Analysis of the influence of perceived self-efficacy on organizational performance among pharmaceutical managers: a structural equation study

Demographic characteristics of respondents (N = 241)

Characteristics	Number (N)	Percentage (%)
Gender
Men	171	71.0
Women	70	29.0
Age
Less than 30 years	10	4.1
31–40 years	140	58.1
41–50 years	77	32.0
Greater than 50 years	14	5.8
Educational qualification
Pharmacist	102	42.4
Non-pharmacist	139	57.6
Type of firm
Privately owned	147	61.0
Multinational	94	39.0
Managerial role
Strategic	64	25.6
Product	39	16.2
Operational sales	138	57.3
Territory of coverage
North	38	15.8
East	11	4.6
West	94	39.0
South	19	7.9
National	79	32,8
Years of experience in pharmaceutical management (Yrs.)
Less than 5 years	100	41.5
5–10 years	79	32.8
10–15 years	45	18.7
Greater than 15 years	17	7.1
Years of experience in the pharmaceutical Industry (Yrs.)
Less than 5 years	18	7.5
6–10	104	43.2
11–15	76	31.5
15–20	31	12.9
Greater than 20	12	50.0
Span of control
Less than 5	50	20.7
5–10	102	42.3
11–15	20	8.3
15–20	22	9.1
Greater than 20	47	19.5
Annual salary (USD$)
2439–7317	82	34.0
7317–12 195	94	39.0
12 195–17 073	28	11.6
17 073–21 951	18	7.5
Greater than 21 951	19	7.9
Total	241	100

Characteristics	Number (N)	Percentage (%)
Gender
Men	171	71.0
Women	70	29.0
Age
Less than 30 years	10	4.1
31–40 years	140	58.1
41–50 years	77	32.0
Greater than 50 years	14	5.8
Educational qualification
Pharmacist	102	42.4
Non-pharmacist	139	57.6
Type of firm
Privately owned	147	61.0
Multinational	94	39.0
Managerial role
Strategic	64	25.6
Product	39	16.2
Operational sales	138	57.3
Territory of coverage
North	38	15.8
East	11	4.6
West	94	39.0
South	19	7.9
National	79	32,8
Years of experience in pharmaceutical management (Yrs.)
Less than 5 years	100	41.5
5–10 years	79	32.8
10–15 years	45	18.7
Greater than 15 years	17	7.1
Years of experience in the pharmaceutical Industry (Yrs.)
Less than 5 years	18	7.5
6–10	104	43.2
11–15	76	31.5
15–20	31	12.9
Greater than 20	12	50.0
Span of control
Less than 5	50	20.7
5–10	102	42.3
11–15	20	8.3
15–20	22	9.1
Greater than 20	47	19.5
Annual salary (USD$)
2439–7317	82	34.0
7317–12 195	94	39.0
12 195–17 073	28	11.6
17 073–21 951	18	7.5
Greater than 21 951	19	7.9
Total	241	100

Note: 1 USD$ is equivalent to 410 Nigerian naira.

Table 1

Demographic characteristics of respondents (N = 241)

Characteristics	Number (N)	Percentage (%)
Gender
Men	171	71.0
Women	70	29.0
Age
Less than 30 years	10	4.1
31–40 years	140	58.1
41–50 years	77	32.0
Greater than 50 years	14	5.8
Educational qualification
Pharmacist	102	42.4
Non-pharmacist	139	57.6
Type of firm
Privately owned	147	61.0
Multinational	94	39.0
Managerial role
Strategic	64	25.6
Product	39	16.2
Operational sales	138	57.3
Territory of coverage
North	38	15.8
East	11	4.6
West	94	39.0
South	19	7.9
National	79	32,8
Years of experience in pharmaceutical management (Yrs.)
Less than 5 years	100	41.5
5–10 years	79	32.8
10–15 years	45	18.7
Greater than 15 years	17	7.1
Years of experience in the pharmaceutical Industry (Yrs.)
Less than 5 years	18	7.5
6–10	104	43.2
11–15	76	31.5
15–20	31	12.9
Greater than 20	12	50.0
Span of control
Less than 5	50	20.7
5–10	102	42.3
11–15	20	8.3
15–20	22	9.1
Greater than 20	47	19.5
Annual salary (USD$)
2439–7317	82	34.0
7317–12 195	94	39.0
12 195–17 073	28	11.6
17 073–21 951	18	7.5
Greater than 21 951	19	7.9
Total	241	100

