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Scholars have extensively examined the role of MCS in SMEs, highlighting their significance in overcoming the inherent resource constraints, environmental uncertainties, and dynamic market conditions characteristic of these enterprises. Studies underscore the diverse array of MCS tools and techniques adopted by SMEs, ranging from traditional budgeting systems to contemporary performance measurement frameworks, and their implications for organizational effectiveness, innovation, and financial performance (Gareng, 2005). Moreover, research has elucidated the role of organizational culture, leadership, and external pressures in shaping the adoption and effectiveness of MCS within SMEs, shedding light on the contextual nuances that influence managerial decision-making processes (Ates, 2013).
Concurrently, investigations into the temporal dimension of organizational experience have unveiled intriguing insights into how years in operation may shape managerial cognition, strategic orientation, and organizational capabilities. Longitudinal studies have demonstrated the evolutionary trajectories of SMEs over time, delineating stages of growth, development, and maturity marked by distinct managerial priorities, practices, and challenges (Singh & Rastogi, 2022, Zor, 2019). Moreover, research has explored the impact of organizational age on factors such as innovation propensity, risk-taking behavior, and market positioning, highlighting the dynamic interplay between experience accumulation and strategic adaptation within SMEs (Moschella, 2022; Wasiq, 2023).
However, despite these advancements, the intersection of organizational experience, MCS utilization, and SME dynamics remains relatively underexplored in the extant literature. Few studies have ventured into understanding how the cumulative experiences garnered over years of operation may influence the depth, breadth, and efficacy of MCS information utilization within SMEs. This lacuna presents a compelling opportunity for scholarly inquiry, offering fertile ground for unraveling the intricate mechanisms through which organizational experience shapes managerial decision-making processes and strategic outcomes within the SME sector.
As we embark on this scholarly endeavor, we aim to contribute to both theoretical advancement and practical insights by elucidating the complex interplay between years in operation, MCS information utilization, and organizational performance within SMEs. Through rigorous empirical inquiry and interdisciplinary dialogue, we endeavor to enrich our understanding of the mechanisms driving managerial decision-making processes and pave the way for informed interventions aimed at enhancing organizational resilience, agility, and competitiveness in today's dynamic business landscape.
LITERATURE REVIEW
Small and medium-sized enterprises (SMEs) constitute a vital segment of the global economy, contributing significantly to employment generation, innovation, and economic growth (Davidsson & Wiklund, 2019). Within this context, the strategic management practices adopted by SMEs play a pivotal role in determining their long-term success and sustainability. Numerous studies have explored the factors influencing strategic decision-making processes within SMEs, with a particular focus on the utilization of Management Control Systems (MCS) information.
Organizational experience, as measured by the number of years in operation, has emerged as a key determinant shaping strategic management practices within SMEs. Previous research suggests that longer-established SMEs tend to exhibit more sophisticated management control systems, leveraging their accumulated knowledge and experience to inform strategic decision-making processes (Chenhall & Langfield-Smith, 1998). However, the precise nature of the relationship between organizational experience and the utilization of MCS information remains subject to debate, necessitating further empirical investigation.
The integration of MCS information within SMEs encompasses various domains of business management, including planning and control, decision support, budgeting, cost accounting and pricing, and strategic planning (Ittner, 2003). While existing literature has examined the impact of MCS on organizational performance and decision-making effectiveness, few studies have specifically explored the influence of organizational experience on the utilization of MCS information across these constructs within SMEs (Hronec & Fleisher, 2017).
Studies examining the relationship between organizational experience and MCS utilization have yielded mixed results. While some research indicates a positive correlation between organizational longevity and the sophistication of MCS information systems (Lara et al., 2017), others suggest that newer SMEs may adopt more innovative and adaptive approaches to strategic management practices, leveraging technological advancements and market disruptions to their advantage (Lyon, 2015).
