The impact of risks on small and medium enterprises in the region of Sebha- Libya 2019-2010

Small and medium enterprises (SMEs) are foundational for economic and social development in developing countries but face multiple risks—economic, financial, political, market competition, and others—of which financial risks are most critical. The study focuses on SMEs in Sebha, Libya, examining the impact of financial risk management on their success. Research problem: SMEs face financial risks that limit competitiveness, growth, continuity, and expansion; key financial risks identified include weak liquidity, poor treasury management, weak financial planning, and the risk of non-repayment of loans and debts. Research question: What is the effect of managing these risks on the success of SMEs in Sebha, Libya? Hypotheses: (1) SMEs in Libya face multiple financial risks that limit growth and development. (2) Financial risk management strategies adopted by SMEs in Libya help confront the risks they face and lead to project success in achieving objectives. Objectives: define SMEs and criteria; assess the effect of financial risk management on success; identify and evaluate the strategies SMEs use to face risks. Importance: scientifically, it advances understanding of financial risk management in SMEs; practically, it informs SMEs and policymakers in Libya. Study limits: subject—SME risks per study criteria; space—Sebha region; time—primarily 2010–2017 (broader discussion 2010–2019).

The theoretical framework defines risk as a pervasive condition of uncertainty affecting decision-making, differentiating between risk, hazard, and gambles, and outlines techniques to deal with risk: retention, transfer (e.g., insurance), reduction, and avoidance. Types of risk include systematic (market-wide) and unsystematic (firm- or industry-specific), with sources such as managerial errors and industry risks; credit risk in trade credit is highlighted for SMEs. Risk measurement approaches include statistical methods, operations research (decision trees, sensitivity analysis), and portfolio diversification principles (diversifying investment areas, balanced weights, minimizing correlation among investments). Strategies to reduce risk involve insurance, long-term arrangements, information development, and market opportunity exploitation. A comprehensive review compares SME definitions across jurisdictions: USA (sales and employment thresholds), EU (micro, small, medium by employees and turnover/budget; independence criterion), UK (Bolton Committee—owner-managed, small market share, independence; employment thresholds), Japan (capital and employment by sector), various other countries and international organizations (ILO, IBRD, UNIDO, EEC), and Arab countries. Libya’s definition: small ≤25 employees, capital ≤2.5 million LYD; medium ≤50 employees, capital ≤5 million LYD. The review also covers the history and financing of SMEs in Libya, common obstacles (legal frameworks, formalization, managerial capacity, skilled labor shortages, access to inputs and sites, limited support for innovation, marketing problems), and impediments to SME development (weak entrepreneurship culture, legal and registration issues, taxation/customs, limited institutional finance and capital markets, poor information systems, skill gaps, infrastructure deficits).

Design: Historical descriptive and quantitative methodologies. Data collection: Structured questionnaire developed from prior studies, with two sections—respondent/project demographics and core study constructs on financial risks, risk management strategies, SME capacity to grow, and success. Population: SMEs in Sebha per Ministry of Industry records (noted as 240 projects in accessible areas; another section references 420 projects). Sampling: Simple random sample using Steven Thompson’s formula (Z=1.96, d=0.05, p=0.5). 120 questionnaires distributed; 110 returned (response rate 91.6%). Sample characteristics: 87.3% male; age ≥40 years 55.4%; employees <10 in 63.4% of firms, 10–<50 in 22.7%; sectors—service 49.1%, industrial 31.8%, financial/banking 11.8%, agricultural 7.3%. Instrument reliability and validity: Content validity via 4 expert reviewers with revisions; internal consistency via Cronbach’s alpha—financial risks (62.12%), SME capacity to grow (69.4%), risk management strategies (67.7%), success (61.7%), overall (66%); reported validity coefficients around 81.6%. Measurement: 5-point Likert scale (1=strongly disagree to 5=strongly agree), with interpretive bands. Analysis: SPSS used for descriptive statistics (frequencies, percentages, means, modes, standard deviations), Cronbach’s alpha, Spearman correlations, and simple linear regression for hypothesis testing.

