Efectos de abonos orgánicos de residuos de bambú sobre la microflora del suelo

Testing the reliability of a research finding refers to evaluating the extent to which the selected research methods of a study yield consistent results (Saunders et al., 2009). Robson (2002) states that there were four potential threats to research reliability. Two of these threats are concerned with the research participants (1- participants’ errors and 2- participants’ bias) and two about the observer (1- observers’ error and 2- observers’ bias) (Robson, 2002).

As explained in section (3.4.1.1) of this chapter, the data about the operational practices of the companies in this study is collected from the IMechE’s MX Awards archive. The companies enter this awards scheme in order to be recognised for their manufacturing excellence. Therefore, it is less likely that they provided inaccurate information about their manufacturing practices. In addition as a part of the process to select the best manufacturing companies, the IMechE’s assessors had also visited the companies to verify their manufacturing practices. Therefore, there has been a high level of accuracy in collecting the data about the companies’ operational practices. In addition, the financial data of the companies is collected from two financial databases (FAME and Amadeus). These databases either directly collects financial reports from the companies or from the official organisations that are in charge of collecting this information (FAME, 2014). The author would have had to use the same method to collect this data.

An alternative method for collecting these data would be to use a survey to collect companies’ data about their operational practices and financial results. However, there are three potential problems associated with this method that could reduce the reliability of the collected data:

1- First, as suggested in the study by Dubey & Gunasekaran (2015), there would be a need to split the survey into two parts and ask suitable individuals in the firms to complete their related parts, to ensure the data they provided is accurate. However, it would be difficult to verify if suitable individuals in the companies have completed the survey or not.

2- Second, it would be difficult to verify if the responses provided by the companies are matching with their actual performance. The data in the IMechE’s archive is verified against the companies’ actual practices, by the IMechE’s qualified assessors. It was not possible for the author to verify the companies’ responses based on his own knowledge.

3- As suggested by many of the earlier studies, such as Wang et al. (2014), one of the limitations of using survey for data collection is that data would be subjective and based on self-reported managerial opinion. However, the data in the IMechE’s archive is reviewed and scored based on the IMechE’s assessment guidelines. So although the IMechE’s archive is also a perceptual data; this data is based on a common assessment guideline and therefore is consistent for all companies. If the data was collected by a survey, each manager might have their own judgement about their performance which might not be similar to that of the other managers. Therefore, it would be difficult to compare their responses.

Therefore, the possibility of having participants’ error and participants’ bias would have been higher if the data were collected by a survey in this study. In addition, the IMechE and the two financial databases (FAME and Amadeus) have no bias towards the companies that their data is used in this study. Consequently, the possibility of having observers’ bias and observer error is also low in this study.

3.5.3 Generalisability

Generalisability of research findings refers to the extent to which the findings are applicable to other research settings (Saunders et al., 2009). The sample of this study consists of seventy-nine UK manufacturing companies. According to the latest UK business population estimate in October 2015, there are 275,565 manufacturing companies in the UK. Therefore, the sample of the study represents small part of the entire UK manufacturing sector. Also, the external validity of the findings of the study is further reduced by its sampling method. Generally, there are two types of sampling designs:

1- Probability sampling design in which all members of a population have an equal chance of being selected as a subject, such as simple random sampling.

2- Non-probability sampling in which the members of a population do not have equal chance of being selected as a subject, such as convenience sampling and purposive sampling (Sekaran, 2003).

In this study ‘Judgement sampling’ is used, which is a purposive sampling and is under the category of non- probability sampling design. In judgement sampling, the subjects are selected because they are in the best position to provide the necessary information for a study (Sekaran, 2003). Although the findings based on

this sampling are less generalisable than the methods in probability sampling design; they are sometimes

the best sampling choice to be used (Sekaran, 2003). In this study, because of the reasons provided in the previous section (3.5.2), using other methods to collect data for the study would be less dependable. Therefore, using the data from the companies in the IMechE’s archival data was the best choice.

Overall, though restricted in generalisability, the dataset of this study is still larger than many of the earlier similar studies in other countries. For example, Valmohammadi (2011) use fifty-three Iranian companies to explore the impact of their total quality management (TQM) on their financial performance. Similarly, Abusa & Gibson (2013) use fifty-six Libyan manufacturers to examine the impact of TQM on their financial performance.

Another study with a small sample size is Kumar et al. (2009) which uses fourteen Canadian manufacturing companies to examine the impact of TQM on their financial performance. Kumar et al. (2009) state that result of their study is not generalisable to all Canadian companies. However, it shows what financial benefits can be achieved by successful implementation of TQM programme by Canadian companies (Kumar et al., 2009). Similarly, the findings of this study cannot be generalised to all UK manufacturing companies; however it shows the financial benefits that can be achieved by strong performance in specific operational practices.

3.6. Conclusion

This chapter discusses the research design for conducting this research, based on Saunders et al.’s (2009) recommended research onion. First, in section 3.2, the research philosophy and approach for conducting this research was explained. There are three ways of thinking about research philosophy including: 1-Ontology, 2-Epistemology and 3-Axiology and there are four main research philosophies in business and management research including: 1-Positivism, 2-Realism, 3-Interpretivism and 4-Pragmatism (Saunders et al., 2009).

