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Article

Moderating Effect of Strategic Planning on the Relationship between Career Path Planning and Job Performance

by
Ali Ahmed Ateeq Ali
1,*,
Mahmoud AlZgool
1,
Mohammed Alzoraiki
1,
Marwan Milhem
1 and
Mujeeb Saif Mohsen Al-Absy
2,*
1
Administrative Science Department, College of Administrative and Financial Science, Gulf University, Sanad 26489, Bahrain
2
Accounting and Financial Science Department, College of Administrative and Financial Science, Gulf University, Sanad 26489, Bahrain
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(11), 8490; https://doi.org/10.3390/su15118490
Submission received: 2 March 2023 / Revised: 30 April 2023 / Accepted: 15 May 2023 / Published: 23 May 2023
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Versions Notes

Abstract

:
This study aimed to assess the moderating effect of strategic planning on the relationship between career path planning and job performance among employees in small and medium-sized enterprises (SMEs) in the Kingdom of Bahrain. A convenience sample consisting of 249 participants was selected. The study used SPSS and Smart-PLS to analyze the data. The findings of the study revealed that strategic planning and career path planning affected SME employees’ performance. In contrast, it was found that strategic planning substantially moderated the relationship between career path planning and job performance. Moreover, the results showed that SP and CPP had the greatest impact on JP. Based on the obtained findings, it is shown that the proposed hypotheses are accepted. The study revealed the impact of the moderator on the relationship between CPP and JP. The results of this study may serve as a guide for the owners and managers of enterprises and decision makers who aim to develop strategic planning and enhance employee performance.

1. Introduction

The crucial and essential role of small and medium-sized enterprises (SMEs) in the economy cannot be neglected at the present time. The significance of these enterprises lies in the fact that SMEs possess a powerful economic force in Bahrain, contributing 40% to the national economy, increasing the contribution to exports to 20%, and providing 43,000 jobs to the labor force. It is important to remember that small and medium-sized businesses make up 90% of all businesses, supply two-thirds of all jobs globally, and are the largest sector capable of creating new jobs. Accordingly, small and medium-sized enterprises (SMEs) are considered dynamic constants that drive economic growth, innovation, and job creation. Furthermore, SMEs play a crucial role in creating job opportunities, in addition to making a significant contribution to the gross domestic product (GDP) of developing countries. Therefore, it is not surprising that these facilities are among the most important economic sources, especially in developing countries. Although many countries, such as the Kingdom of Bahrain, have implemented a policy to facilitate this important industry, SMEs’ contribution to GDP still needs further improvement, as the contribution of this sector decreases in oil-exporting countries and increases in those with more diversified economies. Moreover, the average contribution of this sector in the official operation of Arab countries is about 49%, compared to the average in developed countries of about 60%. On the other hand, the majority of SMEs in the Kingdom of Bahrain face numerous obstacles and challenges due to the rapid rise of new products, processes, technologies, customer preferences, and the competitive environment today. More importantly, SMEs lack staff capabilities and competencies in the context of strategic planning, which is the focus of the current study.
Numerous studies [1,2,3,4] have shown that a variety of factors, including innovation, entrepreneur orientation, market orientation, training, organizational capabilities, human resource capital, strategic planning, career path planning, employee skills, and so forth, have an impact on how well small and medium-sized enterprises (SMEs) perform [5]. Additionally, other factors affect SMEs’ performance, including the lack of skilled manpower, financial support, innovation, and traditional human resource management methods. As for the strategic aspect, studies have concluded that only 10% of small and medium-sized enterprises use the tools of strategic planning techniques in their institutions [6,7].
Notwithstanding these proposed models and studies, the contribution of small and medium-sized enterprises (SMEs) to the Bahraini economy is low. Therefore, this study examined essential factors such as career path planning (CPP) and strategic planning (SP) as independent variables, using strategic planning (SP) as a moderator and the SMEs’ performance in Bahrain as a dependent variable. We investigated the extent to which SMEs follow these integrated models. In this respect, Ahmad and Ahmad [8] argue that firms can perform better and achieve growth when they use their resources per their goals and plans.
Every company places a high value on employee work performance in accomplishing the organization’s goals and employee responsibility awareness [9]. The significant influence that employee performance has on organizational success and survival has drawn researchers’ attention. The literature reveals that SMEs frequently perform poorly, and that employees have disagreements about how to complete duties or do not get along when working together. The top management handles this issue as the mediator and problem solver [10]. In small and medium-sized businesses, poor communication frequently results from misunderstanding, which in turn causes conflicts, subpar performance, and disgruntled clients. Therefore, this research investigated the role of strategic planning as a moderator of SMEs’ performance in Bahrain. We examined the effect of new variables in the model of this study to ascertain which factors influence employees’ performance in small and medium-sized enterprises (SMEs) in the Kingdom of Bahrain [11]. It is expected that top managers, as well as decision makers, can obtain some benefits from the findings of this study in that such findings could help them develop useful strategies for their organizations.
Indeed, several factors at work have an impact on how well employees accomplish their jobs. It is defined as the approach to performing job duties in line with the job description. The art of accomplishing a task within established parameters is called performance [12]. Despite the increasing interest of the Bahraini government in supporting SMEs, SMEs still face significant challenges, including the rapid rise of new products, processes, technologies, customer preferences, the competitive environment today, the lack of staff capabilities, and incompetencies in the context of strategic planning [13]. These challenges may limit their growth and continuity. Therefore, this study was conducted to investigate (i) the relationship between career path planning and job performance, (ii) the relationship between strategic planning and career path planning, (iii) the relationship between strategic planning and job performance, (iv) the indirect impact of strategic planning on job performance and career path planning, and (v) the moderation effect of strategic planning on the relationship between career path planning and job performance.
The present study is significant because it fills a research gap as it examines one of the more recent administrative issues in developing countries in general and the Kingdom of Bahrain in particular. As a result, this field of study is more significant, particularly as it considers the technological and economic changes that the Kingdom of Bahrain’s private sector has experienced. These changes call for studies that focus on the role of human resources and its dimensions, such as career path, strategic planning, and job performance, considering strategic planning a moderating variable.

