measure how often the SOE successfully engaged in each activity: ‘Never’ (1), ‘Very seldom’ (2), ‘Seldom’ (3), ‘Occasionally’ (4), ‘Frequently’ (5), ‘Very frequently’ (6) and ‘Almost always’ (7).

Data analysis

The variance-based approach to Structural Equation Modelling (SmartPLS) was used to test the conceptual model. When evaluating variance-based structural equation models, a two-step process is followed. Firstly, the outer model is evaluated for reliability and validity in terms of the quality of measures used to evaluate the inner model (representing the proposed paths). The values associated with the composite reliability should be 0.70 and higher. All indicators should have significant loadings on their respective latent variables. In terms of validity, the Average Variance Extracted (AVE) should have values of 0.50 and higher (Goh & Wasko, 2012; Henseler et al., 2009). An AVE of at least 0.5 means the construct accounts for the majority of the variance (Götz et al., 2010; Henseler et al., 2009).

The second step involves the evaluation of the strength (ß-values) and statistical significance (t-values) of proposed paths in the conceptual model (Henseler et al., 2009). The inner model should be evaluated for explanatory power (or predictive ability), with an emphasis on R² – the level of variance in the endogenous variable explained by the model (Garson, 2016). Chin (1998), as well as Höck and Ringle (2010), maintain that the R² cut-off value of 0.67 represents a substantial effect, 0.33 represents a moderate effect, and 0.19 represents a weak effect. Garson (2016) further suggests that this is relative to the field, and 0.25 may be considered high in given areas with lower values previously.

Findings

Quality criteria of the outer model

The results showed a composite reliability of 0.849 for TL, 0.867 for sPITs, 0.964 for OP, and 1 for qLFRs (Table 1).

TABLE 1: Quality criteria of outer model.

The reliability estimates are higher than 0.7 and thus regarded as acceptable and satisfactory values (Hair et al., 2012; Henseler et al., 2009). These results support the reliability of the measures used in evaluating the proposed conceptual model. Validity is assessed using AVE, with values of 0.50 and above indicating sufficient convergent validity (Goh & Wasko, 2012; Henseler et al., 2009). All four variables had AVEs above 0.50 (Table 1). Hair et al. (2012) recommend maintaining indicator values above 0.40 for the outer loadings. All indicators in the present study have loadings above 0.40. Furthermore, all the indicators had statistically significant factor loadings on their respective latent variables (Table 2).

TABLE 2: Outer loadings.

As the measures used to evaluate the proposed conceptual model had sufficient reliability and validity, the study investigated the strength of relationships between the latent variables.

Quality criteria of the inner model

Regarding the strength and significance of path coefficients, it should be noted that ‘the individual path coefficients’ significance is assessed using a bootstrapping procedure’ (Hair et al., 2012). T-values above 1.96 are considered significant at the 0.05 level (5%) (Garson, 2016). Statistically non-significant paths (t-value under 1.96) do not support the model hypothesis. In contrast, the paths which are significant (t-value above 1.96) support the hypothesis of the model (Henseler et al., 2009).

It is evident from the results in Table 3 that all proposed paths in the theoretical model are statistically significant, except between TL and quality of subordinate relationships (β = 0.101, t = 1.56).

TABLE 3: Path coefficients of inner model.

The results of the present study thus found support for Hypotheses 1, 2, 4 and 5. All proposed paths in the conceptual model were supported, except for the proposed path of Hypothesis 3, which hypothesised that TL has a statistically significant influence on leader–follower relationships.

Table 4 represents the specific indirect effects. Although TL has a direct influence on OP, it also indirectly influences OP via soft influencing tactics and qLFRs (which was statistically significant: effect = 0.032, p = 0.048). The indirect effect of TL on leader–follower relationship quality via soft influencing tactics is also significant (effect = 0.35, p = 0.000). This result may be the reason why Hypothesis 3 was unsupported. It is evident that TL influences OP directly and indirectly through other variables: the use of sPITs and qLFR (Figure 2). Thus, the relationship between TL and OP is partially mediated. The effect of TL on OP is independent of the indirect effect via soft influencing tactics and leader–follower relationship quality.

TABLE 4: Specific indirect effects.
FIGURE 2: Final model.

To determine whether the proposed conceptual model has predictive validity within SOEs (Hypothesis 6), the R² values were consulted. The independent variables in the model explained 47% of the variance in OP, which can be interpreted as moderate (Chin, 1998). Therefore, Hypothesis 6 was supported.