In conclusion, the literature highlights the complex interplay between organizational experience and the utilization of MCS information within SMEs. While longer-established SMEs may possess greater resources and accumulated knowledge, newer entrants may exhibit greater agility and innovation in their strategic management practices. This study aims to contribute to this body of literature by empirically examining the relationship between organizational experience and MCS utilization across key constructs within SMEs, providing valuable insights for researchers, practitioners, and policymakers alike.
METHODOLOGY
This research employed a descriptive correlational design to investigate the relationship between the number of years in business and the utilization of Management Control Systems (MCS) information for business management support across five key constructs: planning and control, decision support, budgets, cost accounting and pricing, and strategic planning.
Sample Selection
The study was done among the SMEs of Misamis Occidental. The respondents came from companies that are more than 10 years in the industry. This would provide credence to the agency being more formally managed. This criterion is following the same criteria of previous research studies that consider the firms that are not more than 10 years old as young (Beckman, Certo, Covin & Slevin, Yli-Renko, Durendez & Garcia, 2008). A purposive sampling approach was utilized to select SMEs meeting the inclusion criteria. A total of 46 SMEs was selected from diverse industries and geographical locations to ensure representation across various sectors.
Data Collection
Data were collected through structured surveys administered electronically or in-person, depending on the preferences of the respondents. The survey instrument was designed to capture information related to the utilization of MCS information for business management support across the specified constructs. Respondents were asked to rate their agreement with statements using a Likert scale, ranging from 0 (Not Used) to 4 (Systematically Used).
Measures
The questionnaire used in the study is an adaptation from the study of Jankala, (2007) on MCS in the Small Business Context. The questionnaire was developed out of the qualitative study done by Jankala, (2007) on the adoption of MCS among small businesses in Finland. The questionnaire was tested in her study with the following reliability test results for each of the categories. The overall Cronbach alpha for the questionnaire is 0.88. The survey instrument consisted of items representing each of the five constructs: planning and control, decision support, budgets, cost accounting and pricing, and strategic planning.
The accuracy of measures of use of MCS information supporting the business management was (re-evaluated) measured using principle component analysis via the orthogonal rotation with the varimax method. The analysis reveals a Kaiser Meyer Olkin measure of sampling adequacy value of 0.86 which is in the approved range from 0.8 to 1.0. This indicates that the data collected is sufficient for factor analysis. The number of constructs is fixed to five that is retained from the original questionnaire. These constructs are (1) planning and control during a financial year, (2) decision support, (3) budgets, (4) cost accounting and pricing and (5) strategic planning and management. The item/s with a correlation value of less than 0.5 with respect to the construct in which they are loaded, and items that load to the same construct, are deleted since this may affect the validity of the scale. The re-assignment of the retained items to their appropriate construct is summarized in Table 1 (rotated component matrix). The cumulative variance of the five constructs in scale C is 71.22 % which is greater than 70%. This implies that the five constructs identified possessed a satisfactory validity.
Table 1. Rotated Component Matrixa.
