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South African Journal of Economic and Management Sciences

On-line version ISSN 2222-3436
Print version ISSN 1015-8812

S. Afr. j. econ. manag. sci. vol.23 n.1 Pretoria  2020

 

Table 8 represents the goodness of fit statistics. The degrees of freedom were recorded at 41. The minimum fit function chi-square is 63.53 (p = 0.014), the normal theory weighted least square chi-square is 58.05 (p = 0.0041). The estimated non-centrality parameter (NCP) was recorded at 17.05 with a 90% confidence interval of 0.80. The population discrepancy function value was recorded at 0.21 with a 90% confidence interval at 0.0096, 0.5. The entire statistic reported on the chi-square index was acceptable, in agreement with the guidelines of the ranges being as high as 5.0 (Wheaton, Muthen, Alwin & Summers 1977) to as low as 2.0 (Tabachnick & Fidell 2007). The root mean square error of approximation (RMSEA) was recorded at 0.071. The adjusted goodness of fit index (GFI) is 0.82. It has been argued that an RMSEA of between 0.08 and 0.10 provides a mediocre fit and that below 0.08 shows a good fit (MacCallum, Browne & Sugawara 1996). Therefore, the recorded RMSEA of 0.078 is acceptable. The GFI was recorded at 0.89. Previously a cut-off point of 0.90 was recommended for the GFI; however, simulation studies have shown that when factor loadings and sample sizes are low, a higher cut-off of 0.95 is more appropriate (Miles & Shevlin 2007). In light of this, the GFI was acceptable. The root mean square residual (RMR) and standardised root mean square residual (SRMR) of 0.12 and 0.11 were recorded. Values for the SRMR range from 0 to 1.0, with well-fitting models obtaining values less than 0.5 (Byrne 1998; Diamantopoulos & Siguaw 2000); however, values as high as 0.8 are deemed acceptable (Hu & Bentler 1999). Thus the SRMR value of 0.11 is acceptable. The non-normed-fit index (NNFI) was recorded at 0.83. Values for this statistic range between 0 and 1, with Bentler and Bonnett (1980) recommending values greater than 0.80 as indicating a good fit. In the past two decades suggestions have been made that the cut-off criterion should be NNFI 0.95 (Hu & Bentler 1999). The comparative fit index (CFI) was recorded at 0.87. This is one of the most popularly used fit indices, as it is one of the measures least affected by sample size (Fan et al. 1999). For this, a value of CFI 0.95 is presently recognised as indicative of good fit (Hu & Bentler 1999).

 

 

Most of the fitness statistics confirmed that the measurement model of the relationship between business environment and the three organisational structure variables of centralisation, complexity and formalisation was fit for the purpose.

Relationships between variables

The propositions about the relationships between the variables presented above are discussed here. The assessments of the relationships are based on the t-values presented in Table 9, the beta and gamma matrices.

 

 

Proposition 1: In an unstable environment, organisations adopt high levels of complexity compared to a stable environment.

From the gamma matrix, the causal path between environment ξ (exogenous latent variable) and complexity η (endogenous latent variable) is linked by the t-value of 1.60 with a standard error of 0.06 (see Table 9). The t-value is below the cut-off point of T 1.96 p (0.05) recommended by Hu and Bentler (1998) and Diamantopoulos and Siguaw (2000). This indicates that there is no significant relationship between unstable environment and high levels of complexity. The proposed relationship between the two variables could not be supported.

Proposition 2: In an unstable environment, organisations adopt a less centralised organisational structure compared to a stable environment.

From the gamma matrix, the causal path between the environment ξ (exogenous latent variable) and centralisation η (endogenous latent variable) is linked by the t-value of 1.40 with a standard error of 0.06 (see Table 9). The t-value is below the cut-off point of T 1.96 p (0.05) recommended by Hu and Bentler (1998) and Diamantopoulos and Siguaw (2000). This indicates that there is no significant relationship between environment and centralisation. The proposed relationship between the two variables could not be supported.

Proposition 3: In an unstable environment, organisations adopt a less formalised organisational structure compared to a stable environment.

From the gamma matrix, the causal path between environment ξ (exogenous latent variable) and formalisation η (endogenous latent variable) is linked by the t-value of 1.42 with a standard error of 0.052 (see Table 9). The t-value is below the cut-off point of T 1.96 p (0.05) recommended by Hu and Bentler (1998) and Diamantopoulos and Siguaw (2000). This indicates that there is no significant relationship between the environment and formalisation. The proposed relationship between the two variables could not be supported.