2. Literature Review and Hypothesis Development

A high level of performance from employees will make it simpler for such individuals to be considered for raises to positions of more responsibility. Job performance is the outcome of a person’s effort or earnestness in carrying out work that has been assigned to him or her, with his or her talents, experience, and sincerity in line with the obligations that have been handed to him or her.

2.1. Career Path Planning and Job Performance

There are many definitions of career path planning or developing a career path. Whatever definitions or terminology are offered, they all revolve around one concept. The career path can be summarized as a “set of jobs or occupations planned by the individual to be practiced in life; it is a sequence of job changes that occur to the individual in his job life by horizontal or vertical transfer” [14]. Ng, Lai [15] define the career path as “an occupational position held by an individual for his or her entire lifetime”.
As explained by Al Balushi [16], the process of planning the career path means aligning the career aspirations of individuals with the opportunities provided by organizations, while the career path itself is the succession of certain jobs and accompanying these opportunities. These two processes are compatible, while planning the path is concerned with identifying the means to reach the desired goals. In the same context, career paths are the means to achieve goals. Further, career path planning is described by other researchers as a part of human resource management that deals with establishing agreements between the job and the employee to meet organizational productivity goals and personal job satisfaction goals [17].
Other researchers argue that “career path planning is the examined or careful processes through which the individual becomes aware of his skills, interests, motives, other characteristics, gain information about the choices and obligations, determine the goals correlated with career path, and set plans to achieve the specified goals” [14,18]. Moreover, small and medium-sized enterprises (SMEs) have adopted career path planning to enhance and improve services delivered to their customers to improve their quality of work. Small and medium-sized enterprises (SMEs) are also concerned with career path planning to improve employee performance.
However, studies are unambiguous regarding the role of career path planning in generating beneficial results. Numerous studies have revealed a positive relationship between career path planning and employee performance, but the results of these studies have produced conflicting results, and no clear consensus has been achieved [19]. However, unlike investigations in the public sector, where the emphasis has been on the small and medium-sized enterprises (SMEs), the focus of studies in the public sector has been on the influence of career path planning. In the present study, the following hypotheses are formulated:
Hypothesis 1 (H1).
There is a significant positive relationship between career path planning and job performance (CPP -> JP).
Hypothesis 2 (H2).
There is a significant positive relationship between strategic planning and career path planning (SP -> CPP).

2.2. Strategic Planning

Bryson [20] defined strategic planning as the process of selecting the objectives of the organization, defining the policies and strategies necessary to achieve the objectives, and determining the necessary methods to ensure the implementation of the established policies and strategies. It represents the long-term planning process that is formally prepared to achieve the objectives. Furthermore, George, Walker [21] stated that strategic planning (SP) is considered one of the most frequently utilized management strategies in modern organizations and is regarded as one of the top five global managerial approaches.
Recent research demonstrates the value of strategic planning as an organizational resource and emphasizes its critical role in addressing the organizational challenge. According to Steiss [22], strategic planning enables organizations to set objective and assists them in creating and implementing plans to achieve their goals. Similarly, Gardiner [23] found that strategic planning contains basic tenets, is specifically designed to support businesses, and ultimately translates into improved business performance. Consequently, it is a tool that encourages improved organizational effectiveness.
Wolf and Floyd [24] stressed that businesses with a high inclination toward strategic planning have stocks of knowledge that can be used to build huge data capability. Therefore, strategic planning is a valuable theoretical lens for understanding the impact of strategic planning on employee performance in small and medium-sized enterprises (SMEs).
Several previous studies have been conducted to investigate the effect of strategic planning on employee performance; of these, one conducted a meta-analysis of 31 empirical studies (with a total sample of 8618 organizations). The results revealed a strong positive relationship between strategic planning and employee performance. Furthermore, strategic planning can moderate the impact of organizational behavior on employee performance. This influence becomes especially significant when employee performance is measured [25].
Small and medium-sized enterprises (SMEs) have adopted career strategic planning to enhance and improve services delivered to their customers to improve the quality of their work. Such enterprises are also concerned with strategic planning to improve employee performance. However, studies are unambiguous regarding the role of strategic planning in generating beneficial results. Numerous studies have revealed a positive relationship between strategic planning and employee performance, but the results of these studies have produced conflicting results. No clear consensus has been achieved [26]. However, unlike investigations in the private sector, where emphasis has been on the small and medium-sized enterprises (SMEs), the focus of studies in the private sector has been on the influence of strategic planning on other variables.
One of the most significant aspects that affects performance is strategic planning. It is one of the contemporary managerial toolkits that can boost productivity and deal with ambiguous situations. It is stated that strategic planning is a critical mechanism in an organizational setting. It is a process used to determine and achieve an organization’s goals and objectives and it bridges the gaps between where we are and where we want to go.