Characteristics	Number (N)	Percentage (%)
Gender
Men	171	71.0
Women	70	29.0
Age
Less than 30 years	10	4.1
31–40 years	140	58.1
41–50 years	77	32.0
Greater than 50 years	14	5.8
Educational qualification
Pharmacist	102	42.4
Non-pharmacist	139	57.6
Type of firm
Privately owned	147	61.0
Multinational	94	39.0
Managerial role
Strategic	64	25.6
Product	39	16.2
Operational sales	138	57.3
Territory of coverage
North	38	15.8
East	11	4.6
West	94	39.0
South	19	7.9
National	79	32,8
Years of experience in pharmaceutical management (Yrs.)
Less than 5 years	100	41.5
5–10 years	79	32.8
10–15 years	45	18.7
Greater than 15 years	17	7.1
Years of experience in the pharmaceutical Industry (Yrs.)
Less than 5 years	18	7.5
6–10	104	43.2
11–15	76	31.5
15–20	31	12.9
Greater than 20	12	50.0
Span of control
Less than 5	50	20.7
5–10	102	42.3
11–15	20	8.3
15–20	22	9.1
Greater than 20	47	19.5
Annual salary (USD$)
2439–7317	82	34.0
7317–12 195	94	39.0
12 195–17 073	28	11.6
17 073–21 951	18	7.5
Greater than 21 951	19	7.9
Total	241	100

Note: 1 USD$ is equivalent to 410 Nigerian naira.

Table 2 shows the Cronbach α coefficients of the main study constructs. The results showed that PSE had a coefficient of 0.619 and POP had a value of 0.701, respectively. They fall within the acceptable range of 0.6–0.7 to confirm the internal reliability of the data.^[23]

Table 2

Measures of mean, SD and internal reliability of main constructs

Main constructs	Number of items	Mean	Standard deviation	Threshold	Results	Inference
Self-efficacy	7	4.440	0.881	≥0.6–0.7	0.619	Supported
Organizational performance	5	4.528	0.827	≥0.6–0.8	0.701	Supported

Main constructs	Number of items	Mean	Standard deviation	Threshold	Results	Inference
Self-efficacy	7	4.440	0.881	≥0.6–0.7	0.619	Supported
Organizational performance	5	4.528	0.827	≥0.6–0.8	0.701	Supported

Note: SD = standard deviation.

Table 2

Measures of mean, SD and internal reliability of main constructs

Main constructs	Number of items	Mean	Standard deviation	Threshold	Results	Inference
Self-efficacy	7	4.440	0.881	≥0.6–0.7	0.619	Supported
Organizational performance	5	4.528	0.827	≥0.6–0.8	0.701	Supported

Main constructs	Number of items	Mean	Standard deviation	Threshold	Results	Inference
Self-efficacy	7	4.440	0.881	≥0.6–0.7	0.619	Supported
Organizational performance	5	4.528	0.827	≥0.6–0.8	0.701	Supported

Note: SD = standard deviation.

Table 3 shows the output of measures of fit of the three models evaluated: PSE construct labelled A, POP construct labelled B and the final combined structural model labelled C (equals A + B combined). Results revealed that models A, B and C fulfilled the measures of fit indices and hence were acceptable for use for further statistical analysis.

Table 3

Model fit measures for measurement and structural models

Measures		Threshold	Computed values			Inference
	Description		SE model (A)	POP model (B)	Structural model C (A+B)
CMIN	Model chi-square fit	P > 0.05	14.04	11.151	59.779	Accepted
DF	Degrees of freedom		11.00	5.000	50.000	Accepted
GFI	Goodness-of-fit	>0.95	0.984	0.982	n/r	Accepted
AGFI	Adjusted Goodness-of-fit	>0.90	0.96	0.947	n/r	Accepted
TLI	Tucker Lewis Index	>0.95	0.968	0.926	0.966	Accepted
CFI	Comparative Fit Index	>0.95	0.983	0.963	0.974	Accepted
RMSEA	Root-mean-square-error-of-approximation	<0.06	0.034	0.072	0.029	Accepted
SRMR	Standardized root mean square residual	<0.08	0.034	0.057	0.047	Accepted
PCLOSE	Test of close fit	>0.05	0.663	0.218	0.923	Accepted
CMIN/DF	Chi-square/df	1 and 3	1.276	2.23	1.196	Accepted

