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Extensive research in strategic management has shown that more than half of the business strategies fail to accomplish their intended objectives due to problems with strategy implementation. However, in comparison to strategy formulation, strategy implementation has received insufficient attention both at the theoretical and empirical levels. The objective of the study was to develop a comprehensive model that augments the effectiveness of strategy implementation in the context of a developing economy. A sample of 229 top and middle-level managers of commercial banks was chosen to collect quantitative data, and around 187 (82%) questionnaires were returned. The quantitative data analysis was carried out using structural equation modelling. By operationalizing strategy implementation capability as a second-order reflective-formative construct, the study established an empirically informed strategy implementation model that entails three components, namely, critical strategy implementation-related resources, key elements of strategy implementation capability and strategy implementation performance. The study contributes to the existing knowledge on strategy implementation by expanding the extant strategy implementation frameworks/models to a broad-based and integrative model.

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Introduction

Research conducted globally has indicated that most strategies fail at the strategy implementation stage (Rani, 2019). According to empirical studies, about 50 to 90 percent of business strategies failed to achieve their objectives, with poor strategy implementation accounting for the majority of the failures (Bahadoriet al., 2018). Researchers pointed out that strategy implementation is much more difficult than strategy formulation, and it requires serious attention from both academics and practitioners in the field (Greeret al., 2017). Various authors and scholars have developed different strategy implementation models that aim to enhance the effectiveness of strategy implementation.

Although the extant strategy implementation models have identified several factors for the success of strategy implementation, they have notable gaps and limitations. In the extant strategy implementation literature, there appears to be a divergence of opinion on both theoretical and empirical levels in identifying and defining the key strategy implementation factors and components (Srivastava & Sushil, 2017; Verweire, 2014). As a result, academics and practitioners have been pleading for the development of an acceptable and integrative strategy implementation model that improves the success of strategy implementation (Verweire, 2014).

The literature on strategy implementation is fragmented with the majority of studies focusing on one or a few aspects of strategy implementation impacting variables (Mwanje, 2016). Furthermore, a large number of studies on strategy implementation have not been tested in a real-world situation (Oliveiraet al., 2018). There is a paucity of practical data for making strategy implementation analyses, and an assessment of factors that influence strategy implementation efficacy is not sufficiently performed (Bahadoriet al., 2018).

In most of the strategy implementation models, there is also a noticeable gap in defining and operationalising what a strategy implementation capability means? What its components are? How strategy implementation capability as a construct is measured? (Huber, 2011; Oliveiraet al., 2018). These and other questions are not clearly addressed in the extant strategy implementation literature.

Another notable gap in the strategy implementation literature is that the majority of strategy implementation models focused on firms in developed countries, except for only a small number of studies undertaken in emerging economies (Mwanje, 2016). This circumstance imposes its own constraints on improving the efficacy of strategy execution in developing economies, as solutions and techniques created for advanced economies may not be applicable to developing economies.

In general, strategy implementation as an area of study deserves further exploration and synthetisation as the process for effective strategy implementation is yet to be fully identified and explored. As strategy implementation is a very complex process that entails fragmented tasks, there is a need to have a comprehensive and structured strategy implementation model that entails the practical components of the strategy implementation process.

Literature Review

Strategy Implementation-Related Resources

Research indicates that the level of managerial skill affects the capability of organizations to implement their strategy effectively (Cynthia, 2020; Mwanje, 2016). Managerial skills refer to “managers’ ability to effectively organize, allocate, and configure various firm resources” (Guoet al., 2013, p. 452). Managerial skills can be considered valuable resources that enable managers to effectively build, reconfigure, and manage organizational resources and capabilities supporting strategy implementation (Guoet al., 2013). Managerial skills, particularly human and technical skills, enable managers to become more supportive of their employees and hence attainment of better strategy implementation performance through effective organization and motivation of people as well as efficient and effective decision making (Cynthia, 2020; Guoet al., 2013). Similarly, Kemperet al. (2013) stressed the importance of managerial social capital as a valuable resource for enhancing an organization’s strategy implementation capability. Managerial social capital is defined as a type of social capital that exists among managers in an organization, and it incorporates major elements that include networks, norms, and trust among managers in a given organization (Yohaneset al., 2017). In their studies on four multinational companies, Lehtimäki and Karintaus (2013) noted the importance of managerial social capital in implementing strategies effectively by improving access to information, trust, and collaboration among individuals. Accordingly, 2 research hypotheses were proposed to examine the relationship between the critical strategy implementation-related resources and strategy implementation capability.