2.3. The Relationship between Strategic Planning and Job Performance

A significant number of academics have taken an interest in the topic of job performance due to the importance it has for the community as a whole, as well as for individual people and businesses [27]. However, this has been thoroughly debated. This argument’s basic tenet is that emphasizing efficiency and effectiveness excessively is detrimental to more democratic outcomes [28]. However, recent conceptualizations of job performance in government or public firms have demonstrated that it is not just one thing. There are various performance dimensions (including democratic outcomes), evaluative performance on a variety of criteria (including citizens), and different sources and types of data which can be used to measure performance [16,21,29].
According to the goal-setting theory, organizations with goals perform much better because they make sure that resources are allocated to tackling key challenges and that employees are aware of the organization’s top priorities [30]. This claim has been endorsed by scholars studying both private and public sector organizations [21,31]. Typically, strategic planning has generated meaningful strategies, goals, and programs that strive to address strategic concerns. By making it apparent to the organization and its stakeholders what the organizational priorities are and how these will be fulfilled, it applies aspects of goal-setting theory to the formulation of strategy [31].
Numerous studies have indicated that organizations which pursue and implement strategic planning (SP) are more effective and productive than others as SP plays a significant and effective role in enhancing the performance of organizations and employees [32,33,34]. Moreover, the organization’s activities are controlled by strategic planning, which also improves departmental and employee coordination. Hristov and Chirico [35] claimed that the basis of sustainable development lies in maintaining the competitive advantage, which depends mainly on clear strategies from all aspects, which, in turn, lead to enhancing performance. It is argued that strategic planning, in its active role, can enhance the efficiency of management practices, and in turn, this enhancement will be reflected positively on the organizational performance [36]. Moreover, good strategic planning can help organizations link their long-term goals with their operational plans and short-term goals alike.
On the other hand, contradictory results in many studies were recorded regarding the relationship between strategic planning and employee performance. However, researchers believe that these contradictory results are only the result of serious research errors [16,37,38,39]. Alosani, Yusoff [32] pointed out that the relationship between strategic planning and employee performance is modestly positive due to the measurement errors in these studies. Based on what has been discussed above, the following hypotheses are suggested:
Hypothesis 3 (H3).
There is a significant positive correlation between strategic planning and job performance SP -> JP.
Hypothesis 4 (H4).
There is a significant positive indirect impact of strategic planning on job performance and career path planning.

2.4. Moderators in the Strategic Planning and Job Performance Relationship

The effect of the organizational factors might either be enhanced or reduced by the external environment. Many studies indicate that the uncertainty associated with the work environment and the lack of proper planning have a negative impact on business decisions, especially the inability to predict these factors. Therefore, they may deprive the company of effective use of available opportunities.
Researchers have suggested that organizations adopt more effective and strategic planning business practices to remain competitive in highly dynamic marketplaces [4,40]. According to Yoshikuni and Albertin [41], the degree of market competition determines how much an organization’s business practices affect performance. Hence, the strategic planning of the market determines the strategic action to pursue.
Many factors can affect the small and medium-sized companies in Bahrain, either negatively or positively, due to the uncertainty of efficiency and productivity, such as changing technology and strategic planning policies. Strategic planning is more suitable for small and medium-sized companies in Bahrain to facilitate a rapid response to unstable operating environments.
Moreover, George, Walker [21] assert that strategic planning may or may not contribute to job performance. The goal-setting theory does not necessarily elucidate the conditions under which strategic planning SP may have a stronger or weaker effect on job performance. Hence, in the current study, two specific requirements (one moderator) could influence the impact of strategic planning on the relationship between career path planning and job performance. This moderator helps assess the contextual reality of small and medium-sized companies in Bahrain and thus provides further middle-range theorizing on strategic planning (SP). The following hypothesis is made in light of the discussions above:
Hypothesis 5 (H5).
Strategic planning moderates the relationship between career path planning and job performance (SP × CPP -> JP).

2.5. Strategic Planning Important for Both Moderating and Mediating

The importance of strategic planning cannot be overstated. Strategic planning is a process of defining an organization’s direction and making decisions on allocating its resources to pursue this direction. It plays a critical role in ensuring the success of an organization, particularly in the long term. In the context of the study, investigating both moderating and mediating effects of strategic planning on the relationship between two variables indicates the significance of strategic planning. Moderating effects refer to the way in which a third variable affects the strength or direction of the relationship between two other variables. Mediating effects refer to the way in which a third variable explains the relationship between two other variables.
In other words, strategic planning is considered an important variable that can either affect or explain the relationship between other variables in the study. Therefore, it is important to understand the role of strategic planning in the context of the study and why it was investigated as a moderating or mediating variable. By addressing this issue in detail, the study can provide a more comprehensive understanding of the impact of strategic planning on the variables under investigation.

3. Methodology

This research intended to assess the moderating effect of strategic planning on the relationship between career path planning and job performance in SMEs in the Kingdom of Bahrain using strategic planning as a moderating variable. It explains the study design, methods for gathering reliable data, ways to test various hypotheses, and methods to respond to the research questions. Discussions on sampling, data processing, instrumentation (the online surveys), and their validation are given in this section.

3.1. Conceptual Framework

The study includes one dependent variable and two independent variables, as shown in Figure 1. The dependent variable is the employees’ job performance while the independent variables of the study are career path planning and strategic planning. Further, this study uses strategic planning as a moderating variable to quantify the impact of career path planning and job performance among Bahraini employees in SMEs in the Kingdom of Bahrain.

3.2. Research Design

The research design is a framework that explains how data are gathered, analyzed, and presented. This can support researchers in ensuring that the acquired and analyzed data enable the researcher to meet research objectives and provide the most precise response to research questions. The study method used in this research is a quantitative approach. Data were collected through a structured online questionnaire with questions intended to gather data.
In this study, we analyzed primary data using quantitative analytical programs. Descriptive research is intended to be carried out to provide a precise and reliable depiction of the variables pertinent to the research issue. Additionally, a correlational research design was employed in this study to examine the statistical link between the variables that were determined for it.