Measures		Threshold	Computed values			Inference
	Description		SE model (A)	POP model (B)	Structural model C (A+B)
CMIN	Model chi-square fit	P > 0.05	14.04	11.151	59.779	Accepted
DF	Degrees of freedom		11.00	5.000	50.000	Accepted
GFI	Goodness-of-fit	>0.95	0.984	0.982	n/r	Accepted
AGFI	Adjusted Goodness-of-fit	>0.90	0.96	0.947	n/r	Accepted
TLI	Tucker Lewis Index	>0.95	0.968	0.926	0.966	Accepted
CFI	Comparative Fit Index	>0.95	0.983	0.963	0.974	Accepted
RMSEA	Root-mean-square-error-of-approximation	<0.06	0.034	0.072	0.029	Accepted
SRMR	Standardized root mean square residual	<0.08	0.034	0.057	0.047	Accepted
PCLOSE	Test of close fit	>0.05	0.663	0.218	0.923	Accepted
CMIN/DF	Chi-square/df	1 and 3	1.276	2.23	1.196	Accepted

Note: PSE = self-efficacy, POP = perceived organizational performance.

Table 3

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Model fit measures for measurement and structural models

Measures		Threshold	Computed values			Inference
	Description		SE model (A)	POP model (B)	Structural model C (A+B)
CMIN	Model chi-square fit	P > 0.05	14.04	11.151	59.779	Accepted
DF	Degrees of freedom		11.00	5.000	50.000	Accepted
GFI	Goodness-of-fit	>0.95	0.984	0.982	n/r	Accepted
AGFI	Adjusted Goodness-of-fit	>0.90	0.96	0.947	n/r	Accepted
TLI	Tucker Lewis Index	>0.95	0.968	0.926	0.966	Accepted
CFI	Comparative Fit Index	>0.95	0.983	0.963	0.974	Accepted
RMSEA	Root-mean-square-error-of-approximation	<0.06	0.034	0.072	0.029	Accepted
SRMR	Standardized root mean square residual	<0.08	0.034	0.057	0.047	Accepted
PCLOSE	Test of close fit	>0.05	0.663	0.218	0.923	Accepted
CMIN/DF	Chi-square/df	1 and 3	1.276	2.23	1.196	Accepted

Measures		Threshold	Computed values			Inference
	Description		SE model (A)	POP model (B)	Structural model C (A+B)
CMIN	Model chi-square fit	P > 0.05	14.04	11.151	59.779	Accepted
DF	Degrees of freedom		11.00	5.000	50.000	Accepted
GFI	Goodness-of-fit	>0.95	0.984	0.982	n/r	Accepted
AGFI	Adjusted Goodness-of-fit	>0.90	0.96	0.947	n/r	Accepted
TLI	Tucker Lewis Index	>0.95	0.968	0.926	0.966	Accepted
CFI	Comparative Fit Index	>0.95	0.983	0.963	0.974	Accepted
RMSEA	Root-mean-square-error-of-approximation	<0.06	0.034	0.072	0.029	Accepted
SRMR	Standardized root mean square residual	<0.08	0.034	0.057	0.047	Accepted
PCLOSE	Test of close fit	>0.05	0.663	0.218	0.923	Accepted
CMIN/DF	Chi-square/df	1 and 3	1.276	2.23	1.196	Accepted

Note: PSE = self-efficacy, POP = perceived organizational performance.

Exploratory and confirmatory factor analysis of study constructs

An exploratory factor analysis (EFA) was used to examine the underlying structure of 12 items of the two latent variables or constructs in the dataset. EFA utilized principal component analysis as the preferred method of extraction with varimax rotation and with minimum factor loading cut-off set at 0.50 and Eigenvalue set at 1.^{[24, 25]} Four (4) characteristics or factors were extracted from the analysis. The loading of the factors in the EFA ranged from 0.546 to 0.852.

