A Proposed Model for Successful Transitioning of Startup MSMEs from Planning to Operationalization in Greater Accra Region of Ghana ()
1. Introduction
A start-up business is considered as a temporary business or organization that finds repeatable as well as scalable business framework or model (Abdulsaleh & Worthington, 2013). A start-up as its name implies works towards innovation as well as products development that has high wealth creation potential and of course generation of employment. In fact, start-up commences small with a big vision. However, the business model will have a great impact on the current market. Right from the beginning, founders of start-up have visions in order to develop and grow their enterprises into a large undisruptive organization that establish a new and bigger industry (Baker & Collins, 2010).
For a start-up business to be become a fully grown firm, has to go through several stages such as pre-start stage where a business does not exist (Barzun & Graff, 1990). Start-up embraces business formulation including the kind of business plan and strategies to adopt with regards to using a road map such as a business plan which actually constitutes almost everything about the entrepreneurial business in terms of financial sources as well as a business location, business strategies, etc.
The transitioning phase sees to it that all the necessary checks are carried out in order to ensure that the business has what it takes to fully operate sustain and grow (Beck & Demirguc-Kunt, 2006).
However, operational level of the business is considered as the growing status of the business, which has identified various markets in which the firm would be selling its products. As such, the products are manufactured in accordance with the preference of consumers of all kinds. Nonetheless, expansion of business takes place when the need arises which means when products of the company are needed by consumers at their locations (Harabi & Nor, 2003).
At the methodological stage, several techniques were employed to collect the data, cleaned it and analyzed it. The collected data was analyzed using a software known as Statistical Package for Social Sciences (SPSS). Cronbach Alpha was employed to test for reliability of the data. Structural Equation Model (SEM) was also used to validate the model (Best & Kahn, 1986)
The study would assist MSMEs and managers to know the requirements of Start-up Planning Stage (Independent Variables) such as a business plan, capital, business location, customers, technologies. In the same way, the expected variables at the operationalization stage would also be identified.
The findings would assist MSMESs and MSMEs managers in Ghana to make essential strategic business decisions on transitioning start-up enterprises from planning stage to operationalization (Clover et al., 1984).
To academia, it is envisaged that, the research would continue to be used as a relevant tool or source of reference for researchers in future, providing suggestions for further research. The research would offer more interests among researchers to explore further the significant impact of independent variables on dependent variables (Benzoni et al., 2005).
1.1. Problem Statement
An entrepreneurial business on the ground to be formulated for a start-up must go through several stages (Bird, 1995). These stages are essential which needs to be conducted in order to ensure very good launch of business through to operationalization (Churchill & Lewis, 1983). On the other hand, if the start-up processes are not considered meticulously, the business will not commence on a good road map which may even collapse at an early stage.
Many MSMEs do not graduate to operationalization mode due to input or independent stage not being appropriately organized (Clover et al., 1984). This stage is essential for every start-up business. For an enterprise to operationalize and grow is dependent on the inclusion of the required independent variables pertaining to its business (Joseph et al., 1963). However, business success is achieved well when the independent and dependent factors correlate and communicate horizontally through an application of a model which is the core mandate of this study. This would help to hypothetically explain the actual relationships between start-up planning stage factors and operationalization factors and business outcome (Dennis and Child, 1980).
For the above reasons, this study is to be conducted in order to unravel and identify the various requisite factors for a successful start-up of entrepreneurial businesses through operationalization using a standardized formulated model (Cvijanović et al., 2008).
1.2. Objectives
The objective of this research is to develop and authenticate or validate a model for the successful transitioning of start-up entrepreneurs from planning stage to operationalization.
· To detect or identify and develop a model for the successful transitioning of start-up entrepreneurs from planning stage to operationalization.
· To examine the factors of a model for the successful transitioning of start-up entrepreneurs from planning to operationalization.
· To develop and trial or test the model for the successful transitioning of entrepreneurs from planning stage to operationalization.
