Index

SME’s leadership in improving performance

Dorothea Wahyu Ariani 

Management Department, Economics Faculty of Universitas Mercu Buana Yogyakarta, Indonesia.

Abstract

The discussions about leadership and performance never end. Successful leadership (LD) is a causal factor in good individual performance (IP). However, how it affects still requires a lot of proof. This research examined the influence of LD on IP both directly and mediated by individual internal variables. This research examined the mediating variables of the influence of LD on IP. The research was conducted on 416 employees of small and medium enterprises (SMEs) in Yogyakarta, Indonesia using a questionnaire whose validity and reliability were tested. Once the data meets the requirements of the multiple linear regression test, the multiple linear regression (MLR) method was carried out to test the direct influence of LD, motivation (MT) and job satisfaction (JS) on IP. Structural equation modeling (SEM) with a two-step approach was used to test the mediation model. Increased IP is not immediately caused by LD. However, leaders can increase MT and JS directly. MT consistently improves IP, but JS has no effect on IP. The results of this study strengthen the results of previous research that the influence of LD on IP is mediated by MT and JS. A discussion of the results of this research is presented after the presentation of the analysis of the research results.

Keywords: Individual performance, Job satisfaction, Leadership, Motivation.

Contribution of this paper to the literature
The results of this research further confirm that leadership cannot directly improve the performance of its employees. There are several variables that can mediate this influence. This research examines employee motivation and job satisfaction as mediating variables.

1. Introduction

Individual performance is the most important variable to achieve because it contributes to team and organizational performance (Carpini, Parker, & Griffin, 2017).

Achieving this performance cannot be separated from the role of the leader, because leaders are able to motivate their employees (Ng, 2017). Several researchers found that leaders have a direct influence on IP (e.g., (Breevaart, Bakker, Demerouti, & Derks, 2016; Buil, Martínez, & Matute, 2019; Dastane, 2020; López-Cabarcos, Vázquez-Rodríguez, & QuinoA-Pineiro, 2022) ). However, several other researchers found that the influence of LD on IP is not direct, but is mediated by several other variables (e.g., (Phillips, Thai, & Halim, 2019; Salas-Vallina, Simone, & Fernández-Guerrero, 2020) ).

Several researchers agree that IP’s output is employee’s JS, satisfaction with leaders, performance, and organizational citizenship behavior (OCB) (e.g., (Abdelwahed, Soomro, & Shah, 2023; Bakker, Hetland, Olsen, & Espevik, 2023; Banks, McCauley, Gardner, & Guler, 2016; Laschinger, Wong, & Grau, 2013) ). Researchers also agree that JS is an important factor in achieving IP (Dorta-Afonso, González-de-la-Rosa, Garcia-Rodriguez, & Romero-Domínguez, 2021; Kong, Jiang, Chan, & Zhou, 2018; Nemteanu & Dabija, 2021) . Employees who are satisfied have high IP, while those who are dissatisfied have less good IP (Aung, San Santoso, & Dodanwala, 2023). Based on social exchange theory (SET), leaders who provide help and support to employees will be rewarded with their behavior being in line with the leader's preferences (Gao, Murphy, & Anderson, 2020). Appropriate leadership can motivate and satisfy employees so that their IP is better (Hoch, Bommer, Dulebohn, & Wu, 2018).

Apart from JS, MT is a variable that is believed to have a very strong influence on IP, because MT can move employees to achieve goals. In general, employees who are not motivated will feel less satisfied with their work (Azar & Shafighi, 2013; Pang & Lu, 2018) which will affect their IP (Dodanwala & Santoso, 2022; Yukongdi & Shrestha, 2020).

MT is a variable that influences employee’s JS (Alshmemri, Shahwan-Akl, & Maude, 2017; Ayalew et al., 2021; Samson-Akpan, Ojong, & Ndiok, 2016) . Leaders play a role in increasing employee motivation (Asrar-Ul-Haq & Kuchinke, 2016) to achieve targets or performance.