3.3. Population and Sampling

The employees of Bahrain’s small and medium-sized enterprises (SMEs) were the population of the study. We reached out to a convenience sample of people employed by SMEs to participate in this study. Convenience sampling is often the preferred method for researchers since it is both quick and easy to provide data. According to Cochran’s calculation, the sample size should be approximately 249, with a margin of error of 0.05 and a critical value of 1.96 for a population of 700. Assuming a response rate of 92.4%, the sample size was 396. The survey sample size calculator is shown in Figure 2.

3.4. Data Collection Tools

Figure 3 illustrates the procedure of data gathering. Descriptive analysis was carried out using the SPSS version 28 program to analyze the study data.

4. Results

4.1. Characteristics of the Respondents

Table 1 shows the demographic information about the participants. Only 231 of the total 249 participants in the survey responded, giving a response rate of 92.4%. Among these, 19.5% were female and 80.5% were male. The gender variable’s average mean and standard deviation was 1.195 ± 0.396. In the survey, there were more men than women (80.5% vs. 19.5%). Furthermore, 38.5% of the sample’s participants were between the ages of 41 and 50. The remaining age categories made up 67.5% of the population sample. Age had an average mean and standard deviation of 2.870 ± 0.854. Most employees from small and medium-sized businesses (SMEs) who took part in the survey had more than 10 years of experience in the industry; the average mean and standard deviation was 1.84 ± 0.363. Half of those surveyed (44.6%) had a bachelor’s degree while the remaining participants made up 45.6% of the population sample. In addition, the mean of the variables in this research had a value of 2.88, and the standard deviation for the entire variable set was 1.163.

4.2. µ ± SD and Rank for the Variables

According to Table 2, the findings show that the mean score of all variables are higher than 2.28. This demonstrates that respondents were knowledgeable about the significance of strategic planning and career path planning in job performance. Furthermore, the career path planning was most affected by strategic planning due to the higher mean and the lowest SD. The mean score of career path planning was 2.40 ± 1.25, which was significantly higher than the rest of the factors, followed by strategic planning, with a mean score of 2.31 ± 1.27. Finally, job performance registered a higher SD and the lowest mean among the variables, 2.14 ± 138. The overall mean across all dimensions was 2.28 ± 1.3, which highlights the high level of allowable variability inherent in the dataset. Figure 4 shows a simple bar graph that represents the study variables’ µ ± SD.

4.3. Reliability and Composite Reliability

The internal consistency in the measurement of the variable is provided by reliability measurement. For an instrument to be accepted, the reliability must be higher than 0.60. We used SPSS 28 to conduct Cronbach’s alpha and composite reliability tests in this research. Table 3 shows that composite reliability (CR) values ranged from 0.961 to 0.872, while Cronbach’s alpha values were between 0.952 and 0.893. As the square roots of AVE (0.739 and 0.602) were higher than the correlation value (0.50) between two latent constructs, discriminant validity was obtained.
Furthermore, it was indicated that there were no redundant components in the model. Therefore, the results for Cronbach’s alpha, AVE, and composite reliability were all significant and had achieved the appropriate acceptance criteria. Table 3 shows the traits of the three used constructs. All composite reliability estimates are higher than 0.950, and Cronbach’s alpha values are higher than 0.7. This shows a good level of dependability. Consequently, this finding is fit and has good internal consistency [43].

4.4. Assessment of Measurement Model

The assessment of measurement models can be carried out using several techniques, such as exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. Additionally, these techniques can be used to determine the reliability and validity of the model by examining the model’s fit indices, such as the chi-square statistic, root mean square error of approximation, and goodness of fit index. This research used Smartpls4 to determine the reliability and validity of the model by examining the model’s fit indices such as SRMR, exact fit criteria, Nfi, and chi2 (see Figure 5).

4.5. Convergent Validity

Hair, Hollingsworth [44] defined convergent validity as the degree to which a set of variables converge in their measurement of a certain concept. In order to establish and confirm convergent validity, certain requirements must be met. These requirements include factor loadings, composite reliability (CR), and average variance extracted (AVE), all of which were used sequentially, as recommended by [44]. As a result, item loadings were examined, and it became clear that all the items were over 0.50, suggesting acceptable levels [45] at the significance level of 0.01; all factor loadings are thus significant (see Table 3). The composite reliability measures how consistently a set of items shows the latent construct; it is considered a second feature of convergent validity.
As shown in Table 4, the CR values are higher than the suggested value of 0.7, ranging from 0.961 to 0.872. These findings confirm the convergence validity of the measurement model. To verify the convergent validity of the outer model, the values of AVE were examined. The AVE represents the average variance across a set of items relative to the variance shared with measurement errors. More precisely, AVE calculates the difference between the variance collected by the indicators and the variance caused by measurement errors. Table 4 contains the AVE values for the current study. The AVE values for all constructs were over the threshold of 0.5, ranging from 0.739 to 0.616. As a result, the model’s convergent validity is satisfactory.

4.6. Discriminant Validity for Latent Variables

The degree to which a construct is empirically different from other constructs is known as its discriminant validity [44]. In other words, it demonstrates that the items that make use of various structures do not overlap. Therefore, although correlated, constructs measure distinct concepts. This implies that if the discriminant validity is validated, the variance shared by each construct and its measures should be higher than the variance shared by individual constructs [46]. The method developed by Fornell and Larcker (1981) was used to validate the discriminant validity in this study. Table 5 shows the discriminant validity of the variables used. The discriminant validity in this current study was ensured [47].