· To authenticate or validate a model for the successful transitioning of start-up entrepreneurs from planning stage to operationalization.
2. Literature Review
For a start-up company to transition to Micro Small Medium Enterprises (MSMEs) is very tricky and essential. Transitioning successfully from start-up is only anticipated when all the necessary activities needed to be taken at the start-up stage are all in place (Deming, 1980). Some of the independent variables to be anticipated at the planning stage are not limited to: business plan, specific financial sources & risk, business location & customers, government support, technology, the kind of people to hire HRM skills and marketing skills.
The Operationalization stage
Operationalization of an entrepreneurial business, at its level or stage is considered a structured company or enterprise that follows a well-known and created business framework (Stefanovic, Prokic, & Rankovic, 2010).
These companies work towards achieving and gaining profits by value delivery to their clients and consumers. They function in order to establish a great model and follow stable framework and secure viable financial position in the market place for numerous years at the same time getting funding supports to run their enterprises to grow and develop (Holden et al., 1998).
MSMEs at the operational level is a growing business which has identified markets to sell its products. The products are manufactured according to the preference of consumers of all kinds. However, business expansion takes place when the need arises meaning when produces of the company are needed to meet certain customers in other locations (ILO, 2000).
The enterprises at their positions have all the necessary business opportunities such as funding support from financial institutions, banks, venture capitals as well as friends, donors Non-Governmental Organizations (NGOs) and any other funding arrangement and supports to expand and open other branches in Ghana.
At the operationalization of start-up or entrepreneurial business, numerous business requirements are taken into consideration (Jaramillo & Shiantarelli, 1996). Identified the following dependent variables but not limited to: production, job creation, adequate funding, business expansion, increased number of employees and quality & quality improvement.
Conceptual Framework (Figure 1)
This research discusses the various Start-up planning levels or stages of MSMEs leading to operationalization. Some of the elements identified as the factors of Start-up planning are funds from specific financial sources, business plan, location of business, policy established by government and technology whereas the factors expected at the operationalization are production, creation of jobs, adequate financing or funding, expansion of business and business innovation (Kayanula & Quartey, 2000). The relationship between Start-up planning stage factors (independent variables) and operationalization stage factors (dependent variables) were identified. However, the proposed conceptual model describes the key metrics or factors on which the hypotheses are revolved (White, 1999) as depict.
However, the various metrics or factors under both Start-up planning stage and Operationalization stage were suggested by (Xiao, 2011) as follows:
Start-up planning stage (Independent variables)
· Specific financial source & risk.
· Business plan.
· Business location.
· Business registration & Government policy.
· Technology required.
Operationalization stage (Dependent variables)
· Business production.
· Creation of jobs.
· Adequate funding.
· Expansion of business.
· Innovation & quality.
3. Methodology
Method-1: The elements known or identified via primary and secondary data collection for developing and validating the model were that primary data takes more time and is costly while secondary data suffers data quality at times, outdated and irrelevant (World Economic Forum, 2015).
Method-2: Cronbach Alpha was applied in examining for the reliability of independent and dependent variables with (α > 0.82), and (α > 0.85) respectively, although, the threshold is (α > 0.70). Both independent and dependent variables were examined or tested using correlation analysis. Regression analysis was applied in validating the model.
Method-3: The model development was based on the validation of dependent variables in respect of independent variables.
Method-4: As time elapses, technique for collection of qualitative data requires to adopt some technological base to help cut down the time spent on collection of data.
Research design and approach
The approach and design of research are defined or outlined to provide a preview of the stages and decisions adopted in the data collection progressions or processes (Xiao, 2012).
Population and Sample Size Calculations
The entire population for the entrepreneurs was 295. Slovin’s formula was applied in determining confident level of 96%.
Based on the sample size formula shown below, inputting the figures depicted
where:
S = Sample size;
N =Population size;
e = Margin of Error.
S = 200 was used for the MSMEs.