The factor loadings item-wise are as follows; SE1 = effectiveness (0.852), SE2 = efficiency (0.839), SE3 = timeliness (0.732), SE4 = proactiveness (0.707), SE5 = creativity (0.685), SE6 = adaptability (0.765), SE7 = not limited by circumstances (0.770), OP1 = improved sales output (0.623), OP2 = influenced policy (0.602), OP3 = improved subordinates’ performance (0.643), OP4 = improved organizations’ output (0.623) and OP5 = overall managerial impact (0.546). Other adequacy measures obtained were Kaiser Meyer Olkin = 0.693 (cutoff= >0.5); goodness of fit = 0.001 (cutoff < 0.05); total variance explained = 55.319 (minimum cutoff ≥50%); and Bartlett’s test of sphericity χ² = 433.610 [df = 66, P < 0.001]. The pattern matrix obtained from the analysis which produced a 4-factor solution is displayed in Figure 1. Furthermore, the main construct PSE produced three sub-factors known as first-order constructs. They are self-efficiency (PSE1, PSE2), self-creativity (PSE3, PSE4, PSE5) and self-adaptability (PSE6, PSE7). Hence, the main construct is referred to as a second-order construct. POP construct produced only 1 factor consisting of POP1, POP2, POP3, POP4 and POP5. Thereafter, the confirmatory factor analysis (CFA) was used to confirm the factor matrix structure obtained from EFA. CFA was conducted on the combined structural model (A + B) and provided satisfactory model fit measures (Table 3), hence validating the structural model. (Figure 1).

Figure 1

Structural model showing the relationship between PSE and POP (standardized regression coefficients). Key: SE1 = effectiveness, SE2 = efficiency, SE3 = timeliness, SE4 = proactiveness, SE5 = creativity, SE6 = adaptability, SE7 = not limited by circumstances, OP1 = improved sales output, OP2 = influenced policy, OP3 = improved subordinates’ performance, OP4 = improved organizations’ output, OP5 = overall managerial impact.

Path analysis showing the influence of PSE on POP and first-order constructs

The results of this study showed that the mean value of perceived self-efficacy was 4.440 (SD = 0.881) and organizational performance was 4.528 (SD = 0.827). A significant positive relationship existed between PSE and POP among pharmaceutical managers (R² = 0.65, β = 0.651, P < 0.001). Similarly, significant relationships also existed between perceived self-efficacy and the first-order constructs (self-efficiency, self-creativity and self-adaptability) as shown in Table 4.

Table 4

Path analysis of structural model showing causal relationships (N = 241)

Dependent variables	Path	Independent variables	β	t-Value	P-value	Inference
Self-efficiency	<---	PSE	0.574	Reference value		Significant
Self-creativity	<---	PSE	0.649	2.936	0.003	Significant
Self-adaptability	<---	PSE	0.463	2.237	0.025	Significant
POP	<---	PSE	0.651	2.988	0.003	Significant

Dependent variables	Path	Independent variables	β	t-Value	P-value	Inference
Self-efficiency	<---	PSE	0.574	Reference value		Significant
Self-creativity	<---	PSE	0.649	2.936	0.003	Significant
Self-adaptability	<---	PSE	0.463	2.237	0.025	Significant
POP	<---	PSE	0.651	2.988	0.003	Significant

Note: β = β path coefficient, P < 0.05 significance at t-calculated > t-critical.

Table 4

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Path analysis of structural model showing causal relationships (N = 241)

Dependent variables	Path	Independent variables	β	t-Value	P-value	Inference
Self-efficiency	<---	PSE	0.574	Reference value		Significant
Self-creativity	<---	PSE	0.649	2.936	0.003	Significant
Self-adaptability	<---	PSE	0.463	2.237	0.025	Significant
POP	<---	PSE	0.651	2.988	0.003	Significant

Dependent variables	Path	Independent variables	β	t-Value	P-value	Inference
Self-efficiency	<---	PSE	0.574	Reference value		Significant
Self-creativity	<---	PSE	0.649	2.936	0.003	Significant
Self-adaptability	<---	PSE	0.463	2.237	0.025	Significant
POP	<---	PSE	0.651	2.988	0.003	Significant

Note: β = β path coefficient, P < 0.05 significance at t-calculated > t-critical.