Discussion of findings

The direct relationship between TL and OP was first tested in the conceptual model (Hypothesis 1). Using path coefficients of SmartPLS, the TL to OP path (TL → OP) was found to be statistically significant (β = 0.64; t = 15.59). These results are consistent with previous research showing that TL positively influences OP (Dhanphat et al., 2015; Wahab et al., 2016). Furthermore, these results are in line with previous research conducted in Zimbabwean SOEs, demonstrating a significant and strong positive relationship between TL and OP (r = 0.6, Sig. < 0.05) (Desderio, 2016). The positive influence of TL on OP results from transformational leaders’ ability to motivate followers to make extra effort and focus on the whole organisation rather than self-interests alone, and to encourage innovation, goal achievement, pride and optimism about the future, thereby achieving better OP (Avolio, 2007; Desderio, 2016; Obiwuru et al., 2011; Wahab et al., 2016). Besides the above reasons for improved performance, the transformational leaders’ development of organisational goals and empowerment of followers also aids in improving OP (Dhanphat et al., 2015). The results support Hypothesis 1, which proposes that TL has a statistically significant influence on OP.

Regarding the effect of TL on soft proactive influencing tactics (Hypothesis 2), the results from the inner model show that there is a significant path coefficient between TL and proactive influencing tactics (β = 0.696, t = 20.92), and TL explains 49% of the variance in soft proactive influencing tactics. Thus, TL positively and significantly predicts soft proactive influencing tactics. The present study goes beyond establishing a correlational relationship to demonstrate the predictive nature of the relationship between TL and soft proactive influencing tactics. These findings are in line with a study by Lian and Tui (2012) demonstrating a positive and significant relationship between TL and soft proactive influencing tactics and showing that TL positively and significantly predicted two soft influence tactics: inspirational appeal and consultation. This may be because transformational leaders use consultation and inspirational appeals, encouraging a more satisfied, cooperative and stable relationship between transformational leader and follower (Yukl et al., 2008). The results of the present study support Hypothesis 2, which proposes that TL has a statistically significant influence on soft proactive tactics.

The present study did not find support for the path between TL and leader–follower relationship quality (Hypothesis 3) (β = 0.101; t = 1.56). However, support was found for the path between soft proactive influencing tactics and leader–follower relationship quality (β = 0.496; t = 8.48). Therefore, the lack of a direct influence between TL and leader–follower relationship quality could be explained by the indirect relationship where soft proactive influencing tactics possibly mediate the relationship between TL and leader–follower relationship quality, which was found to be the case based on the results associated with the specific indirect effects. In other words, TL influenced the quality of leader–follower relations indirectly through soft proactive influencing tactics. A study by Piccolo and Colquitt (2006) puts the present results into perspective as these authors suggest that while some followers accept the transformational leader’s behaviours towards them, others may resist, as various factors trigger different followership behaviours among employees (see also Xu et al., 2019).

As the transformational leader is dependent on followers’ willingness to surrender power partially through inclination or pressure, the followers’ responses (e.g. resistance to the leader’s behaviours) could impact the development of leader–follower relationship quality (Smircich & Morgan, 1982). Consequently, these responses influence whether TL positively predicts leader–follower relationship quality. Leaders lacking some TL characteristics (idealised influence, inspirational motivation, intellectual stimulation and individualised consideration) are less likely to use positive influencing tactics when interacting with subordinates, resulting in less than optimal leader–follower relationships. The results of the present study do not support Hypothesis 3 proposing that TL has a statistically significant influence on leader–follower relationship quality.

In relation to the influence of soft proactive influencing tactics on leader–follower relationship quality (Hypothesis 4), it was proposed that the more leaders apply soft proactive influencing tactics, the higher the leader–follower relationship quality. However, when leaders use fewer soft tactics, a lower quality leader–follower relationship is expected. Influence tactics can result in ‘both beneficial and disruptive performance outcomes, depending on the context in which they are applied’ (Kapoutsis et al., 2016, p. 3). For example, applying too much pressure and demanding compliance in an assertive and direct manner can affect the popularity of the leader and cause followers to dislike them, subsequently having a negative effect on relationship quality (Kapoutsis et al., 2016).