Data sources
Both primary and secondary sources of data were considered. The primary source of data represents data from respondents through distribution of questionnaires while the secondary source emanates from websites of the firm, internet and archives of the company, etc. The main source of data for this study was qualitatively provided by MSME respondents through answering questionnaires.
Data collection tools
Both qualitative and quantitative techniques were employed in this process. The qualitative technique used questionnaires both closed and opened types to solicit data from respondents. However, the quantitative data such as respondent’s ages, educational levels and the length of years in service were also collected by quantitative techniques.
The designed questionnaires however contained Likert scale measurement as depicted below. This showed an activity level in processes utilized in collecting the data.
· 5 = Strongly agree;
· 4 = Agree;
· 3 = Neutral;
· 2 = Disagree;
· 1 = Strongly Disagree.
A Cronbach Alpha for a reliability test is considered at the threshold of (α > 0.7).
However, the reliability for this test was 0.82 which is higher than the threshold. As such, the qualitative tools utilized in this research were proven valid and reliable since they measured what was intended for and the outcome depicted the reality (Joseph et al., 1963).
Data collection
200 sampled respondents were used to collect the data. Examples of some of data collected were the effectiveness of a model to transition start-up MSMEs from planning to operationalization (Zenith et al., 2000).
Data analysis and validation
The collected data was analyzed using Statistical Package for Social Sciences (SPSS). Structural Equation Model (SEM) was used to validate the model. Cronbach Alpha was also used to check for the reliability of the data to ensure internal steadiness or consistency and constructs for the proposed model designed (Ghosh & Kwan 1996).
4. Results and Analysis
Table 2 shown above represents the age demography, it could be observed that the respondents had diverse age orientations spanning from 28 through 58 years and above. It is particularly noteworthy that majority of the MSME respondents are within their young adulthood years of within 28 to 37 years (representing 93%). This dynamics was also identified by Wagenvoort (2003) who pointed that the youthful nature of persons engaged in MSMEs offers a prospect for successful long-term interventions for MSMEs.
Table 3 above represents the educational status of the respondents. 21% of them could not have any formal education, 33% had their junior high school education followed by 16.5% who had their vocational and technical education as well as 29.5% with first degree but none of them had a second degree.
KMO and Barlett’s test of sphericity
Barlett’s test of sphericity of 0.00, which is (sig < 0.05) and KMO score with
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Table 1. Reliability statistics of scale applied (Cronbach’s alpha).
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Table 2. Summary of demographic characteristics of respondents.
0.856 was the indicator of the suitability of the sample for factor analysis, is depicted in the table below. As clarified that a factor loading score higher than 0.5 for all items stated the satisfactory explanations of the item.
The analysis reconfirmed eight factors with a cumulative total variance explained of 70.4%. It further showed initial eigenvalues more than 1.0. The results showed a different grouping of items, measuring the construct and these new item groupings were further tested with Cronbach Alpha. Three (3) items grouping measured, had Alpha values greater than 0.7 (start-up planning stage-independent variable, NBSSI-control and operationalization stage-dependent variable).
Eigenvalues from the total variance explained was pruned down to fit the criteria (Eigenvalues greater than 1.0 should be extracted), furthermore, to establish the suitability of the measurement of the construct. The varimax rotation test was confirmed by a reliability test (Cronbach Alpha > 0.7,) based on the thirteen constructs. Three constructs with eight statements passed the reliability test with a Cronbach alpha higher than the threshold.
Table 5 shown below represented the reliability measurement of the various variables with threshold of Cronbach Alpha (α > 0.7) as against the test results of
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Table 3. Summary of demographic characteristics of respondents—education.
start-up stage of (0.859 > 0.7), operational stage (0.826 > 0.7) and control (0.683 > 0.7). This means that, all the three variables measured more than the threshold as indicated in the table below
4.1. Confirmatory Factor Analysis (CFA)
In confirmatory factor analysis, numerous statistical tests were used in order to determine how well the model fits the data. However, it is true that a good fit between the constructed model and the data could not mean that the model is “correct”, or even that it explains a large proportion of the covariance (McDonalds & Shirley, 2010).