Discussion

The objective of the study was to investigate the influence of perceived self-efficacy on the perceived organizational performance of pharmaceutical managers as well as examine the differences in the relationship based on the type of firm and gender. Study hypothesis-testing was based on the significance of the influence of self-efficacy on the organizational performance of a manager. The study showed that there was a robust positive causal relationship obtained between perceived self-efficacy (PSE) and perceived organizational performance (POP) [R² = 0.65, P < 0.001] as well as its first-order constructs such as self-efficiency (PSE1, PSE2), self-creativity (PSE3, PSE4 and PSE5), and self-adaptability (PSE6, PSE7) (P < 0.05). Therefore, the proposed hypothesis H1 (β = 0.65, P < 0.001) was supported, which implies that self-efficacy significantly predicts or influences organizational performance among pharmaceutical managers. This study outcome aligns with several studies showing that self-efficacy positively influences the performance of employees.^[6–8] In other words, managers with high levels of self-efficacy invariably have or achieve higher levels of performance in their organizations. This assertion of the study is contrary to the outcome of a meta-analytic study which showed that there is no relationship between both variables.^[11] Furthermore, other studies also suggested a negative relationship.^[3,12] Therefore, human resource managers have a role to play in prioritizing cognitive training for managers. Training should focus on identified areas of weakness and/or strength and be conducted to improve competencies critical to the success of the organization. This would impact positively the short and long-term productivity of managers in their designated roles. This assertion is supported by studies that reported higher cognitive and self-efficacy levels in employees exposed to training based on capacity building, enhanced collaboration and performance.^{[26, 27]} Furthermore, to investigate performance scores relative to the level of self-efficacy by respondents in respective managerial roles (operational, product and strategic managers), a simple plot diagram was developed using the key indicators. Thus, the simple plot (Figure 2) revealed that there is a marginal impact on mean performance scores based on the effect of low, moderate and high self-efficacy exhibited by strategic, product and operational sales managers. It showed that no respondent adopted the low self-efficacy position. At moderate levels of self-efficacy, product managers had higher performance scores, compared to strategic managers and the least were operational sales managers. Although, the interaction effect of managerial role and self-efficacy level was not statistically significant [F (5, 235) = 1.975, P = 0.141)]. This analysis however suggests that there is a need to improve the focus on capacity development of operational sales managers because of their importance in ensuring that organizational strategies are implemented and desired performance achieved.

Figure 2

Simple plot of the interaction between self-efficacy level and managerial role on performance.

Furthermore, in line with the suggestion of Manasseh (2015), it is relevant to examine if gender and type of firm exerted any confounding impact on the relationship between perceived self-efficacy and perceived organizational performance.^[11] To achieve this, the multigroup analytical approach to the structural model was adopted using the AMOS program. The multigroup analysis examined the hypothesis of whether the positive effect of perceived self-efficacy on perceived organizational performance was stronger in men compared to women as well as higher in managers in privately owned firms compared to multinational firms (Table 5). The non-significant effect between the groups on the hypothesized relationship suggests that there is minimal or non-existent dominance due to the numerical superiority of men (n = 171) versus women (n = 70) in addition to managers in privately owned firms (n = 147) compared to multinational firms (n = 94). Hence, hypothesis H2 was not supported. This finding is supported by a study that compared user perception between students and non-students utilizing a paratransit service^[28] and a study that compared the effect of entrepreneurial self-efficacy on performance based on gender differences.^[29] However, this study outcome is at variance with some studies that identified higher levels of self-efficacy in men compared to women in an academic setting.^{[30, 31]} However, another study showed mixed reviews on performance in men and women students with regards to academic performance in different course subjects but the overall gender differences were found to be insignificant.^[32] The outcomes of the study strengthen the need for adequate consideration and focus on the specific self-efficacy needs of pharmaceutical managers as it affects performance in their different functional roles.^[2] In other words, self-efficacy had a varied impact on mean performance across the managerial roles (operational, product and strategic) occupied by study participants as shown in Figure 2. The most affected category was the operational sales managers with the least level of self-efficacy and performance score. This finding justifies the need for more attention to be given to capacity development in skills, and competencies of managers in line with their work roles.