H1: Managerial skills positively and significantly affect strategy implementation capability.

H2: Managerial social capital positively and significantly influences strategy implementation capability.

Strategy Implementation Capability and Its Components

Strategy implementation capability is one of the major organizational capabilities that enable organizations to be competent and successful in the marketplace (Verweire, 2014). Strategy implementation capability was conceptualized as a second-order construct composed of various first-order constructs that include strategy communication, strategy cascading, incentives and rewards alignment, organizational structure alignment, organizational culture alignment and strategy monitoring and evaluation (Alharthyet al., 2017; Bernardoet al., 2017; Srivastava & Sushil, 2017). These presumed components of strategy implementation capability were selected as they are very significant factors for effective strategy implementation (Alharthyet al., 2017; Bernardoet al., 2017; Srivastava & Sushil, 2017). Table I summarizes the findings on the effects of strategy implementation capability components on strategy implementation success.

Contributors Key findings
Alharthyet al. (2017); Bernardoet al. (2017); Thanyawatpornkulet al. (2016) Strategy communication positively affects the success of the strategy implementation.
Srivastava and Sushil (2017); Kaplan and Norton (2008) Strategy cascading positively affects the success of the strategy implementation.
Srivastava and Sushil (2017); Mwanje (2016); Mutie and Irungu (2014) Organizational structure alignment to strategy positively influences the success of the strategy implementation.
Siddique and Shadbolt (2016); Nyamboga and George (2014) Organizational culture alignment to the strategy positively influences the success of the strategy implementation.
Siddique and Shadbolt (2016); Mutie and Irungu (2014) Incentives and reward alignment to the strategy positively affect the success of the strategy implementation.
Obeidatet al. (2017); Maiche and Oloko (2016) Strategy monitoring and evaluation positively affect the success of the strategy implementation.
Table I. Summary of Findings on the Effects of Strategy Implementation Capability Components on Strategy Implementation Success

Thus, 6 research hypotheses on the relationship between strategy implementation success and the components of strategy implementation capability were proposed:

H3: Strategy communication is positively and significantly related to strategy implementation success.

H4: Strategy cascading is positively and significantly related to strategy implementation success.

H5: Organizational structure alignment is positively and significantly related to strategy implementation success.

H6: Incentives and rewards alignment is positively and significantly related to strategy implementation success.

H7: Organizational culture alignment is positively and significantly related to strategy implementation success.

H8: Strategy monitoring and evaluation are positively and significantly related to strategy implementation success.

On top of the individual components of strategy implementation capability, the study has tested a research hypothesis that assesses the relationship between strategy implementation capability, as a single construct, and strategy implementation success. Strategy implementation capability is a bundle of organizations’ strategy implementation abilities that contribute to strategy implementation success, and it is vital for effective strategy implementation (Huber, 2011). It is obvious that if an organization creates the capability to implement its strategy, it will definitely implement its strategy successfully. Accordingly, the following hypothesis was proposed:

H9: Strategy implementation capability positively and significantly affects strategy implementation success.

The Mediating Role of Strategy Implementation Capability

The resource-based view commends that organizational effectiveness is highly influenced by an organization’s ability to convert its resources into capability. Under the resource capability approach, resources such as managerial skills and managerial social capital are critical sources for creating a capability that enables organizations to implement their strategies successfully (Guptaet al., 2018). The study presumes that the key drivers of effective strategy implementation are the critical components of strategy implementation capability and resources within the firm that are used as input for translating the formulated strategy into a successful strategy implementation endeavor (Morganet al., 2012). Various researchers underscore the significant roles of strategy implementation-related resources (such as managerial skills and managerial social capital) and organizational capabilities (such as strategy implementation capability) and their interrelation for the successful implementation of a strategy (Cynthia, 2020; Kemperet al., 2013; Morganet al., 2012; Mwanje, 2016). Thus, the following 2 hypotheses were proposed:

H10: Strategy implementation capability mediates the relationship between managerial skills and strategy implementation success.