A “good model fit” only indicates that the model is plausible. When reporting the results of a confirmatory factor analysis, one is urged to report: 1) The proposed models; 2) Any changes made; 3) Which measures identify each latent variable; 4) Correlations between latent variables; 5) Any other pertinent information, such as whether constraints are used. With regard to selecting model fit statistics to report, one should not simply report the statistics that estimate the best fit, though this may be tempting. Though several varying opinions exist, Kushnir (2010), recommend reporting the Chi-squared test, the Root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the standardized root mean square residual (SRMR).
4.2. Goodness of Fit
Model goodness of fit is examined and the indices tabulated in Tablebelow, based on the research objectives and hypothesized model. To reduce the complexity and likelihood of the unwanted interaction effects between large numbers of variables as postulated by (Long, 2009), the study treated the start-up variables as a latent factor. It had several observed variables, the control or NBSSI (three observed variables), and (operationalization also has five observed variables).
Start-up planning factor was a predictor variable (independent variable) predicting MSME (dependent variable) controlled by NBSSI. The hypothesized model generated fit indices of RMSEA = 0.206, CFI = 0.749, TLI = 0.809, and a CD = 0.929. These fit indices fall within the permissible threshold (except for RMSEA).
The results were compared considering the following indicators NFI & TLI > 0.8 permissible; >0.9 good; >0.95 great (Maltzman & Shirley, 2010). CFI > 0.95 if 12 < M < 30; RAMSEA < 0.05 good (Ghosh & Kwan, 1996). Following these indicators, the study examined the relationship between start-up planning factors and operationalization factors when controlled by NBSSI.
However, the table shown below also represents the various goodness of fit for the individual components such as start-up planning factors as well as NBSSI (controller) and operationalization.
With respect to Table 7 above representing goodness of fit for the individual components, it is clear that some of the indexes could not pass all but others pass
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Table 7. Goodness of Fit for the Individual Components.
it. Index such as RMSEA < 0.05 for fit statistics startup planning stage presenting 0.105, control 0.111 and operationalization 0.011 which all could not pass it but index such as CD > 0.8 with fit statistics startup planning stage presenting 0.930, control 0.956 and operationalization 0.911 showing that all passed it.
Taking a critical look at Table 4 above, tucker-lewis index passed two except which did not pass it. Also, in terms of Comparative Fit Index (CFI) one of them was behind the threshold as indicated in Table 4 above.
There were other tests depicted in Table 1 above representing KMO and Bartlett’s test of sample adequacy and Table 5 representing Cronbach Alpha test on reliability provided a very good outcome to this process contributing towards having a good model constructed.
However, testing for goodness of fit does not guarantee a perfect or excellent model. Some statistics can show a good fit but would not present a perfect model. Likewise, others cannot present very good fit statistics but could produce an excellent model. It is essential that goodness of fit is tested or measured since it forms part of the procedures to arrive at constructing a stipulated model. Cut off points for fit index need to be cautioned against its strict reliance because, sometimes can be misleading and subject to misuse (Schuacker, Smith, & Beck, 2012).
The structural equation modelling technique was used to test the theoretical hypotheses model with the maximum likelihood approach. Here, a model was developed for the start-up planning, operationalization and control by a construct. Figure 2 shown below is an illustration of the model. According to Fox (1997), a given variable is said to function as a control to the extent that it accounts for the relation between the predictor and the criterion. Control variables explain how external physical events take on internal psychological significance. Other than control variables, which specify when certain effects hold, speak to how or why such effects occur. Harabi (2005) cited that choice may moderate the impact of incentive on attitude change induced by discrepant action, and this effect is in turn controlled by dissonance arousal reduction sequence. In this study, the start-up planning factors (the predictor variable) are controlled by National Board for Small Scale Industry (NBSSI), on the operationalization (the dependent variable). In other words, the control variables (NBSSI) account for the relationship between start-up planning factors and operationalization factors. The figure shown below represents the hypothesized model
4.3. Structural Equation Model
Ha1: There is a positive significant relationship between Start-up Planning Stage and Control.