Table 5

Multigroup comparison of effect of self-efficacy on organizational performance

Path parameters	Gender		Type of firm
Variables/parameters	Men	Women	Privately owned	Multinational
DF	3		1
χ² value	3.187		2.070
P-value	0.364		0.150
ß-coefficient	0.670	0.580	0.670	0.520
Inference	No difference between groups		No difference between groups

Path parameters	Gender		Type of firm
Variables/parameters	Men	Women	Privately owned	Multinational
DF	3		1
χ² value	3.187		2.070
P-value	0.364		0.150
ß-coefficient	0.670	0.580	0.670	0.520
Inference	No difference between groups		No difference between groups

Note: DF = degrees of freedom, *significance at P < 0.05.

Table 5

Multigroup comparison of effect of self-efficacy on organizational performance

Path parameters	Gender		Type of firm
Variables/parameters	Men	Women	Privately owned	Multinational
DF	3		1
χ² value	3.187		2.070
P-value	0.364		0.150
ß-coefficient	0.670	0.580	0.670	0.520
Inference	No difference between groups		No difference between groups

Path parameters	Gender		Type of firm
Variables/parameters	Men	Women	Privately owned	Multinational
DF	3		1
χ² value	3.187		2.070
P-value	0.364		0.150
ß-coefficient	0.670	0.580	0.670	0.520
Inference	No difference between groups		No difference between groups

Note: DF = degrees of freedom, *significance at P < 0.05.

Implications of study to practice

This study contributed another dimension to extant research on perceived self-efficacy and performance among pharmaceutical managers. Managers should consciously identify their weaknesses and seek remedial improvement actions on an ongoing basis. Further improvement of the path model to adapt to other countries and contexts of pharmacy practice is suggested. Human resource managers in pharmaceutical marketing organizations should develop innovative strategies to identify the key self-efficacy needs of managers based on their specific functional roles. This approach would positively improve the overall performance of managers. Therefore, self-efficacy and organizational performance can be enhanced through a variety of measures. First, human resource managers should ensure a culture of continuous capacity improvement in work-based competencies, skillsets and collaborative mentorship is built within teams. Second, human resource managers should adopt focal and routine training to enhance the work-based competencies, professional development and problem-solving skills of pharmaceutical managers. Finally, intra- and inter-team collaboration among managers would foster a learning environment for challenged managers to enhance their performance capability.

Limitations of the study

The study adopted quantitative measures to investigate the relationship between perceived self-efficacy and perceived organizational performance, which used self-reported measures. The challenge associated with self-reported measures was that respondents may tend to score themselves highly on questions relating to performance and self-efficacy. Also, there is a need for qualitative research to further broaden the scope of understanding of the constructs as they relate to managers in the pharmaceutical industry in Nigeria. The study did not include validity measures of the model and thus should be done for future studies.

Conclusion

The focus of the study was to investigate the influence of perceived self-efficacy on performance among managers in the pharmaceutical industry in Nigeria. The measurement models of self-efficacy, organizational performance constructs and structural models had acceptable measures of fit. Three first-order constructs were derived from the self-efficacy construct namely self-adaptability, self-efficiency and self-creativity. Structural equation modelling revealed a significant positive effect of self-efficacy on organizational performance. Simple plot analysis revealed lower performance scores at a moderate level of self-efficacy with operational sales managers compared to strategic and product managers. Multigroup analysis revealed no dominant effect of gender and type of firm on the hypothesized relationship. The study suggests management training for managers to improve their level of self-efficacy. The study concluded that self-efficacy has a significant influence on the organizational performance of pharmaceutical managers.

Acknowledgment

Authors wish to appreciate the management of the Ministry of Health, Health Research, and statistics department for their approval of this research work.

Funding

None.

Conflict of Interest

The author declares that he has no conflicts of interest.

Authorship Statement

This is to certify that this original research work was done ethically and the manuscript is in compliance with the policies of this Journal.

Ethical Approval

Ethical approval was obtained from the department of research and statistics, Ogun state Ministry of Health. The approval number is HPRS/381/416 dated 23rd November, 2021. Informed consent was obtained from respondents before the administration of the questionnaire.

Data Availability

The data used for this study is available on request from the author.

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