|
Components |
||||
A |
B |
C |
D |
E |
|
CON1C |
|
|
|
|
.622 |
CON2C |
|
|
|
|
.662 |
CON3C |
|
|
|
|
|
CON4C |
|
|
|
.728 |
|
CON5C |
|
|
|
.715 |
|
CON6C |
|
|
|
|
|
CON7C |
|
.741 |
|
|
|
CON8C |
|
.560 |
|
|
|
CON9C |
|
|
|
|
.685 |
CON10C |
|
|
|
|
|
CON11C |
.509 |
|
|
|
|
CON12C |
|
.533 |
|
|
|
CON13C |
|
|
|
|
|
CON14C |
|
.542 |
|
|
|
CON15C |
|
|
.798 |
|
|
CON16C |
|
|
.725 |
|
|
CON17C |
|
|
.822 |
|
|
CON18C |
|
|
.782 |
|
|
CON19C |
|
|
.775 |
|
|
CON20C |
|
|
.646 |
|
|
CON21C |
|
|
|
|
.569 |
CON22C |
|
|
.578 |
|
|
CON23C |
|
|
|
|
.526 |
CON24C |
.586 |
|
|
|
|
CON25C |
|
.690 |
|
|
|
CON26C |
|
.777 |
|
|
|
CON27C |
|
.584 |
|
.529 |
|
CON28C |
|
.815 |
|
|
|
CON29C |
|
.703 |
|
|
|
CON30C |
|
.730 |
|
|
|
CON31C |
|
.787 |
|
|
|
CON32C |
|
|
|
.701 |
|
CON33C |
.561 |
|
|
.542 |
|
CON34C |
.511 |
|
|
|
.526 |
CON35C |
.816 |
|
|
|
|
CON36C |
.748 |
|
|
|
|
CON37C |
.797 |
|
|
|
|
CON38C |
.775 |
|
|
|
|
CON39C |
.794 |
|
|
|
|
CON40C |
.686 |
|
|
|
|
CON41C |
.567 |
|
|
|
|
CON42C |
|
|
|
.621 |
|
CON43C |
|
|
|
.658 |
|
CON44C |
|
|
.569 |
|
|
CON45C |
.543 |
|
.654 |
|
|
CON46C |
.675 |
|
|
|
|
CON47C |
.732 |
|
|
|
|
CON48C |
.756 |
|
|
|
|
CON49C |
.581 |
|
.622 |
|
|
CON50C |
.674 |
|
.530 |
|
|
CON51C |
.561 |
|
|
|
|
CON52C |
.702 |
|
|
|
|
CON53C |
.566 |
|
|
|
|
CON54C |
.706 |
|
|
|
|
CON55C |
.693 |
|
|
|
|
CON56C |
|
|
.555 |
|
|
Data Analysis
Descriptive statistics, including means, standard deviations, frequencies, and percentages, were computed to summarize the demographic characteristics of the sample and the responses to survey items. Descriptive statistics for the 56 items in the questionnaire are calculated based on the scores on the scale from not used at all (scored 0) to used systematically as a part of normal routines (scored 4). The items are divided into three groups: relatively high use, relatively moderate use and relatively low use. This grouping is drawn from Chenhall and Langfield-Smith (1998). However, as in the context of adoption, this classification is only an approximation and does not represent any absolute usage figures.
Relatively High Use = mean of above 2.50
Relative Moderate Use = mean equal or above 2.0
Relative Low Use = mean below 2.0
Correlation analysis, specifically Pearson's correlation coefficient, was employed to examine the strength and direction of the relationship between the number of years in business and the utilization of MCS information for business management support across the five constructs.
Ethical Considerations
Ethical approval was obtained from the relevant institutional review board prior to data collection. Informed consent was obtained from all participants, and measures were taken to ensure confidentiality and anonymity throughout the research process.
LIMITATIONS
Despite efforts to obtain a representative sample, the findings of this study may be subject to certain limitations, including sampling bias and self-reporting biases. Additionally, the cross-sectional nature of the study limits the ability to establish causality between variables. Future research could employ longitudinal designs to further explore the dynamics of MCS utilization over time within SMEs.
Overall, this methodology provided a systematic approach to investigate the relationship between organizational experience, as measured by the number of years in business, and the utilization of MCS information for business management support across key constructs, thereby contributing to a deeper understanding of the strategic management practices within SMEs.
RESULTS AND DISCUSSION
In this section, we present the results of our investigation into the relationship between the number of years in business and the utilization of Management Control Systems (MCS) information for business management support among small and medium-sized enterprises (SMEs). Through descriptive analysis, we summarize the demographic characteristics of the sample and provide an overview of respondents' perceptions regarding the five key constructs: planning and control, decision support, budgets, cost accounting and pricing, and strategic planning. Subsequently, we delve into the findings of the correlation analysis, examining the strength and direction of the relationship between organizational experience and MCS utilization across these constructs. Through a comprehensive discussion, we interpret the implications of our results, highlighting the nuances of MCS utilization within SMEs and offering insights into the strategic management practices that emerge over time in response to organizational experience. Additionally, we address the limitations of our study and suggest avenues for future research to further elucidate the dynamics of MCS utilization within the SME sector.