4.7. The Prediction Relevance of the Model

The R-Square (R2) of the endogenous variable accounts for the variance of a particular variable that is described by predictor variables, as is well known in the field of multivariate data analysis [43]. Therefore, the size of the R2 for the endogenous variables was considered as a measure of the model’s capacity for prediction. Additionally, the model’s predictive validity was verified using the sample reuse method created by Stone and Geisser in 1975. In this respect, it is claimed that the sample reuse technique fit the PLS modeling approach quite well. According to the squared multiple correlation (R2) values for the dependent variables (job performance) in this study, where R2 = 0.78 for JP, as shown in Table 6 and Figure 6, the model demonstrated an outstanding fit to the data. Therefore, a significant portion of the variance in the job performance in SMEs in the Kingdom of Bahrain is explained by a single independent latent variable (IV). Meanwhile, dependent variables explained more than 78% of the variation in job performance in SMEs in the Kingdom of Bahrain.
R 2 = 1 − R s s T S S

4.8. Effect Size, f2

Effect size is mostly used in this study to assess the impact and relationship of factors. Cohen [48] argues that the ideal effect size is less than 0.02 (0.02 = tiny, 0.15 = medium, 0.35 = high). The effect of CPP is small in Table 7, while SP has a medium effect size overall and a stronger effect than others. The following formula was used to determine the impact of strategic planning and career path planning on job performance in SMEs in the Kingdom of Bahrain; the impact of affective commitment as mediation on job performance is shown in Table 7 and Figure 7.
E f f e c t   s i z e f = √   ( η 2 / ( 1 − η 2 ) ) .
As shown in Table 7 and Figure 7, both career path planning and strategic planning variables received nearly identical scores in terms of the effect of CPP → JP (0.363), and SP → JP was 0.300, which had a strong effect of approximately 36% on the job performance. The highest effect was between SP → JP, which is considered the strongest effect among all the variables, scoring above the 0.35 range. This was considered a strong effect according to Cohen’s criterion, while SP × CPP had no effect.

4.9. The Assessment of the Inner Model and Hypothesis Testing Procedures

Table 8 shows the assessment of the inner model and hypothesis testing procedures.

4.10. Path Model Significance Results

The bootstrapping method coupled with Smart PLS4 was used in the current study to guarantee that the path coefficients are statistically significant. Bootstrapping was mainly used to provide t-values associated with each path coefficient. As a natural consequence, the p-values for the hypotheses were also generated, as shown in Table 8. It can be concluded that strategic planning (SP → JP) positively impacted job performance as the dependent variable and was supported at a significance level of 0.01 (β = 0.487, t = 7.773, p = 0.000). Additionally, this result confirms our hypothesis due to the fact that the t-value was greater than 1.96 with a lower SD (0.063), p > 0.000. Moreover, SME employees supported strategic planning as an independent variable and job performance as the dependent variable. We accepted the alternate hypotheses and rejected the null hypothesis due to the significant result of this variable. The correlation between career path planning and strategic planning (SP → CPP) was confirmed at the level of significance of 0.01 (β = 0.885, t = 70.033, p < 0.000). This variable is considered the most significant due to the results, especially as it had the lowest value of SD, which means that there was less disagreement among respondents. Further, statistical tests revealed that strategic planning and career path planning (SP → CPP) were supported by SME employees at a significance level of 0.01 (β = 0. 454, t = 6.903, p < 0.000). Based on this result, we accepted the alternative hypothesis and rejected the null hypothesis. The PLS.4 bootstrapping approach was used to analyze all the relationships previously mentioned. Figure 8 graphically illustrates the hypotheses.

4.11. Testing the Level of Significance of the Indirect Effect

Figure 8 shows that an indirect effect is registered for SP → CPP → JP = 0.431(0.885 × 0.487) (a × b), while the direct effect is 0.487. Thus, it can be concluded that the construct SP in SME employees as a mediator affects the relationship between CPP and JP. Statistically, this hypothesis is considered partial mediation since the direct effect is significant and less than 80%. Table 9 provides the results obtained from the indirect analysis.

4.12. Moderating Effect of the Strategic Planning

This study also aimed to examine the moderating effect of strategic planning in the relationship between career path planning and job performance (SP × CPP → JP) in small and medium-sized enterprises (SMEs) in the Kingdom of Bahrain. To carry out moderation analysis via Smart PLS4, an interaction term labeled “moderating effect 1” was added to the model, as shown in Figure 9.
As indicated, the interaction term has a positive impact of 0.099 on job performance (JP). The results in the table show that the value of the relationship between career path planning (CPP) and job performance (JP) is 0.487. It implies that when (SP) is increased by one standard deviation unit, the relationship between CPP and JP is increased by the size of the interaction term (0.487 + 0.885 = 1.8). Conversely, if SP is reduced by one standard deviation unit, the relationship between CPP and JP becomes 0.424 (0.063 less than 0.487). The following simple slope plot depicts the two-way interaction effect. The three lines in Figure 10 represent the relationship between JP (y-axis) and CPP (x-axis). The relationship of the mean level of influence of the moderating variable SP is shown by the middle line. The other two lines show the relationship between CPP and JP as well as the mean values of SP, where SP is 1 SD above and below the mean.
Hypothesis H5 postulates that strategic planning (SP) moderates the relationship between career path planning (CPP) and job performance (JP). The result indicates that SP moderates the relationship between CPP and JP (ß = 0.063; t-value = 2.055; p-value = 0.020) p-value ˂ 0.05), as shown in Table 10. Hence, H5 is supported. However, it is suggested that the values 1 SD above and below the mean at the red line should be chosen (Cohen et al., 2003). The relationship between CPP and JP is positive for all three lines, as indicated by their positive slope. Therefore, higher levels of CPP result in superior JP. The upper line (in green, Figure 10) represents a high level of the moderating constructor (1 standard deviation above the mean at green line) and SP has a flatter slope, while the higher line (in blue, Figure 10) represents a low level of the moderating constructor (1 standard deviation below the mean at blue line). As a result, the simple slope plot and moderation analysis in Excel (Figure 11) support the positive relationship between CPP and JP. Furthermore, these results strongly support the claim discussed earlier regarding the positive interaction term, where higher SP levels entail a more positive relationship between CPP and JP. Based on the positive and steeper slope, stronger strategic planning (SP) increases the relationship between career path planning (CPP) and job performance (JP). Moreover, this result is consistent with the Excel results shown in Figure 11. Additionally, Figure 11 presents the results of SP as a moderating variable between CPP and JP.
According to the results of the moderation analysis, when SMEs in Bahrain have strong strategic planning (SP), the relationship between CPP and job performance will be strong. To implement CPP and boost job performance, SMEs must keep up with rapid changes in the business environment. Furthermore, when SMEs have good strategic planning (SP), the influence of CPP on job performance is strongly moderated. In order to improve the performance of their SMEs, owners and managers in Bahrain may make use of this information to match the implementation of their CPP with the business environment. This may support good decision making to implement CPP for the objective of improving JP.