H11: Strategy implementation capability mediates the relationship between managerial social capital and strategy implementation success.

Methodology

Considering the research aspects of the three approaches (i.e., quantitative, qualitative, and mixed), the study employed a quantitative research approach to address the study’s objectives suitably. The study utilized a cross-sectional survey research design. Accordingly, the study employed a quantitative data collection method that incorporated a structured questionnaire as a data collection instrument. The constructs in the study were operationalized based on a detailed review of the literature to enhance the validity and reliability of the measures in the questionnaire. The widely applicable measures of reliability, i.e., Cronbach’s coefficient alpha, were also used to assess the reliability of the measures (Aliet al., 2017; Hairet al., 2017). Furthermore, the study has employed statistical tests, including the Average Variance Extracted (AVE) and the Fornell-Larcker criterion, to assess the convergent and discriminant validity of the measurement in the questionnaire, respectively, as recommended by different researchers (Aliet al., 2017; Hairet al., 2012).

The study has adopted a proportional stratified sampling method to enhance the representativeness of the sample for different-sized Ethiopian commercial banks and ensure that there is an equal chance for the sampling units being selected; about 22 commercial banks are operating in Ethiopia. The population of the study was 564 top and middle-level managers (CEOs, vice presidents, and directors) working in 17 commercial banks in Ethiopia. Five commercial banks were excluded from the population of the study as these banks were established recently and had no prior experience in implementing a strategic plan. Using the widely applicable sample size determination formula (Weiers, 2002), a sample of 229 senior and middle-level managers was drawn randomly from a population of 564 top and middle-level managers working in 17 commercial banks in Ethiopia.

The quantitative data analysis was performed in two stages. First, descriptive statistics such as means and standard deviations were conducted using SPSS v20. Second, using SmartPLS-3 software, Structural Equation Modelling (SEM) was used to test the research hypotheses and model the relationships between the independent and dependent latent variables of the study. According to its predictive nature and related advantages, Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed in the study to test the research hypotheses and model the relationship between the independent and dependent latent variables of the study.

Results and Discussion

Response Rate

As indicated in the methodology, a sample of 229 questionnaires was distributed, and the researcher managed to collect 187 (82%) completed questionnaires from the 15 commercial banks in Ethiopia. Two commercial banks did not provide a permission letter for data collection, and data were not collected from them. Considering the good response rate of 82% (Saunderset al., 2007), it was acceptable to proceed with the data analysis stage.

Descriptive Statistics

Descriptive statistics such as mean and standard deviation were computed for all items in the questionnaire to assess the strategy implementation practices in the Ethiopian banking industry. The widely applicable classification for the Likert scale developed by Zaidatol and Bagheri (2011) was used to interpret the results of the descriptive statistics. According to this classification, for a five-point Likert Scale, a mean score of greater than 3.8 is considered high, 3.4 to 3.79 is considered moderate, and a mean score of less than 3.39 is considered low. As shown in Table II, the strategy implementation practices of commercial banks in Ethiopia, expressed in terms of the study constructs, were found at low and moderate levels. The strategy implementation success, strategy cascading, incentive and reward alignment, and organizational culture alignment practices of commercial banks were rated low. Meanwhile, managerial skills, managerial social capital, strategy communication, organizational structure alignment, and strategy monitoring and evaluation practices of commercial banks in Ethiopia were rated at a moderate level. Table II depicts the level of strategy implementation practices of commercial banks in Ethiopia based on the rating procedure developed by Zaidatol and Bagheri (2011).

Constructs Mean SD Level
Strategy implementation success 3.27 1.12 Low
Managerial skills 3.42 1.07 Moderate
Managerial social capital 3.54 0.94 Moderate
Strategy communication 3.63 0.93 Moderate
Strategy cascading 3.35 1.02 Low
Incentive and reward alignment 3.20 1.03 Low
Organizational structure alignment 3.42 0.95 Moderate
Organisational culture alignment 3.00 0.99 Low
Strategy monitoring and evaluation 3.50 1.01 Moderate
Table II. Summary of Strategy Implementation Practices of Commercial Banks by Study Constructs