Table 2 presents the demographic profile of respondents, delineating the distribution of SMEs based on the number of years in operation. The data reveal a diverse range of organizational experiences, with the majority of respondents (59%) falling within the 10 to 16 years bracket, indicating a prevalence of relatively younger enterprises in the sample. Conversely, a smaller proportion of respondents are situated in the higher years’ brackets, with only 9% representing SMEs operating for 17 to 23 years, and a mere 4% for 31 to 37 years. Interestingly, the distribution demonstrates a downward trend in representation as the number of years in operation increases, with a notable decline in respondents beyond the 23-year threshold. This disparity suggests a potential skew towards newer entrants within the SME landscape, possibly reflecting the dynamic nature of the sector characterized by high turnover rates and entrepreneurial ventures (Qalati, 2022; Xie, 2023). Conversely, the relatively fewer respondents in the higher years’ brackets may indicate a survival bias, with more established enterprises demonstrating resilience and longevity amidst competitive pressures (Muriithi, 2017; Addae-Korankye & Aryee, 2021). These findings underscore the importance of considering organizational experience as a determinant of strategic management practices, prompting further exploration into the implications of varying tenure on the utilization of Management Control Systems information within SMEs.
Table 2. Demographic Profile of Respondents.
Years |
Number |
Percentage |
10 to 16 |
27 |
59% |
17 to 23 |
4 |
9% |
24 to 30 |
8 |
17% |
31 to 37 |
2 |
4% |
38 to 45 |
5 |
11% |
Total |
46 |
100% |
The provided table delineates the utilization of Management Control Systems (MCS) information across various domains of business management practices within small and medium-sized enterprises (SMEs) (Quinco-Cadosales, M.N. and Famacion-Quinco, D. 2022). Sub-group A focuses on planning and control during a financial year, revealing mean scores ranging from 1.78 to 2.65. Constructs such as "Budgets" (2.65) and "Product and/or service profitability analysis" (2.45) exhibit relatively high mean scores, indicating a significant utilization of MCS information in these areas. Conversely, constructs like "Comparisons of financial ratios to industry averages and competitors’ ratios" (1.63) and "Monthly or quarterly income statements including determination of depreciation and change in stock" (1.78) demonstrate lower mean scores, suggesting a comparatively lower level of integration of MCS information. This implies that the emphasis on utilizing Management Control Systems (MCS) information in areas such as budgeting and product/service profitability analysis suggests that SMEs prioritize allocating resources and optimizing their core operations to enhance profitability and performance (El Deeb, 2012) and the comparatively lower integration of MCS information in tasks like comparing financial ratios and including depreciation in income statements poses potential gaps in decision-making processes related to financial analysis and resource allocation (Sunday, 2011).
In Sub-group B, which examines decision support constructs, mean scores range from 1.23 to 2.53. "Weekly forecasts or budgets for sources and uses of cash" (2.53) stands out with a relatively high mean score, indicating a significant utilization of MCS information for cash flow forecasting. The overall mean for decision support constructs is 1.90, suggesting a moderate level of integration of MCS information in decision-making processes within the sample of SMEs. The finding that SMEs exhibit a significant utilization of Management Control Systems (MCS) information for cash flow forecasting implies a proactive approach to managing liquidity and financial stability. By prioritizing weekly forecasts or budgets for sources and uses of cash, SMEs can better anticipate and mitigate potential cash flow challenges, thereby enhancing their resilience to financial uncertainties (Afrifa & Tingbani, 2017). However, the moderate level of integration of MCS information in decision support constructs suggests room for improvement in leveraging MCS for broader decision-making processes beyond cash flow management. Addressing this gap could empower SMEs to make more informed and strategic decisions across various facets of their operations, ultimately fostering greater efficiency, agility, and competitiveness in the marketplace (Ahmad & Zabri, 2016).
