Abstract
This chapter presents the concept of “vulnerability” and a vulnerability analysis for Small and Medium-sized Enterprises (SMEs) to assess the multiple risks firms might suffer from during economic and financial downturns depending on their management systems and financial health. SMEs can be more flexible and react faster than large firms but, unlike their larger counterparts, most of them do not have at their disposal effective management systems and tools to ensure their sustainability. In a context of economic and financial crisis, an emergency plan for vulnerable SMEs has been carried out in the Basque Country, Spain to address this issue using a vulnerability analysis model.
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References
Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Technological Studies, 2(2), 285–309.
Battiston, S., Delli Gatti, D., Gallegati, M., Greenwald, B., & Stiglitz, J. E. (2007). Credit chains and bankruptcies propagation in production networks. Journal of Economic Dynamics and Control, 31, 2061–2084.
Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience: The concept, a literature review and future directions. International Journal of Production Research, 49(18), 5375–5393.
Boissay, F. (2006). Credit chains and the propagation of financial distress. European Central Bank Working Paper, 573.
Bradley, D., & Rubach, M. (2002). Trade credit and small businesses: A cause of business failures?. Technical Report. University of Central Arkansas. Retrieved from http://sbaer.uca.edu/research/asbe/2002/papers/02asbe055.pdf.
British Standards Institution. (2006). BS 25999-1:2006 Business continuity management. Code of practice.
Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity assessment (p. 17). Thousand Oaks, CA: Sage publications.
Chin, W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 2(1), vii–xv.
Chin, W. W., & Frye, T. (2003). PLS-Graph version 3.00, Build 1017. University of Houston.
Christensen, C. M., & Raynor, M. E. (2003). The innovator’s solution: Creating and sustaining successful growth. Boston, MA: Harvard Business School Press.
Crutzen, N., & Van Caillie, D. (2008). The Business failure process: An integrative model of the literature. Review of Business and Economics, LIII(3), 288–316.
Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38, 2.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Franco, M., & Haase, H. (2010). Failure factors in SMEs: A qualitative study from an attributional perspective. International Entrepreneurship and Management Journal, 6(4), 503–521.
Freel, M. S., & Robson, P. J. A. (2004). Small firm innovation, growth and performance. International Small Business Journal, 22(6), 561–575.
Fujiwara, Y. (2008). Chain of firms bankruptcy: A macroscopic study of link effect in a production network. Advances in Complex Systems, 11(5), 703–717.
Masurel, E., & Montfort, K. V. (2006). Life cycle characteristics of small professional service firms. Journal of Small Business Management, 44(3), 461–473.
Nunnally, J. (1978). Psychometric theory (2nd ed.). New York, NY: McGraw-Hill.
Ooghe, H., & De Prijcker, S. (2008). Failure processes and causes of company bankruptcy: A typology. Management Decision, 46(2), 223–242.
ORKESTRA. (2013). Informe de competitividad del País Vasco 2013. Transformación productiva para el mañana. Publicaciones Deusto, Instituto Vasco de Competitividad-Fundación Deusto. Bilbao.
Penrose, E. (1959). The theory of the growth of the firm. Oxford: Oxford University Press.
Pretorius, M. (2008). Critical variables of business failure: A review and classification framework. South African Journal of Economic and Management Sciences, 11(4), 408–430.
Ropega, J. (2011). The reasons and symptoms of failure in SME. International Advances in Economic Research, 17(4), 476–483.
Schumpeter, J. A. (1942). Capitalism, socialism and democracy. New York, NY: Harpers & Bro.
Scott, M., & Bruce, R. (1987). Five stages of growth in small business. Long Range Planning, 20(3), 45–52.
Sheppard, J. P., & Chowdhury, S. D. (2005). Riding the wrong wave: Organizational failure as a failed turnaround. Long Range Planning, 38, 239–260.
Sull, D. (1999). Why good companies go bad. Harvard Business Review, 77(4), 42–56.
Teece, D., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
VTT Technical Research Centre of Finland. (2002). SME risk management toolkit. Retrieved from http://virtual.vtt.fi/virtual/pkrh/pdf/en/vulnerability-analysis-booklet.pdf.
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Annex: Evaluation of the Measurement and the Structural Model
Annex: Evaluation of the Measurement and the Structural Model
Evaluation of the measurement model
The normal criteria in order to accept an indicator as part of a construct is that it possesses a greater loading than 0.707, which implies that the variance shared between the construct and its indicators is larger than the error variance (Carmines and Zeller 1979). However, some authors believe that this rule should not be so strict and loads of 0.5 or 0.6 can be acceptable in the early stages of scales development (Chin 1998) or when the scales are applied in different contexts (Barclay et al. 1995).