- Financial risks ranking (Table 7, n=110): • Weak liquidity: 63.6% rated “most dangerous,” 23.6% “dangerous,” 7.3% “less dangerous,” 4.5% “weak danger,” 0.9% “no danger.” • Poor treasury management: 24.5% most dangerous, 25.5% dangerous, 26.4% less dangerous, 16.4% weak danger, 7.3% no danger. • Weak financial planning: 14.5% most dangerous, 36.4% dangerous, 34.5% less dangerous, 5.5% weak danger, 9.1% no danger. • Risk of non-repayment of loans/debts: 14.5% most dangerous, 30.0% dangerous, 30.9% less dangerous, 17.3% weak danger, 7.3% no danger. - Descriptive statistics (means/modes/SDs) for risk items (Table 8): liquidity mean 1.55 (mode 1, SD 0.884); treasury management mean 2.56 (mode 3, SD 1.231); financial planning mean 2.58 (mode 2, SD 1.095); non-repayment mean 2.73 (mode 3, SD 1.133). - Impact on capacity to grow (Table 9–10): agreement that dealing with financial risks presents obstacles to growth—means 3.44 (mode 4, SD 1.154) and 3.75 (mode 4, SD 1.145). - Financial risk management strategies (Table 11–12): generally moderate agreement that organizations consider risks in strategy (mean 3.65), can anticipate signals (3.82), and adopt plans (3.43–3.48); lower adoption of quantitative methods (mean 2.98) and varied methods (2.92); training perceived necessary (mean 3.98), communication highly valued during risk handling (mean 4.05); received training moderate (mean 3.05). - Success indicators (Table 13–14): most respondents disagreed that having branches indicates success (mean 1.61), and expressed low satisfaction (mean 1.82); mixed views on sales meeting targets (mean 3.45) and whether SMEs achieve objectives (mean 2.42); cooperation with other projects as success indicator low (mean 2.25). - Hypothesis tests: • H1 (financial risks vs capacity to grow): Spearman R=0.037; R²=0.001; F=0.079; p=0.781; slope B=-0.045—non-significant relation. • H2 (risk management strategies vs success): Spearman R=0.165; R²=0.027; F=1.618; p=0.209; slope B=-0.590—non-significant relation. - Overall: Liquidity shortages and loan repayment risk are foremost financial risks; SMEs face obstacles in applying suitable strategies; descriptive evidence supports that financial risks affect performance, though regressions found no statistically significant linear relationships at 5% level.

Findings confirm that SMEs in Sebha face multiple financial risks, most notably liquidity constraints and loan repayment challenges, which hinder operational performance and perceived capacity to grow. Respondents generally acknowledge the importance of integrating risk considerations into strategic planning and value communication and training, yet adoption of quantitative risk detection methods is limited. Despite descriptive agreement that risk management is practiced and that risks obstruct growth, the simple linear regressions did not yield statistically significant relationships between financial risks and growth capacity nor between risk management strategies and success, suggesting that the relationships may be more complex (nonlinear, mediated, or confounded by contextual factors such as market, regulatory, and security conditions). Success measures indicate modest outcomes (few branches, low satisfaction, weak inter-project cooperation), aligning with the presence of significant financial and managerial constraints. The study addresses the research question by documenting the prevalence and perceived impact of financial risks and the mixed, often insufficient application of risk management strategies in SMEs, highlighting areas for strengthening to improve growth and success.

The study contributes by profiling the financial risk landscape of SMEs in Sebha, Libya, and assessing the perceived role of financial risk management in their success. It identifies liquidity shortages and loan repayment risks as the most critical financial threats and reveals obstacles in implementing risk management strategies, limited use of quantitative methods, and modest success indicators. Recommendations include: conducting rigorous environmental analyses of financial risks; comprehensive feasibility studies for prospective SME investments; regular financial analysis to assess financial positions; developing realistic SME strategies that explicitly account for financial risks; establishing supportive institutions and incubators; prioritizing financing, training, and marketing support with a capable coordinating authority to assist struggling SMEs; and strengthening scientific management, IT adoption, specialization, and competency-based staffing. Future research could employ longitudinal designs, larger and more representative samples, and multivariate models to capture complex relationships, and explore sector-specific dynamics and the role of external factors (legal frameworks, infrastructure, security).

- Scope limited to SMEs in Sebha, Libya; results may not generalize to other regions or countries. - Timeframe primarily 2010–2017 (with broader discussion to 2010–2019); cross-sectional survey limits causal inference. - Population figures reported inconsistently (240 accessible projects vs 420 noted later), which may affect sampling frame clarity. - Security and accessibility constraints (focus on safer areas) may bias the sample. - Reliance on self-reported questionnaire responses introduces response and social desirability biases; moderate reliability (overall Cronbach’s alpha ~66%). - Simple linear models may not capture complex, mediated or nonlinear relationships between risk, management strategies, and success outcomes.