Sub-group C delves into budgeting practices, with mean scores ranging from 2.55 to 2.95. Constructs such as "Cash flow statement for the financial year" (2.95) and "Monthly purchasing forecasts or budgets" (2.88) exhibit relatively high mean scores, indicating a significant utilization of MCS information in these aspects of budgeting. The overall mean for budgeting constructs is 2.75, indicating a relatively high level of integration of MCS information in budgeting practices within the surveyed SMEs. The results show that the items highly used pertain to traditional financial management practices. Cash flow, purchases and sales budgets are among the top in the high usage and indicates that the appreciation of traditional MCS is greater than for sophisticated MCS. This result is in conformity with the result of Ahmad & Zabri, (2016). Davila & Foster, (2005) also concluded that budgets, cashflows and sales projections are the first management control instruments implemented in a business.
Sub-group D explores cost accounting and pricing constructs, revealing mean scores ranging from 1.85 to 2.83. The finding that SMEs demonstrate a significant utilization of Management Control Systems (MCS) information in pricing strategies, particularly with "Pricing based on full-costing approach" (2.83), underscores the importance of adopting comprehensive costing methodologies in decision-making processes. By leveraging MCS information in pricing strategies, SMEs can ensure that pricing decisions are grounded in a thorough understanding of costs, thereby enhancing profitability and competitiveness in the marketplace (Bragg, 2012). However, the moderate level of integration of MCS information in cost accounting and pricing constructs suggests potential opportunities for SMEs to further enhance their cost management practices. By deepening their utilization of MCS information in cost accounting and pricing, SMEs can refine their cost structures, optimize pricing strategies, and improve overall financial performance (Wijewardena,2004).
Lastly, Sub-group E examines strategic planning and management constructs, with mean scores ranging from 1.48 to 2.88. The noteworthy utilization of Management Control Systems (MCS) information in "Personnel analysis" (2.88) and "Customer analysis" (2.78) within SMEs underscores the critical role of data-driven insights in informing strategic planning decisions (Alsem, 2019). By leveraging MCS information in personnel and customer analyses, SMEs can gain valuable insights into workforce performance, customer behavior, and market trends, enabling more informed and targeted strategic initiatives (Biswas & Akroyd, 2022). However, the moderate level of integration of MCS information in strategic planning and management constructs suggests potential areas for further enhancement in leveraging MCS for strategic decision-making. Deepening the utilization of MCS information in strategic planning can empower SMEs to develop more robust growth strategies, enhance customer engagement, and optimize resource allocation, thereby driving sustainable competitive advantage and long-term success (Singh, 2008). This implies that SMEs should prioritize strengthening the integration of MCS information across all facets of strategic planning, ensuring alignment with organizational goals and market dynamics. By doing so, SMEs can unlock new opportunities for growth, innovation, and market leadership in an increasingly dynamic business landscape.
The summary of mean results across the five constructs reveals intriguing insights into the utilization of Management Control Systems (MCS) information in SMEs' business management support practices. Notably, constructs such as "Budgets" and "Cost accounting and pricing" exhibit relatively high mean scores, suggesting a robust integration of MCS information in these areas. This indicates that SMEs prioritize budgeting, cost analysis, and pricing strategies, recognizing their importance in driving financial performance and strategic decision-making (Chenhall, 2003, Cinquini & Tennuci, 2010). Conversely, constructs like "Decision Support" and "Strategic planning and management" demonstrate comparatively lower mean scores, highlighting potential areas for improvement in leveraging MCS information for broader decision-making processes and long-term strategic planning (Cescon, 2019). This implies that while SMEs excel in certain aspects of business management support, there is a need for a more holistic approach to integrating MCS information across all facets of strategic management. By addressing these gaps and deepening their utilization of MCS information, SMEs can enhance their decision-making capabilities, foster innovation, and drive sustainable growth, ultimately positioning themselves for success in an increasingly competitive marketplace (Table 3).