This study analyzes how leaders influence IP through JS and employee’s MT. This study was conducted on SMEs in Yogyakarta. SME leadership is rarely researched (Franco & Matos, 2015) because in general the leader is the owner of the company. Apart from that, SME leaders also work like other employees. SMEs also rarely assess leader effectiveness (Nazarian, Soares, & Lottermoser, 2017).

 Because the role of leaders in improving performance is very large (Burawat, 2019) research on leadership still needs to be carried out. The results of this study confirm the results of previous research that the influence of LD on IP is not direct, but is mediated by other variables, namely JS and MT.

2. Theoretical Review and Hypotheses Formulation

In a fiercely competitive environment, performance is an important variable for organizational development and sustainability. IP is how much employees can achieve the goals or targets set (Blickle & Schütte, 2017). In general, performance is related to employee behavior in the workplace (Abdelwahed et al., 2023). IP is a form of individual behavior. Optimal IP can be achieved if there is a leader who manages them to become reliable employees (Eliyana & Ma’arif, 2019). IP is also an individual's ability to carry out activities so that they can contribute to organizational performance (Campbell & Wiernik, 2015).

Empirical research has proven the influence of leadership on JS and IP (e.g., (Aboramadan & Dahleez, 2020; Lai, Tang, Lu, Lee, & Lin, 2020; Para-González, Jiménez-Jiménez, & Martínez-Lorente, 2018; Tian et al., 2020) ). This is because leaders influence attitudes, namely JS (Gao et al., 2020) and behavior, namely performance (Torlak & Kuzey, 2019) of employees.

The relations between employees and their leaders is the key to company success (Tseng & Levy, 2019). Leaders generally help achieve performance by making it easier to achieve targets, encouraging employee enthusiasm for work, and increasing employee self-confidence (Salas-Vallina et al., 2020). Leaders also direct employees about how to work and shape work culture.

According to Abdelwahed et al. (2023) the influence of LD on IP can be direct or mediated by other variables. Some researchers found a direct influence of LD on IP (e.g., (Hongdao, Bibi, Khan, Ardito, & Nurunnabi, 2019; López-Cabarcos et al., 2022; Swanson, Kim, Lee, Yang, & Lee, 2020; Zollo, Rialti, Tron, & Ciappei, 2021) ) while several other researchers found that there were mediating variables between the two. Work engagement was found to be a variable that mediates the relationship between LD and IP (e.g., (Bakker et al., 2023; Tummers & Bakker, 2021)). JS also mediates the relationship between the two (e.g., (Adiguzel, Ozcinar, & Karadal, 2020; Braun, Peus, Weisweiler, & Frey, 2013; Salas-Vallina et al., 2020) ).

Other researchers have found MT to mediate the influence of LD on IP (e.g., (Advani, 2015; Alilyyani, Wong, & Cummings, 2018; Bryan & Vitello-Cicciu, 2022; Stollberger, Las Heras, Rofcanin, & Bosch, 2019) ).

Furthermore, JS is a work attitude or positive perception of work by comparing targets and results achieved (Szabó et al., 2023). In general, JS is related to and can improve IP (Eliyana & Ma’arif, 2019). JS is also a positive feeling and expression towards work and enjoying the job (Ayalew et al., 2021) so that employees feel compatible with the job (Gul, Usman, Liu, Rehman, & Jebran, 2018). Employees who feel satisfied with the organization and their work will perform well, but if they are not satisfied they will experience stress and have the potential to resign from the organization (Chen & Wang, 2019; Huertas-Valdivia, Gallego-Burín, Castillo, & Ruiz, 2021) .

Meanwhile, MT is an encouragement to generate and direct behavior to achieve desired goals (Ayalew et al., 2021). MT, which is the energy to try, is often associated with JS (Szabó et al., 2023). MT is an internal and external force related to work, while JS is an individual's attitude towards work (Lohela-Karlsson, Jensen, & Björklund, 2022). MT is an important component in improving IP (Kuvaas, Buch, & Dysvik, 2017). Employees’ MT will feel satisfaction at work (Pang & Lu, 2018) and conversely, employees who are dissatisfied with their work are also not motivated at work (Huertas-Valdivia et al., 2021).