5. Discussion

The empirical findings confirmed the predictive validity of most of the hypothesized variables. The results of the current study mainly showed how well the measurement model suited the data observed. Furthermore, all variables could meet the primary construct validity and reliability standards. Additionally, the current study model explained about 78% of the variance in SME employees’ job performance. This confirms the model’s theoretical foundations. The results of the path coefficient analysis confirmed most of the proposed hypotheses. Statistically, Hypothesis H1 regarding the career path planning and job performance relationship (CPP → JP) was supported, as CPP was identified as the major factor that influences employee performance.
As seen in Table 8, CPP was an influential variable predicting the dependent variable, JP. This demonstrates the importance of the cognitive and functional benefits of CPP from the perspective of SME employees. One of the major tasks of career planning, especially in SMEs, is to determine the employees’ skills, capabilities, and abilities. Therefore, suitable and qualified employees can be prepared through training and development programs to assume higher roles and positions in an organization when required. Thus, the respondents’ response to this variable was positive and confirmed statistically. Hence, managers of SMEs have to arrange for more training and development programs for employees, which could help in effective career path planning that contributes to improving job performance.
This study supports the working model of career path planning. Hypothesis H2 on the relationship between strategic planning and career path planning (SP → CPP) was supported, with CPP being one of the most crucial ways that an employee can set a roadmap for his or her future career in the organization if s/he follows a clear, realistic plan and depends on a self-assessment of his/her abilities, which must correspond to his/her ambitions. Additionally, the SP variable had the most statistically significant impact for participants as it obtained the highest t-value (70.033) and the lowest SD (0.013). Mathematically, it is considered by participants as the strongest correlation and sufficient justification to support the hypotheses. This result was consistent with the finding of [2,51]. This result could alert SME mangers of the importance of effective strategic planning that could help in effective career path planning where there is a significant relationship between SP and CPP.
The SP → JP finding supports the third hypothesis regarding the fact that strategic planning influences job performance. The results show that strategic planning had a strong effect on job performance. Considering the lack of an empirical investigation that integrates these variables together to form a comprehensive strategic model, this finding provides an important contribution to the field of strategic planning. One of the major components and essential aspects of strategic planning is that it works to synergize the interests of the employees and the company and ensures that each of them work to achieve a common goal. It also aids in determining the organization’s direction. Therefore, the respondents’ response to this variable was positive due to the shared interests between employees and firms, and this is congruent with the findings obtained by [51] concerning SP. The results obtained in the current study will help managers to understand the important effect of strategic plan on job performance, as a well-designed strategic plan will help employees and managers to achieve the business’s target performance.
Hypothesis H4 suggested that the indirect effect of SP → CPP → JP equals 0.431(0.885 × 0.487) (a × b), while the direct effect is 0.487. Hence, CPP partially mediates the relationship between SP and JP positively. However, the results of this hypothesis are more important for SME staff. The current study predicted a positive relationship between SP and JP, and this received indirect effect confirmation: SP → CPP → JP = 0.431(0.885 × 0.487). Since the indirect effect is significant and much less than 80% in this situation, the indirect hypothesis is statistically considered as partial mediation. The outcomes of the indirect analysis are shown in Table 9. The current study’s moderating hypothesis (H5) states that the link between career path planning (CPP) and job performance is moderated by strategic planning (SP × CPP → JP). The outcome shows that SP has a moderating effect on the interaction between CPP and JP. Statistically, the effect was positive according to mathematical proofs, simple slope plot analysis, and moderation analysis in Excel, shown in Figure 9 and Figure 10. This result agrees with the study of Richard M. Walker in the United States [21].
SME stakeholders in the Kingdom of Bahrain must consider the consequences of these important findings. For instance, HR managers of SMEs in the Kingdom of Bahrain may be urged to create a scheme that supports employees’ expectations in terms of strategic planning and career path planning. Additionally, recognizing the influence that continuously guides employees in managing their individual careers may help them define their long-term career path planning goals. These organizational-level activities increase motivation and provide employees with a feeling of “worth”. As a result, these initiatives may also aid Bahraini SMEs by lowering staff turnover and raising the levels of employee satisfaction.