Table 3. Utilization of Management Control Systems Information across Business Management Practices in Small and Medium-sized Enterprises.
Business Management Support Constructs |
N |
Mean |
SD |
A. Planning and control during a financial year |
|
|
|
1. Monthly or quarterly income statements excluding determination of deprecation and change in stock |
46 |
1.88 |
1.34 |
2. Monthly or quarterly income statements including determination of depreciation and change in stock |
46 |
1.78 |
1.29 |
3. Analysis of working capital and its parts (stocks, debtors, creditors) including use of ratios |
46 |
1.78 |
1.29 |
4. Fund flow statements of the financial year describing sources and uses of earnings and capital |
46 |
2.10 |
1.13 |
5. Budgets (annual, flexible, or rolling) |
46 |
2.65 |
1.29 |
6. Budget follow-ups, at least quarterly, and variance analysis |
46 |
2.30 |
1.24 |
7. Use of financial ratios in analysis of profitability, leverage and liquidity |
46 |
1.85 |
1.31 |
8. Comparisons if financial ratios to industry averages and competitors’ ratios |
46 |
1.63 |
1.33 |
9. Efficiency of analysis of production and operations (levels of action, lead times, labor hours, delivery, etc.) |
46 |
2.10 |
1.37 |
10. Product and/or service profitability analysis |
46 |
2.45 |
1.28 |
11. Customer profitability analysis |
46 |
2.50 |
1.30 |
12. Calculations and analysis of financial risks |
46 |
2.38 |
1.27 |
13. Project follow-ups and reports |
46 |
2.15 |
1.29 |
14. Quality improvement analysis |
46 |
2.33 |
1.14 |
Overall Mean |
|
2.13 |
|
B. Decision Support |
|
|
|
15. Monthly or quarterly income statements excluding determination of deprecation and change in stock |
46 |
1.60 |
1.17 |
16. Monthly or quarterly income statements including determination of depreciation and change in stock |
46 |
1.63 |
1.29 |
17. Analysis of working capital and its parts (stocks, debtors, creditors) including use of ratios |
46 |
1.23 |
1.10 |
18. Fund flow statements of the financial year describing sources and uses of earnings and capital |
46 |
1.83 |
1.24 |
19. Budgets (annual, flexible, or rolling) |
46 |
2.03 |
1.33 |
20. Budget follow-ups, at least quarterly, and variance analysis |
46 |
2.15 |
1.23 |
21. Analysis of buy-or-make/produce alternatives |
46 |
1.85 |
1.33 |
22. Reports relating to the alternatives for production/operation processes |
46 |
1.75 |
1.19 |
23. Market surveys and other marketing reports alike |
46 |
1.95 |
1.38 |
24. Estimates and plans for the number of employees |
46 |
2.38 |
1.23 |
25. Weekly forecasts or budgets for sources and uses of cash |
46 |
2.53 |
1.38 |
Overall Mean |
|
1.90 |
|
C. Budgets |
|
|
|
26. Monthly or quarterly budgets of cash flows |
46 |
2.70 |
1.38 |
27. Cash flow statement for the financial year |
46 |
2.95 |
1.30 |
28. Monthly sales forecasts or budgets |
46 |
2.60 |
1.26 |
29. Annual sales forecasts or budgets |
46 |
2.55 |
1.26 |
30. Monthly purchasing forecasts or budgets |
46 |
2.88 |
1.09 |
31. Annual purchasing forecasts or budgets |
46 |
2.83 |
1.13 |
Overall Mean |
|
2.75 |
|
D. Cost accounting and pricing |
|
|
|
32. Calculations of product or service-level costs |
46 |
2.48 |
1.38 |
33. Calculations for cost centers |
46 |
2.55 |
1.34 |
34. Calculations based on target costing (price and target profit are known, so planning is used for reaching allowed producing costs) |
46 |
2.30 |
1.36 |
35. Product life-cycle analysis (all costs from product development to the end of production and exit from markets) |
46 |
2.23 |
1.29 |
36. Calculations based on activity-based costing (for example, selling, purchasing, delivering etc.) |
46 |
2.50 |
1.40 |
37. Calculations of customer costs |
46 |
2.35 |