Based on SET, satisfied and motivated employees will provide the best for the organization (Dorta-Afonso, Romero-Domínguez, & Benítez-Núñez, 2023). Based on SET, leaders and followers develop a relationship where if one party gets something, the other party responds (Gooty & Yammarino, 2016).

According to SET, a relationship of mutual trust between leaders and followers can produce positive attitudes and behavior in the workplace (Khan, Ismail, Hussain, & Alghazali, 2020). Leaders play an important role in generating motivation (Deci, Olafsen, & Ryan, 2017) and increasing employee JS (Mayfield, Mayfield, & Neck, 2021). Several researchers found that motivation also acts as a mediating variable in the influence of leaders on IP (e.g., (Khan et al., 2020; Megheirkouni, Amaugo, & Jallo, 2018; Rehman, Bhatti, & Chaudhry, 2019) ). Proposed hypothesis are: 

H1: Leadership positively influences performance.
H2: Motivation positively influences performance.
H3: Job satisfaction positively influences performance.

3. Methods

3.1. Samples and Procedures

This research was conducted at SMEs in Yogyakarta, Indonesia with employees as the research sample. This research uses Google Form to collect data from respondents. The questionnaire was distributed over 4 months (January to April 2023) and 416 data were collected. The collected data is used to carry out validity and reliability tests, classical assumption tests, and mediation test of relationship models. Validity test uses factor analysis with factor loading of more than 0.50 and Kaiser Meyer Olkin  Measure of Sampling (KMO) above 0.60 (Tabachnick & Fidell, 1996).

Validity test using Cronbach Alpha using the criteria of Zikmund, Babin, Carr, and Griffin (2010) namely 0.80 < α < 0.95 is very good reliability, 0.70 < α < 0.80 is good reliability, 0.60 < α < 0.70 is fair reliability, and α below 0.60 is poor reliability. Multiple linear regression testing requires passing normality, heteroscedasticity and multicollinearity tests. Model mediation testing was carried out using SEM with a two-step approach (Byrne, 2010).

3.2. Measurements

This research used a quantitative approach with survey techniques using questionnaires. The questionnaire was taken from previous research, namely Romawati, Supriadi, and Setyadi (2022). This research uses 6 valid and reliable IP items (factor loading 0.654 to 0.830, KMO 0.674, and α = 0.867, for example, I am able to complete work according to target), 4 valid and reliable LD items (factor loading 0.806 to 0.913, KMO 0.827, and α = 0.891, for example, my leader always provides solutions), 5 valid and reliable MT items (loading factor 0.743 to 0.851, KMO 0.786, and α = 0.881, for example, I have responsibility), and 6 JS items which is valid and reliable (loading factor 0.755 to 0.824, KMO 0.794, and α = 0.865, for example, I am satisfied with the opportunities given).

4. Results

4.1. Preliminary Analysis

Before testing the influence of the independent variable on the dependent variable and testing the mediation model of the relationship, a correlation test and classical assumption test were carried out on the data used in this research. The results of the correlation test between the variables studied are presented in Table 1.

Table 1. Correlations, mean, composite reliability.
Variables
LD
MT
JS
PF
Leadership (LD)
1.000
Motivation (MT)
0.242**
1.000
Job satisfaction (JS)
0.708**
0.582**
1.000
Performance (PF)
0.292**
0.764**
0.648**
1.000
Mean
3.7692
3.9192
3.5849
3.8205
Standard deviation
0.6999
0.5728
0.5972
0.5742
Composite reliability
0.9460
0.8930
0.9540
0.9370

Note:

** significant at p ≤ 0.01 level.

Table 1 shows that the correlation between variables is significantly positive. IP is quite strongly correlated with motivation and JS. JS is also quite strongly positively correlated with LD and MT. Meanwhile, although weak, the correlation between LD and MT and between LD and IP is also significantly positive. The average of LD, MT and IP is high, while the average of JS is moderate.