The first results for the 19 original indicators show diverse outcomes. According to Diamantopoulos and Winklhofer (2001), reflective indicators are essentially interchangeable and; therefore, their removal does not change the essential nature of the underlying construct. Given the initial values obtained, some reflective indicators that did not meet the criterion of individual reliability were removed from the model. This situation was not unexpected due to the nature of the model. Without a more in-depth analysis, some of the removed indicators did not seem to be as representative as others and could be somehow redundant. Economic and financial indicators could also have been separated in two constructs following the failure process proposed by Ooghe and De Prijcker (2008).
The tests for the remaining ten indicators show satisfactory results:
Constructs | Indicators | Table 4. Individual item reliability | |
---|---|---|---|
Original indicators | Remaining indicators | ||
Structural vulnerability | Strategy | 0.7326 | 0.7166 |
Size | 0.5920 | 0.6151 | |
Continuity | 0.0923 | Removed | |
Management | 0.7357 | 0.7180 | |
Managers | 0.6810 | 0.6896 | |
Organization | 0.7075 | 0.7198 | |
Operational vulnerability | Customer concentration | 0.3870 | Removed |
Internationalization | 0.5739 | 0.6202 | |
Commercial resources | 0.8103 | 0.8228 | |
Innovation | 0.5023 | 0.6047 | |
Production | −0.1808 | Removed | |
Purchasing | 0.1259 | Removed | |
Quality | 0.3497 | Removed | |
Economic-financial vulnerability | Added value | 0.0525 | 0.5110 |
Finance | −0.1851 | Removed | |
Short-term finance | −0.3504 | Removed | |
Costs | −0.9055 | Removed | |
Wages | −0.5685 | Removed | |
Equity | 0.3171 | 0.8360 |
Composite reliability measures construct reliability. Values starting from 0.7 are accepted in early stages of research but values higher than 0.8 would be preferable (Nunnally 1978). The first and second constructs are more reliable than the third one, which, not by much, but does not meet the standards.
AVE or Average Variance Extracted measures convergence validity or the amount of variance of the construct which is due to its own indicators. Recommended values for AVE should be greater than 0.5 (Fornell and Larcker 1981). The results show that none of the constructs meet the standards, but they are close to them.
Table 5. Construct reliability and convergent validity | Structural vulnerability | Operational vulnerability | Economic-financial vulnerability |
---|---|---|---|
Composite Reliability | 0.822 | 0.727 | 0.636 |
Average Variance Extracted | 0.480 | 0.476 | 0.480 |
When examining discriminant validity for PLS models the accepted method is to show that the square roots of the average variances extracted (diagonal values) are higher than the inter-construct correlations. In this case, the test shows satisfactory results.
Table 6. Discriminant Validity | Structural vulnerability | Operational vulnerability | Economic-financial vulnerability |
---|---|---|---|
Structural vulnerability | 0.6928 | ||
Operational vulnerability | 0.550 | 0.6899 | |
Economic-financial vulnerability | 0.211 | 0.164 | 0.6928 |
Evaluation of the structural model
In order to assess the research hypotheses, path-coefficient levels and the contribution of the exogenous constructs to the amount of variance explained in endogenous constructs (R2) have been measured, multiplying path and correlation coefficients. Values starting from 0.2 are accepted in early stages of research but values higher than 0.3 are preferable.
A t-statistic was used to check the significance of path coefficients. Any value greater than 1.6479 is likely to be significant (p < 0.1).
In addition, the predictive power of the model has been tested using the Q2 Stone-Geisser statistic. Cross-validated redundancy (Q) higher than 0 means the model has predictive relevance.
As we can see in Table 7, structural vulnerability is a key element to explain operational vulnerability, but operational vulnerability does not significantly influence economic and financial vulnerability.
Table 7. Structural model evaluation | Path | T-Statistic | Correlation | R2 (amount of variance explained) | Q2 (cross-validated redundancy) |
---|---|---|---|---|---|
H1: Impact of structural vulnerability on operational vulnerability | 0.550 | 8.3492 | 0.550 | 30.25 % | 0.0185 |
H2: Impact of operational vulnerability on economic-financial vulnerability | 0.164 | 1.2645 | 0.164 | 2.69 % | −0.4431 |
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Garagorri, I. (2016). SME Vulnerability Analysis: A Tool for Business Continuity. In: North, K., Varvakis, G. (eds) Competitive Strategies for Small and Medium Enterprises. Springer, Cham. https://doi.org/10.1007/978-3-319-27303-7_12
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