Ha2: There is a positive significant relationship between Startup Planning Stage and Operationalization Stage.
Ha3: There is a positive significant relationship between Startup Planning Stage and Operational Stage mediated by Control.
4.4. Hypothesized Model
With regards to the hypothesized relationship between constructs illustrated in
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Figure 3. Structural equation model (path analysis).
the model in Figure 3 shown above. Path analysis was used to test the theoretical hypothesis model with a maximum likelihood approach. Here the study proposed start-up planning and operationalization theories in reviewed literature. The output of the structural equation model reveals that the conceptualized variables including start-up planning and control (β = 0.11, p < 0.000), have a significant positive relationship and supported while positive significant relationship between startup planning stage and operationalization stage (β = 0.04, p < 0.35) not supported. The structural equation model (path analysis) also confirmed the satisfactory goodness of fit (see total goodness of fit table) shown above.
There is a positive significant relationship between startup planning stage and control which presented beta estimation of 0.11 (11%) with a good significant level of 0.02 (α < 0.05) supported as depicted in the above Table 5. However, positive significant relationship between startup planning stage and operational stage presented 0.04 (4%) with a significant level of 0.35 (α < 0.05) which was not even supported though the relationship was just little as shown in the table above. Finally, positive significant relationship between startup planning stage and operational stage mediated by control established 1.07 (107%) with splendid significant level of 0.01 (α < 0.05) and supported. The control stage represented by National Board for Small Scales Industry (NBSSI) is in position to ensure that all the start-up factors are okay and fit for operationalization. The control (NBSSI) stage uses three (3) functions such as pre-transition, transition and post transition for its activities. This helps to ensure a good start-up towards operationalization.
5. Limitations of Research and Suggestion for Future Study
Some degrees of limitations were identified during data collection as follows: Due to the fact that information is sensitive, selected MSMEs were demanding money before releasing the needed primary information. This makes the exercise
very difficult. It is also time consuming. In addition because of the low level educational status of some of the MSMEs, they were assisted by research assistance to complete some part of the questionnaires.
Due to the nature and contributions of this topic, the framework would assist MSMEs in making the right business choices, funding and any other essential factor that is required at the start-up planning stage to operationalization stage. It would also help the business to strive and breakeven.
6. Conclusion
From the findings, few conclusions were drawn in accordance with the objective in question. The study findings were able to unravel the various factors that are undertaken at the start-up planning stage of entrepreneurs to operationalization. Some of these factors were business plan, specific financial sources, adequate funding, site for setting up the business, innovation, quality assurance, etc. Some of funding sources identified by the research at the start-up planning stage were assistance from friends, family and owner’s equity. These sources of funding are less risky and have little or no interest charges at all.
Prior to operationalization of the start-up enterprise, the system goes through transitioning periods such as pre-transitioning as well as transitioning stage and post transitioning stages. All these three stages have some contributions towards successful operationalization of the enterprise.
The findings identified and anticipated at the operationalization side of the business are production, Job creation, adequate funding, business expansion, and increased number of employees, quality & quality improvement, promotion of products and innovation of products.
The findings of the study depicted that a start-up company at the planning stage must work with interest free funding since it is a business on the ground level. Though a start-up level of any business demands a huge sum of capital for the ground works which needs not attract any interest since the business is a start-up one.
The results depicted that, entrepreneurial businesses must develop and work with a well-established business plan which serves as a road map of the business. This, in addition, ensures that the company follows operational principles, trajectory and uses the specific resources spelt in the plan. The findings also showed that, product quality must be good and appealing to enable it to gain appreciation in both local and international markets. The quality must always see innovation when the need arises.
The findings posited that, it is always good to inculcate a transitioning period or stage of a start-up business prior to operationalization where it would be possible to determine if the business is ready for operationalization due to the activities that take place at pre-transitioning as well as transitioning and post-transitioning stages.