Next, a classical assumption test was carried out which included normality, heteroscedasticity and multicollinearity tests. The multicollinearity test results found that the three independent variables did not have multicollinearity because the variance inflation factor (VIF) value was below 10 (VIF leadership = 0.219, VIF motivation = 1.657, VIF job satisfaction = 3.127). The results of the data normality test found that the data was normally distributed. Meanwhile, the results of the heteroscedasticity test show that the data is quite widely spread and does not form a particular pattern. The results of normality and heteroscedasticity tests are presented in Figure 1.

Figure 1. Normality and heteroscedasticity test results of all variables.

4.2. Multiple Linear Regression Analysis

Multiple linear regression is used to test the direct influence of independent variables on the dependent variable. The test results are as follows: Next, a classical assumption test was carried out which included normality, heteroscedasticity and multicollinearity tests. The multicollinearity test results found that the three independent variables did not have multicollinearity because the variance inflation factor (VIF) value was below 10 (VIF leadership = 0.219, VIF motivation = 1.657, VIF job satisfaction = 3.127). The results of the data normality test found that the data was normally distributed. Meanwhile, the results of the heteroscedasticity test show that the data is quite widely spread and does not form a particular pattern. The results of normality and heteroscedasticity tests are presented in Figure 1.

Table 2. Test results of the direct effect.
ANOVAb
Model
Sum of sq.
df
Mean sq.
F
Sig.
1
 Regr.
107.084
3
35.695
494.561
0.000a
Resid.
29.736
412
0.072
Total
136.821
415

Note:

a. Predictors: (Constant), JS, MT, LD.
b. Dependent variable: PF.

Table 2 shows the direct influence of the independent variable on the dependent variable. JD, MT, and JS simultaneously influence IP. Furthermore, Table 2 needs to be completed with a coefficient of determination to determine the percentage influence of the three independent variables on IP. The magnitude of the influence of all independent variables on the dependent variable is shown in the coefficient of determination value, namely the adjusted R-square value.

Table 3. Coefficient of determination.
Model summaryb
Model
R
R sq.
Adj. R sq.
Std. err. of est.
Change statistics
R sq.ch.
F ch.
df1
df2
Sig. F ch.
1
0.885a
0.783
0.781
0.26865
0.783
494.561
3
412
0.000

Note:

a. Predictors: (Constant), JS, MT, LD.
b. Dependent variable: PF.

Table 3 shows that the influence of LD, MT and JS on IP is quite large, namely 78.1%. This means that there are still other variables that influence performance but are not too big, namely 22.9% and were not examined in this research. Next, the influence of each independent variable on performance is presented in Table 4.

Coefficientsa
Model
Unstd. coeffs.
Std. coeffs.
t
Sig.
Coll. stats.
B
Std. error
Beta
Tolrnc.
VIF
1 (Constant)
0.274
0.104
  
2.621
0.009
  
 
LD
-0.074
0.028
-0.091
-2.664
0.008
0.455
2.197
MT
0.715
0.030
0.713
24.124
0.000
0.604
1.657
JS
0.286
0.039
0.297
7.323
0.000
0.320
3.127

Note:

a. Dependent variable: PF.

Table 4 shows that all independent variables (LD, MT, JS) have an effect on the dependent variable (IP). MT and JS have a positive effect on IP (H2 and H3 are supported), while the effect of LD on IP is significantly negative but the effect is very weak (H1 is not supported). Furthermore, because the influence of LD on IP is negative and very weak, it is necessary to test the mediation model using SEM with a two-step approach.

4.3. Mediation Model Test Results

The results of multiple linear regression testing found that the effect of LD on IP was negative and weak. Therefore, it is necessary to test the mediation model. Based on previous research results, the influence of LD on IP is not direct, but is mediated by MT (e.g., (Advani, 2015; Basford, Offermann, & Wirtz, 2012; Stollberger et al., 2019) ) and JS (e.g., (Adiguzel et al., 2020; Braun et al., 2013; Eliyana & Ma’arif, 2019) ). The results of testing the mediation model are presented in Table 5.