Evaluation of the Measurement Model

The assessment of the measurement model deals with evaluating the constructs’ reliability and validity (Aliet al., 2017). Reliability can be defined as the degree to which a given measurement is free from error and produces a consistent result under different conditions (Sreejeshet al., 2014). To assess the constructs’ reliability, Cronbach’s alpha was used in the study. Cronbach’s alpha provides an estimate of the construct’s reliability based on the indicators’ inter-correlations (Hairet al., 2017). The Cronbach’s alpha should be larger than 0.7 to ensure internal consistency reliability (Aliet al., 2017). The computed Cronbach’s alpha values of all constructs of the study were above 0.7, which indicates the reliability of the measures in the questionnaire.

Construct validity checks whether a particular item measures what it is intended to measure (Sreejeshet al., 2014). If a measure captures what it is intended to measure, scores on that measure should be more correlated to scores on other similar construct (i.e., convergent validity) and not or less related to scores on different construct (i.e., discriminant validity) (Tharenouet al., 2007). To assess the convergent and discriminant validity of the measurement model, the study employed the Average Variance Extracted (AVE) and the Fornell-Larcker criterion, as recommended by different researchers (Aliet al., 2017). All the study constructs exhibited convergent validity as the Average Variance Extracted (AVE) of all constructs were computed to be above 0.5. On the other hand, the outer loading scores between any of the study constructs and its corresponding indicators were higher than any of the outer loading scores between the construct’s indicators and the other study constructs. The results, therefore, confirm the discriminant validity of the study constructs. On top of the cross-loading, the Fornell-Larcker criterion was also employed to assess the discriminant validity of constructs. The square roots of AVE for all the exogenous constructs were computed to be above the correlation coefficients among the constructs in the model, establishing the discriminant validity of the constructs.

Assessment of the Structural Model

The evaluation of multicollinearity, coefficient of determination (R2) and the significance of the path coefficients are the major criteria used for assessing the quality of a structural model in PLS-SEM (Aliet al., 2017; Hairet al., 2017). As recommended by various researchers, Tolerance and Variance Inflation Factor (VIF) values were computed to assess the multicollinearity level among constructs (Hairet al., 2017). A tolerance value below 0.20 (VIF value above 5) in the exogenous constructs shows critical multicollinearity levels. The exogenous constructs didn’t show critical levels of multicollinearity, as the Tolerance and VIF scores of all constructs were above 0.2 and below 5, respectively. The significance of the path coefficients is another measure to evaluate the significance and direction of the relationships among the latent variables in the structural model (Aliet al., 2017; Hairet al., 2017). Two structural models, i.e., the strategy implementation success model and the strategy implementation model, were proposed and tested using PLS-SEM to examine the research hypotheses of the study. Table III above shows the results of the PLS-SEM algorithm for the Strategy Implementation Success Model.

Paths Path coefficients (β) Std. Dev. t(|O/STDEV|) p
Strategy communication -> Strategy implementation success 0.191 0.064 2.966 0.003
Strategy cascading -> Strategy implementation success −0.102 0.070 1.470 0.142
Organizational structure alignment -> Strategy implementation success 0.187 0.076 2.463 0.014
Incentive and reward alignment -> Strategy implementation success 0.201 0.063 3.192 0.002
Organisational culture alignment -> Strategy implementation success 0.158 0.061 2.585 0.010
Strategy monitoring & evaluation -> Strategy implementation success 0.333 0.078 4.279 0.000
Table III. PLS-SEM Algorithm Result for Strategy Implementation Success Model

Table V shows the summary of the hypothesis-testing results for strategy implementation success and strategy implementation models.