In the present study, support was found for the path between soft proactive influencing tactics and leader–follower relationship quality (β = 0.496; t = 8.48). These findings resonate with a previous study indicating that soft influence tactics (personal appeals, collaboration, rational persuasion, consulting and ingratiation) contributed to 69% of the variation in the qLFR equality (Cerado & Rivera, 2015). These soft proactive tactics are mostly preferred by leaders as they are friendly and subtle, which consequently persuades followers to carry out tasks freely and to be supportive and loyal to leaders. Soft proactive influence tactics ‘avoid perceived strain in a relationship’ and may ‘contribute to the engagement of all stakeholders’ (Bochenko et al., 2015, p. 37). It challenges followers to focus on shared goals, while the leader supports them in achieving their potential, thereby developing good leader–follower relationships (Cerado & Rivera, 2015). This means that the more a leader applies soft influence tactics, the more likely leader–follower relationship quality will improve. Yukl and Michel (2006) found that in high-quality leader–follower relationships, the most frequently used soft influencing tactics are rational persuasion, consultation, inspirational appeals and collaboration.

With regards to the influence of leader–follower relationship quality on OP (Hypothesis 5), the results demonstrated that the path for qLFRs to OP (qLFR → OP) is statistically significant (β = 0.091; t = 2.105) at a 5% significance level. This indicates that leader–follower relationship quality is a significant predictor of OP. These results support previous studies showing a positive, significant association between leader–follower relationship quality and OP and indicating that high-quality leader–follower relationships are significantly and positively associated with high OP, whereas low-quality leader–follower relationships are associated with low OP (Lapierre & Hackett, 2007; Mayfield & Mayfield, 2009). Lapierre and Hackett (2007) highlighted that the average correlation between leader–follower relationship quality and OP is positive and significant (r = 0.32, p < 0.001). Thus, with good-quality leader-follower relationships, employees experience job satisfaction and their performance improves. It has also been observed that leader–follower relationship quality positively and significantly influences employee performance and hence OP (Lapierre & Hackett, 2007). As the relationship improves, followers reciprocate by improving OP. These relationships are developed early, but mature and become stable over time. In addition, good leader–follower relationship quality increases job satisfaction among followers, which can be instrumental in followers striving to achieve goals, hence resulting in improved OP (Mayfield & Mayfield, 2009). Hypothesis 5, which proposed that leader–follower relationship quality has a statistically significant influence on OP, is supported.

In terms of the predictive validity of the conceptual model wherein it was proposed that TL can influence OP through sPITs and high-quality leader–follower relationships (Hypothesis 6), the model explained 47% of the variance in OP overall. This is indicative of the model’s predictive ability. Theoretically, the model shows how TL influences sPITs, and how sPITs affect leader–follower relationship quality, with a subsequent influence on OP. In addition to TL’s direct influence on OP, it is evident that this relationship may be more complex. The empirical findings indicate that the behaviours of TL (inspirational motivation, individualised consideration, intellectual stimulation and idealised influence) are linked to sPITs (inspirational appeals, collaboration, consultation and rational persuasion). Soft proactive tactics then influence leader–follower relationship quality, which subsequently influences OP. Therefore, Hypothesis 6 is supported.

Conclusion

Various researchers call for the integration of TL theory and influence tactics theory, and the combined application of influence tactics theory and LMX to explain their effects on OP. The aim of this study was to develop and test a TL and OP model that includes the role of soft influence tactics and leader–follower relationship quality. The integrated conceptual model provided empirical evidence and theoretical explanations for the integration of these variables. The study – which included a sample of 12 SOEs in Zimbabwe – points to a complex relationship between TL and OP in which TL was found to influence OP through other variables in the form of sPITs and the qLFRs. It is recommended that SOEs take deliberate steps to develop managers’ and potential managers’ TL attributes, and to apply sPITs effectively to contribute to quality leader–follower relationships, as this can positively influence OP.

Acknowledgements

Competing interests

The authors have declared that no competing interest exists.

Authors’ contributions

E.B. was responsible for the data-gathering; project administration and obtaining the necessary resources. M.K. was involved in the conceptualisation of the research; the writing of the introduction and literature review; the review and editing of the article and supervision. P.N. was responsible for the visualisation; methodology; data analysis; and writing of the results, discussion and conclusion.

Ethical considerations

Before data collection commenced, ethical clearance was granted by the research ethics committee of the Faculty of Economic and Business Sciences at the University of the Free State (UFS-HSD2017/1150). The committee suggested that no biographical data be obtained from the sample to protect participants. Therefore, ethical clearance was only granted for participants to complete the survey questions. Participation was voluntary and anonymous. Participants were informed of the purpose of the study, as well as their right to withdraw if they so wished. As part of the informed consent process, it was explained to participants that their responses would be anonymous (22 September 2017).

Funding information

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Data availability

The data that support the findings of this study are available on request from the corresponding author.

Disclaimer

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.

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