Table 5. Mediation model test results.
Influence of independent Variable on dependent variable
β
Critical ratio
Leadership Motivation
0.257*
4.614
Leadership Job satisfaction
0.685*
20.953
Job satisfaction Performance
0.049
1.203
Motivation Performance
0.918*
22.590
Motivation Job satisfaction
0.463*
14.423

C min = 13.152     df = 1       p = 1   Cmin/df =13.152
GFI   =  0.984      AGFI = 0.939     CFI = 0.989
NFI   =  0.989      IFI = 0.990         TLI =0.937           

Note:

* significant at α ≤ 0.05 level.
GFI: Goodness of fit index.
AGFI: Adjusted goodness of fit index.
CFI: Comparative fit index.
NFI: Normed fit index.
IFI: Incremental fit index.
TLI: Tucker Lewis index.

Table 5 shows that the influence of LD on IP is fully mediated by MT, while the influence is partially mediated by JS. This relationship model has been modified according to theory and data. This is indicated by a TLI value of more than 0.90, a CFI and AGFI value of more than 0.95. In addition, the model also found that MT influences JS.

5. Discussion

This research reexamines the influence of LD on IP and strengthens the results of previous research that LD does not have a direct effect on increasing IP. LD, MT, JS and IP are interrelated variables, so the correlation between variables is high and significantly positive. However, the correlation between LD and MT and the correlation between LD and IP are low. The average MT, JS and IP of SMEs employees are relatively high. Employees also rate their leaders as good and quite transformational.

This research supports the results of previous research that LD is related to IP (e.g., (Buil et al., 2019; Hongdao et al., 2019; López-Cabarcos et al., 2022) ). The role of leaders in motivating employees is also shown by the leader's influence on MT being significantly positive. This supports the results of previous research (e.g., (Bakker et al., 2023; Bilginoglu & Yozgat, 2018; Hoch et al., 2018; Ng, 2017) ). Leaders are able to encourage and align the behavior of their followers to achieve organizational goals. This research also strengthens the results of previous research that LD is related to and can increase employee’s JS (e.g., (Adiguzel et al., 2020; Banks et al., 2016; Qalati, Zafar, Fan, Limón, & Khaskheli, 2022) ). LD can indeed improve employee attitudes and behavior in an organization.

Furthermore, this research found a fairly strong relationship between MT and JS. This is consistent with the research results of Szabó et al. (2023). This research also found the influence of MT on JS which is consistent with the results of previous research (e.g., (Alshmemri et al., 2017; Aung et al., 2023; Pang & Lu, 2018; Samson-Akpan et al., 2016) ). MT is a force that is able to move individuals to work and achieve targets, while JS is an employee's attitude towards what they do (Lohela-Karlsson et al., 2022).

However, this research does not support the results of previous research which found that JS can improve IP (e.g., (Dorta-Afonso et al., 2021; Kloutsiniotis & Mihail, 2020; Kong et al., 2018) ). The results of this study found that although JS is quite strongly and significantly positively correlated, JS has no effect on IP.

LD studies from the follower's perspective have been widely conducted in Asia (Asrar-Ul-Haq & Kuchinke, 2016; Riaz et al., 2017). However, the research results are still inconsistent and give rise to debate. LD’s outcomes are attitudes, behavior, and effectiveness. MT and JS are employee work attitudes, while IP is individual effectiveness and behavior in the workplace. There are still many LD’s outcomes that require research because the results are not consistent.

6. Conclusion

Leaders play a very important role in generating MT and increasing employee’s JS, which ultimately can improve IP. Employees are satisfied when they are motivated. Human Resource Management (HRM) practices need to be prioritized in organizations in order to motivate and improve IP. HRM is the most valuable resource and cannot be replaced by any other resource.

This research contributes to leadership development in SMEs so that its effectiveness is always evaluated because it impacts MT, JS and IP. Some of the weaknesses of this research are the use of self-assessment which can increase the beta value and the use of cross-section data to test the mediation model. Future research is expected to use other-ratings in assessing IP and longitudinal data to test the mediation model. In addition, more research samples can represent the population more optimally.

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