As shown in Table III, the t and p values were computed to assess the significance of the path coefficients in the strategy implementation success model and to test the hypothesized relationships among the study constructs. The results of the PLS-SEM algorithm for the strategy implementation success model confirmed that positive and significant relationships were confirmed between strategy implementation success and other study constructs that include strategy communication, incentives and rewards alignment, organizational structure alignment, Organizational culture alignment, and strategy monitoring and evaluation. These findings are consistent with many empirical studies conducted in strategy implementation. For example,; Thanyawatpornkulet al. (2016) stated that strategy communication is one of the three most critical factors affecting the success of strategy implementation as it facilitates the proper execution of strategies through the motivation of employees. Various authors on strategy implementation also indicated that organizational structure, incentives, and rewards as well organizational culture alignments to strategy positively and significantly affect strategy implementation success (Mutie & Irungu, 2014; Mwanje, 2016; Srivastava & Sushil, 2017). Numerous researchers further pointed out that strategy monitoring and evaluation positively influence the success of strategy implementation (Maiche & Oloko, 2016; Obeidatet al., 2017). However, unlike prior research on strategy implementation, this study does not support the positive relationship between strategy cascading and strategy implementation success. Though the relationship between strategy cascading and strategy implementation was statistically insignificant, the study found that strategy cascading negatively influences strategy implementation success in the case of commercial banks in Ethiopia (β = −0.102, p = 0.142 > 0.05). Fig. 1 depicts the results of the PLS-SEM algorithm for the Strategy Implementation Success Model.

Fig. 1. PLS-SEM algorithm results for strategy implementation success model.

For making hypothesis testing for other hypothesized relationships, the second model, i.e., the strategy implementation model, has to be specified and tested. However, the strategy implementation model entails one second-order latent variable that needs to be constructed and validated before testing the model. Based on the results of the strategy implementation success model, among the six proposed components of strategy implementation capability, those strategy implementation components that significantly influence strategy implementation success were retained as candidate formative indicators for the strategy implementation capability construct. Accordingly, Strategy Implementation Capability was constructed as a second-order reflective-formative construct composed of Strategy Communication, Incentive and Reward Alignment, Organisational Culture Alignment, Organisational Structure Alignment and Strategy Monitoring and Evaluation.

The reliability and validity of constructs measured using reflective indicators were assessed based on various reliability and validity measures. However, the same validity and reliability measures could not be used for those constructs measured formatively as the evaluation of the measurement model draws on different approaches depending on whether the construct has been measured using reflective or formative indicators. As per the recommendations of various researchers, the construct validity of strategy implementation capability as a second-order reflective-formative construct was examined in three ways: by conducting multicollinearity assessment among first-order exogenous constructs, checking the nomological validity and assessing the outer weight significance (Hairet al., 2017; Huber, 2011).

First, a multicollinearity assessment was made, and no serious collinearity problem was observed among the five exogenous constructs (strategy communication, incentive and reward alignment, organizational culture alignment, organizational structure alignment and strategy monitoring and evaluation) as the Tolerance and Variance Inflation Factor (VIF) scores of all constructs were above 0.2 and below 5. Second, examining the nomological validity at the second-order construct level is vital. This enables us to check whether the formative construct carries the intended meaning. This may be demonstrated by checking whether the relationship between the research model’s second-order formative construct and other constructs is significant. To establish nomological validity, there should be a strong and significant relationship between the second-order formative construct and other constructs in the model, as suggested by the underlying theory (Henseler, 2017). The results in the strategy implementation model indicate a substantial positive association between strategy implementation capability and strategy implementation success, consistent with the underlying theory, indicating nomological validity. Third, all the outer weight scores of the five constructs were computed and found to be significant at a 5% significance level. This indicates that all five indicators truly contribute to forming the strategy implementation capability construct. Thus, the assessment results of multicollinearity, nomological validity and significance of the outer weights indicate that strategy implementation capability as a second-order reflective-formative construct is a valid construct that contains five dimensions: Strategy Communication, Incentive and Reward Alignment, Organisational Culture Alignment, Organisational Structure Alignment and Strategy Monitoring and Evaluation.

The results of the quantitative analysis, as shown in Table IV, revealed the positive and significant relationships between strategy implementation capability and its antecedents (managerial skill and managerial social capital). The results also confirmed the mediating roles of strategy implementation capability between key strategy implementation-related resources (managerial skills and managerial social capital) and strategy implementation success. Prior studies confirmed that managerial skills and social capital are positively and significantly related to strategy implementation capability. Managerial skills, especially human and technical skills, enable managers to become more supportive of their employees, and this, in turn, improves the strategy implementation performance through the effective motivation of employees and efficient and effective decision-making (Guoet al., 2013; Mwanje, 2016). Lehtimäki and Karintaus (2013) also underlined the significance of managerial social capital in effectively implementing strategies by improving access to information, trust, and collaboration among individuals. Fig. 2 illustrates the results of the PLS-SEM algorithm for the Strategy Implementation Model.