6. Conclusions

The failure and poor performance of medium-sized businesses have gained attention from researchers, entrepreneurs, and policy makers worldwide. Although previous studies have attempted to analyze this topic, the significance of the complementarity between business expertise, strategic planning, and the performance of small and medium-sized enterprises (SMEs) has not been investigated. Therefore, this study explored the association between career path planning and job performance among SME employees in the Kingdom of Bahrain, as well as the moderating effect of strategic planning on that relationship.
The findings of the study revealed that strategic planning and career path planning affected SME employees’ performance. Additionally, it was found that strategic planning substantially moderated the relationship between career path planning and job performance. Moreover, the results showed that SP and CPP had the greatest impact on JP. Based on the obtained findings, it is shown that the proposed hypotheses are accepted. The study revealed the impact of the moderating variable, SP, on the relationship between CPP and JP.
The findings of the present study about strategic planning and career path planning could be useful for managers, researchers, and policy makers. Similarly, the findings introduce the emergence of the comprehensive approach to Human resource management (HRM) dimension (strategic planning and career path planning). Furthermore, this study offers empirical, theoretical, and practical evidence regarding the relationship between these variables (SP → CPP → JP). This study provides practical evidence of strategic planning as a moderator between career path planning and job performance (SP × CPP → JP). The findings also indicate that small and medium-sized enterprises (SMEs) in the Kingdom of Bahrain are aware, to some extent, of the appropriate role that strategic planning and path planning play in job performance in their daily operations.
This is the first study in the Kingdom of Bahrain to investigate the role of strategic planning in the relationship between career path planning and job performance. It uses strategic planning as a moderator in the relationship between career path planning and job performance. The present study emphasizes the importance of the role of HRM, and its dimensions such as strategic planning and career path planning should be considered because of their value in leadership competencies; attention should be also paid to the main components of HRM. Further, the study suggests that much attention be paid to human capital, the most important element in the success of any operation, by developing strategies related to career path planning in order to come up with the best results for SME staff. The study suggests that support from senior management be provided to SME employees in order to achieve creativity at work and to support creativity financially and morally. This would help in achieving leadership competencies capable of managing the organization.
This study has its limitations. Some of these limitations are that it (i) selected only one country and (ii) did not cover a large sample size. We recommend that future studies also consider other countries, such as Arab countries in general and members of the Gulf Cooperation Council (GCC) in particular. Further, the sample size should be enlarged in future research. For generalizing the results, we suggest employing longitudinal methods rather than cross-sectional methods, along with other distinct analysis strategies, including CFA and SEM procedures.