1.21 |
38. Calculations of project costs |
46 |
2.13 |
1.36 |
39. Calculations of quality costs (for example failures and their prevention) |
46 |
2.20 |
1.47 |
40. Calculations of environmental costs |
46 |
1.85 |
1.39 |
41. Pricing based on variable costing and contribution margin |
46 |
2.73 |
1.30 |
42. Pricing based on full-costing approach |
46 |
2.83 |
1.26 |
Overall Mean |
|
2.38 |
|
E. Strategic planning and management |
|
|
|
43. Long-run budgets (for example, including 2-5 years) |
46 |
1.95 |
1.22 |
44. Benchmarking reports and analysis (for example comparisons to a respective top-firm for learning purposes) |
46 |
1.68 |
1.29 |
45. Market share analysis and forecasts |
46 |
1.78 |
1.23 |
46. Competitor analysis and forecasts |
46 |
1.98 |
1.12 |
47. Customer analysis (satisfaction, behavior etc.) |
46 |
2.78 |
1.27 |
48. Analysis and forecasts of customer’s value added |
46 |
2.58 |
1.20 |
49. Business partner analysis and reports |
46 |
1.80 |
1.40 |
50. Value chain analysis |
46 |
2.00 |
1.32 |
51. Personnel analysis (performance, satisfaction etc.) |
46 |
2.88 |
1.14 |
52. Analysis and scenarios for the development of external business environment |
46 |
2.33 |
1.25 |
53. Analysis of business strengths and weaknesses, etc. |
46 |
2.70 |
1.14 |
54. Reports and analysis of innovation and development |
46 |
2.38 |
1.17 |
55. Analysis and scenarios for alternative strategies |
46 |
2.33 |
1.19 |
56. Shareholder value analysis/EVA |
46 |
1.48 |
1.20 |
Overall Mean |
|
2.19 |
|
Next, we present the findings of our investigation into the correlation between organizational longevity, as measured by the number of years in operation, and the utilization of MCS information across five pivotal constructs: planning and control, decision support, budgets, cost accounting and pricing, and strategic planning and management. Through a systematic analysis of these correlations, we aim to uncover the nuanced relationship between organizational experience and the integration of MCS information within SMEs. This examination offers valuable insights into how the trajectory of organizational growth shapes managerial practices, informs strategic decision-making, and ultimately influences the performance and sustainability of SMEs in today's dynamic business environment.
Pearson’s correlation and multivariate regression analysis was performed to verify the relationship between the number of years in business and Scale C which is measured by five constructs i.e. planning and control, decision support, budgets, cost accounting and pricing, and strategic planning.
Table 4 the model fitting information suggest that there is a significant improvement of the regression model to predict if the expansion variables are included in the model compared to intercept model only (no variables model. a p- value of 0.046 means that we come up with a regression model which is better than model 0 (intercept model only) and can significantly explain a variation in the dependent variable.
Table 4. ANOVAa.
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
Regression |
399.238 |
5 |
79.848 |
.784 |
.046b |
Residual |
4073.197 |
40 |
101.830 |
|
|
Total |
4472.435 |
45 |
|
|
|
The analysis highlights the relationship between the number of years in operation and five key constructs: planning and control, decision support, budgets, cost accounting and pricing, and strategic planning. Table 4 elucidates that four out of these five constructs exhibit a significant relationship with the dependent variable, which suggests that organizational experience, as measured by the number of years in operation, has a notable impact on these aspects of business management within SMEs.