Paths Path coefficients (β) Std. Dev. t(|O/STDEV|) p
Managerial skills -> Strategy implementation capability 0.258 0.090 2.851 0.004
Managerial skills -> Strategy implementation success 0.341 0.074 4.637 <0.001
Managerial social capital -> Strategy implementation capability 0.541 0.079 6.868 <0.001
Managerial social capital -> Strategy implementation success 0.130 0.081 1.602 0.109
Strategy implementation capability -> Strategy implementation success 0.417 0.068 6.144 <0.001
Managerial skills -> Strategy implementation capability -> Strategy implementation success 0.108 0.042 2.576 0.010
Managerial social capital -> Implementation capability -> Strategy implementation success 0.226 0.053 4.283 <0.001
Table IV. PLS-SEM Algorithm Result for Strategy Implementation Model

Fig. 2. PLS-SEM algorithm results for strategy implementation model.

Hypotheses Coefficient (β) p Hypotheses test results
H1: Managerial skills are positively and significantly related to strategy implementation capability. 0.258 0.004 Supported
H2: Managerial social capital is positively and significantly related to strategy implementation capability. 0.541 <0.001 Supported
H3: Strategy communication is positively and significantly related to strategy implementation success. 0.191 0.003 Supported
H4: Strategy cascading is positively and significantly related to strategy implementation success. 0.102 0.142 Not Supported
H5: Organizational structure alignment is positively and significantly related to strategy implementation success. 0.187 0.014 Supported
H6: Incentives and rewards alignment is positively and significantly related to strategy implementation success. 0.201 0.002 Supported
H7: Organizational culture alignment is positively and significantly related to strategy implementation success. 0.158 0.010 Supported
H8: Strategy monitoring and evaluation is positively and significantly related to strategy implementation success. 0.333 <0.001 Supported
H9: Strategy implementation capability positively and significantly affects strategy implementation success. 0.417 <0.001 Supported
H10: Strategy implementation capability mediates the relationship between managerial skills and strategy implementation success. 0.108 <0.010 Supported
H11: Strategy implementation capability mediates the relationship between managerial social capital and strategy implementation success. 0.226 <0.001 Supported
Table V. Summary of Hypothesis-Testing Results

The other criteria used for assessing the quality of a structural model in PLS-SEM, coefficient of determination (R2), measures the model’s predictive power, and it is one of the widely applicable criteria for evaluating the structural (inner) model (Aliet al., 2017; Hairet al., 2017). In general, R2 values of 0.25, 0.50, and 0.75 for endogenous latent variables are taken as weak, moderate, and strong, respectively (Hairet al., 2017). The R2 value of strategy implementation success in the strategy implementation success model was computed to be 0.55, indicating the model’s moderate predictive power in explaining the variance of the endogenous latent variable by that of exogenous variables. On the other hand, the R2 values of strategy implementation capability and strategy implementation success in the strategy implementation model were 0.56 and 0.64, respectively. The results indicate that the predictive power of the strategy implementation model is acceptable. Thus, based on the quantitative results of the study, the final strategy implementation model was illustrated as shown in Fig. 3.

Fig. 3. The final strategy implementation model.

Conclusion

The major objective of the study was to develop a comprehensive model that augments the effectiveness of strategy implementation in the context of a developing economy. The study established an empirically informed strategy implementation model that contains three major components, namely critical strategy implementation-related resources, key components of strategy implementation capability, and strategy implementation performance. Based on the quantitative data analyses, it was concluded that strategy communication, organizational structure alignment, organizational culture alignment, incentives and rewards alignment, and strategy monitoring and evaluation are the key components of strategy implementation capability that positively and significantly affect strategy implementation success. It was also confirmed that managerial skills and managerial social capital are the two critical strategy implementation-related resources that positively and significantly influence the strategy implementation capability of banks. The study operationalized and validated the strategy implementation capability as a second-order formative-reflective construct that entails five first-order constructs: strategy communication, organizational structure alignment, organizational culture alignment, incentives and rewards alignment, and strategy monitoring and evaluation.

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