 

Discussion

The purpose of this article was to reconsider the propositions made by Burns and Stalker (1961) on how organisations should structure themselves in unstable environments. Burns and Stalker proposed that in unstable environments, organisations tend to adopt organic structures. The main properties of an organic structure as identified by the authors are low centralisation, high specialisation and high formalisation. The three propositions formulated by Burns and Stalker were empirically tested in this article.

This article considered the environment as the independent variable denoted by the four elements: political, economic, social and technological. The original works of Burns and Stalker considered the environment as a function of only two elements, that is, technological and marketing forces. It was necessary for us to expand the independent variables used by Burns and Stalker in our study as a result of: (1) fundamentally different environmental contexts and (2) the rapidly changing socioeconomic environment between 1961 when the seminal work was conducted and the present. Moreover, it is our conviction in this article that the relationships between organisation structure and operating environment could be explained using more variables other than market and technology.

Our findings in this article were inconsistent with the original work of Burns and Stalker. The research setting of our study exudes a high propensity for instability which one could consider as conducive for the empirical testing of Burns and Stalker's organic structure proposition. However, business organisations in Zimbabwe do not seem to adopt an organic structure despite the unstable environment in which they operate. Consistent with our findings, Zimbabwean organisations adopt a hybrid structure that suggests a combination of both mechanistic and organic structures. We attempt to attribute this contrasting outcome partly to the methodological problem of case study of homogeneous electronic firms that was adopted by Burns and Stalker. This contrasts with our quantitative research strategy, using heterogeneous firms in the manufacturing sector which represents the largest business sector in Zimbabwe's economy. Research methods literature has consistently argued in favour of the prospect of generalisable outcomes in quantitative research (Bryman & Bell 2011) in comparison to qualitative research, and case study research design in particular. Similarly, while Burns and Stalker's work was conducted in an economically unstable environment in the UK, the drivers of operating instability in Zimbabwe fundamentally transcend what could be referred to as a 'normal' economic environment to include complex political, turbulent social and, most importantly, 'abnormal' economy.

Beyond methodological and environmental factors that we have described above, the expansion of independent variables employed by Burns and Stalker and our study further accounted for the variations in both studies. It is our submission in this article that the present study has succeeded in empirically establishing a novel dimension in the existing relationship between organisational structure versus operating environment. This dimension presents an important incremental theoretical and practical contribution to the prevailing body of knowledge in the broad field of strategic and change management.

Lastly, through this article, we have responded to a stream of scholars advocating for an empirical establishment of hybrid organisational structures in addressing the rapidly changing business operating environment (Battilana & Dorado 2010; Doherty, Haugh & Lyon 2014). We therefore, based on our finding in this article, recommend a managerial practice that embraces a hybrid structure, rather than concentrating solely on the adoption of an organic structure in a dynamic environment.

 

Conclusion

In a highly dynamic operating environment like the one currently seen in Zimbabwe, it is theoretically expected that organisations adopt Burns and Stalker's organistic structural postulation in order to achieve some degree of efficiency. However, it is our argument in this article that rather than the norm, organisations in Zimbabwe react and adapt to the hostile and unpredictable dynamics in the economy by opting for a hybrid structural adaptation mechanism. Furthermore, it is characteristic of organisations to hurriedly downsize their workforce when confronted with operational difficulties occasioned by rampant economic and sociopolitical uncertainties. The outcome of such unplanned downsizing is the high propensity of the organisation losing employees whose skills and expertise are critical in the formulation and implementation of a turnaround strategy. We consider such practice as a 'panic' reactionary instead of proactive and sustainable contingency approach in managing organisational change. Short-term adaptation strategy of this description could be more damaging to optimal organisational efficiency and ultimate survival in the long run.

 

Acknowledgements

The authors would like to acknowledge the African Leadership University, Rwanda, for their contribution to the publication fees.

Competing interests

The authors have declared that no competing interests exist.

Author's contributions

N.S. conceptualised the paper and performed data collection, analysis and discussions. O.M.S. provided theoretical grounding and coordinated compilation.

Funding information

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Disclaimer

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.

 

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Correspondence:
Sibindi Ntandoyenkosi
sibindi@cbcbyo.org

Received: 21 Sept. 2017
Accepted: 29 Aug. 2019
Published: 10 Dec. 2019

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