Among the examined constructs, planning and control (CONS A SUM) emerges as the most strongly related factor, evidenced by its highest beta value of 0.479. This robust relationship suggests that variations in the number of years in operation exert a considerable impact on the planning and control mechanisms adopted by SMEs. Furthermore, the significant relationships observed with budgeting (CONS SUM C), cost accounting and pricing (CONS SUM D), and strategic planning (CONS SUM E) further accentuate the profound influence of organizational experience on strategic management practices, financial planning processes, and the formulation of long-term strategic objectives within SMEs. These findings offer valuable insights into the intricate dynamics at play within SMEs, highlighting the pivotal role of experience in shaping decision-making processes and strategic orientation. Such nuanced understanding is essential for both academic discourse and practical applications, offering researchers and practitioners alike a deeper appreciation of the factors driving SME success and resilience in today's dynamic business environment.
However, the analysis reveals that decision support (CONS SUM B) does not exhibit a significant relationship with the improvement of the regression model (p=0.712). This suggests that, unlike other constructs, decision support may not significantly contribute to the variation in the dependent variable. While decision support remains an integral aspect of managerial decision-making processes, its impact on organizational outcomes within SMEs may be less pronounced compared to other factors examined in this analysis (Table 5).
Table 5. Coefficientsa.
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
|
B |
Std. Error |
Beta |
|||
(Constant) |
17.792 |
4.656 |
|
3.821 |
.000 |
CONS A SUM |
.223 |
.119 |
.479 |
1.882 |
.037 |
CONS B SUM |
.154 |
.223 |
.052 |
.240 |
.712 |
CONS C SUM |
.337 |
.307 |
.283 |
1.098 |
.027 |
CONS D SUM |
.540 |
.509 |
.222 |
1.060 |
.029 |
CONS E SUM |
.161 |
.448 |
.180 |
.359 |
.042 |
- Dependent Variable: year
CONCLUSION AND RECOMMENDATION
In conclusion, our analysis sheds light on the intricate relationship between organizational experience, as measured by the number of years in operation, and key constructs of business management within small and medium-sized enterprises (SMEs). The findings reveal that while four out of the five identified constructs-planning and control, budgets, cost accounting and pricing, and strategic planning-exhibit significant relationships with the dependent variable, decision support does not significantly contribute to the variation in organizational outcomes.
These results underscore the significance of organizational experience in shaping strategic management practices and financial decision-making processes within SMEs. Planning and control emerge as particularly influential factors, highlighting the importance of effective planning mechanisms in driving organizational success and resilience. Moreover, the significant relationships observed in budgeting, cost accounting and pricing, and strategic planning emphasize the multifaceted impact of organizational experience on various facets of business management.
While the findings of this study provide valuable insights into the strategic management practices of SMEs, it is important to acknowledge certain limitations. Firstly, the study's sample size and scope may limit the generalization of findings to a broader population of SMEs. Additionally, the use of cross-sectional data may restrict the ability to draw causal inferences about the observed relationships. Future research endeavors could address these limitations by employing longitudinal studies with larger and more diverse samples, allowing for a more robust examination of the dynamics between organizational experience and business management practices within SMEs.
Furthermore, while decision support did not exhibit a significant relationship in this analysis, it remains an integral aspect of managerial decision-making processes. Future research could explore the specific factors that influence the effectiveness of decision support mechanisms within SMEs, providing valuable insights into avenues for improvement in this area.
Overall, despite these limitations, the findings of this study contribute to a deeper understanding of the strategic management practices adopted by SMEs and offer valuable implications for enhancing organizational performance and sustainability in today's dynamic business environment.
ACKNOWLEDGEMENT
We extend our heartfelt gratitude to the small and medium-sized enterprise (SME) respondents whose invaluable contributions enriched our research. Their willingness to share insights and experiences was pivotal in shaping our understanding of management control systems in SMEs. We also acknowledge the Department of Trade and Industry (DTI) for their support and collaboration throughout the duration of this study. Additionally, we express our appreciation to La Salle University for providing the necessary resources and academic environment conducive to conducting this research. Their support has been instrumental in the successful completion of this project, and we are deeply grateful for their assistance.
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