Author Contributions

Conceptualization, A.A.A.A. and M.A. (Mahmoud AlZgool); methodology, A.A.A.A. and M.A. (Mohammed Alzoraiki); software, A.A.A.A. and M.A. (Mahmoud AlZgool); validation, A.A.A.A. and M.A. (Mahmoud AlZgool); formal analysis, A.A.A.A. and M.A. (Mohammed Alzoraiki); investigation, A.A.A.A. and M.A. (Mahmoud AlZgool); resources, A.A.A.A. and M.A. (Mahmoud AlZgool); data curation, A.A.A.A. and M.M.; writing—A.A.A.A. and M.A. (Mohammed Alzoraiki); writing—review and editing, M.S.M.A.-A.; visualization, M.S.M.A.-A.; supervision, A.A.A.A.; project administration, M.S.M.A.-A.; funding acquisition, A.A.A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted with the approval of Gulf University.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Conceptual framework. Source: Author’s conceptual framework and hypotheses model.
Figure 1. Conceptual framework. Source: Author’s conceptual framework and hypotheses model.
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Figure 2. Margin of error vs. sample size. The sample size (n) is calculated according to the formula: n = [z2 × p * (1 − p)/e2]/[1 + (z2 × p × (1 − p)/(e2 × N))] where z = 1.96 for a confidence level (α) of 95%, p = proportion (expressed as a decimal), N = population size, e = margin of error = 1.96, p = 0.5, N = 700, e = 0.05, n = [1.962 × 0.5 × (1 − 0.5)/0.052]/[1 + (1.962 × 0.5 × (1 − 0.5)/(0.052 × 700))], n = 384.16/1.5488 =248.037, n ≈ 249 [42].
Figure 2. Margin of error vs. sample size. The sample size (n) is calculated according to the formula: n = [z2 × p * (1 − p)/e2]/[1 + (z2 × p × (1 − p)/(e2 × N))] where z = 1.96 for a confidence level (α) of 95%, p = proportion (expressed as a decimal), N = population size, e = margin of error = 1.96, p = 0.5, N = 700, e = 0.05, n = [1.962 × 0.5 × (1 − 0.5)/0.052]/[1 + (1.962 × 0.5 × (1 − 0.5)/(0.052 × 700))], n = 384.16/1.5488 =248.037, n ≈ 249 [42].
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Figure 3. Procedure of data gathering. Source: Generated by the authors.
Figure 3. Procedure of data gathering. Source: Generated by the authors.
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Figure 4. A simple bar graph that represents the study variables’ µ ± SD. Note: SP = strategic planning, CPP = career path planning, JP = job performance.
Figure 4. A simple bar graph that represents the study variables’ µ ± SD. Note: SP = strategic planning, CPP = career path planning, JP = job performance.
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Figure 5. Assessment of measurement model.
Figure 5. Assessment of measurement model.
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Figure 6. R-square adjusted.
Figure 6. R-square adjusted.
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Figure 7. Effect size, f 2 .
Figure 7. Effect size, f 2 .
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Figure 8. Indirect effect. Indirect effect for SP → CPP → JP = 0.431(0.885 × 0.487). Note: The indirect effect can be extracted by multiplying (a × b); a value greater than 20% and less than 80% is characterized as partial mediation [43]. Source: Generated by the authors.
Figure 8. Indirect effect. Indirect effect for SP → CPP → JP = 0.431(0.885 × 0.487). Note: The indirect effect can be extracted by multiplying (a × b); a value greater than 20% and less than 80% is characterized as partial mediation [43]. Source: Generated by the authors.
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Figure 9. Moderator model.
Figure 9. Moderator model.
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Figure 10. Simple slope plot analysis: SP × CPP -> JP.
Figure 10. Simple slope plot analysis: SP × CPP -> JP.
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Figure 11. Moderation analysis in Excel of SP × CPP → JP.
Figure 11. Moderation analysis in Excel of SP × CPP → JP.
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Table
Table 1. Characteristics of participants.
Table 1. Characteristics of participants.
Respondents’ CharacteristicsNMean ± SD(%)
GenderMale 186 80.5
Female45 19.5
Total2311.195 ± 0.396
AgeLess than 2010 4.3
31–4071 30.7
41–5089 38.5
above 5061 26.4
Total2312.870 ± 0.854
Experience<1036 15.6
>10195 84.4
Total2311.84 ± 0.363
EducationHigh school40 17.3
Diploma29 12.6
Bachelor103 44.6
Post-graduate37 16.0
Others22 9.5
Total2312.88 ± 1.163
Table
Table 2. Mean ± SD and rank for the variables.
Table 2. Mean ± SD and rank for the variables.
ConstructsNumber of ItemsCodeMean ± SDRank
Strategic planning7SP2.31 ± 1.272
Career path planning9CPP2.40 ± 1.251
Job performance9JP2.14 ± 1383
Average33 2.28 ± 1.3
Note: Rating scale is a 5-point Likert scale: (1) strongly disagree, (2) disagree, (3) neutral, (4) agree, (5) strongly agree. Scores range from 1 to 5, with higher scores indicating greater competence. SD: standard deviation.
Table
Table 3. Reliability and composite reliability.
Table 3. Reliability and composite reliability.
ConstructCronbach’s AlphaComposite ReliabilityAverage Variance Extracted (AVE)
CPP0.9290.9410.639
JP0.9520.9610.739
SP0.8530.8720.516
Cronbach’s alpha: Average measure of internal consistency and item reliability and is preferred when EFA is used for factor extraction; <0.7 is accepted. CR: Measures scale reliability overall and is preferred with CFA. AVE: Measures the level of variance captured by a construct; 0.5 is accepted. AVE: Average variance extracted. CR: Composite reliability [43].
Table
Table 4. The convergent validity analysis.
Table 4. The convergent validity analysis.
ConstructCodeNumber of ItemsFactor
Loading		CRAVE
Strategic planningSP70.6870.9410.639
Career path planningCPP90.7180.9610.739
Job performanceJP90.8470.8720.516
Notes: Factor loading: Variance explained by the variable on that particular factor; <0.7 or higher is accepted [44]. CR: Measures scale reliability overall and is preferred with CFA. AVE: Measures the level of variance captured by a construct; 0.5 is accepted. AVE: Average variance extracted. CR: Composite reliability.
Table
Table 5. Discriminant validity analysis.
Table 5. Discriminant validity analysis.
ConstructCPPJPSPSP × CPP
CPP0.899
JP0.7930.880
SP0.7850.7700.818
SP × CPP0.2190.2980.310
Note: The square root of the average variance extracted is represented by a diagonal, while the other elements reflect the correlation estimate.
Table
Table 6. Coefficient of determination result R2.
Table 6. Coefficient of determination result R2.
Exogenous
Construct		Endogenous
Construct		R2[44][48]
SP, CPP JP0.78SubstantialSubstantial
Key: SP = strategic planning, CPP = affective commitment, JP = job performance. Higher value is preferred: 0.67 = substantial, 0.33 = average, 0.19 = weak [49].
Table
Table 7. Effect size, f 2 .
Table 7. Effect size, f 2 .
CPPJPSPSP × CPP
CPP0.0000.3630.0000.000
JP 0.0000.0000.0000.000
SP 3.6250.3000.0000.000
Note: f2 = (R2 included − R2 excluded)/(1 − R2 included). Key: f 2 of 0.02 = weak effect, 0.15 = moderate effect, 0.35 = strong effect [48].
Table
Table 8. Mean, STDEV, t-values, p-values, and decision.
Table 8. Mean, STDEV, t-values, p-values, and decision.
No.HypothesisβµSDt-Valuep-ValueDecision
H1CPP → JP0.4870.4830.0637.7330.000Supported
H2SP → CPP0.8850.8860.01370.0330.000Supported
H3SP → JP0.4540.4610.0666.9030.000Supported
Note: SP = strategic planning, CPP = career path planning, JP = job performance. Beta (β) values range from −1 to +1. Assessed significance and confidence intervals. Significance value is based on the degrees of freedom, p < 0.05 [50].
Table
Table 9. Indirect effect.
Table 9. Indirect effect.
Indirect Effect
Specific indirect effects
SP → CPP → JP0.431
Table
Table 10. Moderation analysis.
Table 10. Moderation analysis.
No.HypothesisβµSDt-Valuep-ValueDecision
H5SP × CPP → JP0.0630.0620.0312.0550.020Supported
βeta (β) values range from −1 to +1. Assessed significance and confidence intervals, standard deviation (SD), p-values. Significance: p < 0.05 [24].
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Ali, A.A.A.; AlZgool, M.; Alzoraiki, M.; Milhem, M.; Al-Absy, M.S.M. Moderating Effect of Strategic Planning on the Relationship between Career Path Planning and Job Performance. Sustainability 2023, 15, 8490. https://doi.org/10.3390/su15118490

AMA Style

Ali AAA, AlZgool M, Alzoraiki M, Milhem M, Al-Absy MSM. Moderating Effect of Strategic Planning on the Relationship between Career Path Planning and Job Performance. Sustainability. 2023; 15(11):8490. https://doi.org/10.3390/su15118490

Chicago/Turabian Style

Ali, Ali Ahmed Ateeq, Mahmoud AlZgool, Mohammed Alzoraiki, Marwan Milhem, and Mujeeb Saif Mohsen Al-Absy. 2023. "Moderating Effect of Strategic Planning on the Relationship between Career Path Planning and Job Performance" Sustainability 15, no. 11: 8490. https://doi.org/10.3390/su15118490

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