Based on the characteristics of this research,the ontological position of this research is closer to subjectivism

and therefore closer to the interpretivism research philosophy. The epistemological position of the study is aligned with pragmatism and theaxiological position of this research is closer to the positivism philosophy.

There are two main approaches to performing a research, including: deductive and inductive approaches. This study starts with developing a conceptual model based on the findings of earlier studies and testing the model based on the data from UK manufacturing companies. Therefore, this approach is aligned with testing theory and deductive research.

Based on the purpose of the study, section 3.3 discussed the selected research strategy, choice of data collection and analysis and the time horizon of the study. The purpose of this study is to find the relationship between the operational practices and financial performance of UK manufacturing companies. Explanatory study is useful to studying a situation to explain the relationship between variables (Saunders et al., 2009). Therefore, this study is an Explanatory research and the research question of the study is: What operational practices in UK manufacturing companies can improve their financial performance? After analysing seven potential research strategies and based on the purpose and research question of the study, archival research strategy was considered a suitable strategy for this study.

The data about companies’ operational practices is in numerical format and is collected from the IMechE’s archival data. The financial data are also in numerical format and collected from two financial databases (i.e. Fame and Amadeus). Using statistical analysis methods, the relationships between these variables are identified. To explain the identified relationships between the variables, the findings of the study were discussed with ten academics or business consultants in two focus groups and two interviews.

Using qualitative data to explain the relationships between quantitative variables is one reason for using a mixed-method in a study (Saunders et al., 2009). The data from the focus groups and interviews are also analysed qualitatively. Therefore, a mixed-method research is applied in this study. In addition this study is longitudinal study in which the impact of the companies’ operational practices on their financial performance is analysed up to three years after their participation in the MX Awards.

Section 3.4 discusses the methods of data collection and analysis in this study. The main sources of data collection in this study are two independent archival datasets. The companies’ operational information is collected from the IMechE’s archival data. The IMechE uses a sophisticated procedure to collect this data. First, interested companies send their self-appraisal questionnaire in ten areas of performance to the IMechE to apply for their chosen award (s) (Tsinopoulos & McDougall, 2011). Then the companies’ audits are reviewed and scored by the IMechE’s assessment board and the companies are ranked based on their

confirm that companies’ practices are matching with the companies’ self-appraisals and to clarify any other questions. Finally, winner companies in each category of the awards are selected. Also, regardless of winning an award or not, the IMechE sends a feedback report to all applicant companies to suggest the areas of improvement (Garside & Tsinopoulos, 2004). The financial data of those companies are collected from two financial databases (i.e. FAME and Amadeus). The data for these databases are either directly collected from the financial reports of the companies or from the official organisations that are in charge of collecting this information (FAME, 2014).

Correlation and regression analyses are the two most commonly recommended statistical methods for analysing the relationship between two or more variables. These two methods were also the most common statistical methods used in the previous studies that analysed the relationship between companies’ operational practices and their financial performance. Therefore, these two methods are selected to be used in this study.

The study began by conducting a correlation analysis, which tests the interdependence of the variables. It was used to find the correlated pairs of the companies’ operational and financial variables. There were three alternatives available for performing correlation analysis. However, the dataset of the study failed to meet the assumptions of the Pearson and Spearman correlation methods. Pearson correlation assumes that all variables should be measured at a continuous level, but in this study the operational measures were in an ordinal level. Also, Spearman correlation assumes uniformity of the dataset, an assumption that this study failed to meet. Uniformity refers to the simultaneous increase or decrease of the two data items under analysis. However, the dataset of the study meets the assumption of the Chi-square test for independence, and therefore a special form this method (Fisher’s exact test), which is suitable for smaller datasets, was used for finding correlations in this study.

Then, for each of the identified correlated pairs in the previous step, regression analysis was used to find dependence of the financial variables on their associated operational variables. Here two methods were also available: 1- Linear regression, which is suitable for continuous measurement level, and 2- Logistic regression, which is suitable for predicting binary data items. Since the financial variable of the study were binary and could only have into two distinct categories (Improved and deteriorated), Logistic-regression fit the dataset. As with correlation analysis, a special form of Logistic-regression – Exact-logistic-regression – was used in this study.

Finally, in section 3.5 of this chapter, the credibility and generalisability of the findings of the study was discussed. The credibility of the study was tested by evaluating the validity of the selected variables of the study to be consistent with the purpose of the study. The validity of the operational variables was tested by finding similarities between the question from the IMechE’s MX survey and the operational variables that have been considered in the earlier studies.

Since the operational variables of the study (question from the IMechE’s MX survey) are similar to the operational variables of the earlier studies, they are valid measures to evaluate companies’ operational practices. Also, the selected financial ratios from the databases of the study are aligned with the recommendations of some of the reviewed frameworks in chapter two. Therefore, the financial variables of the study are also valid measures to evaluate companies’ financial result.

The reliability of research findings was also examined by considering Robson’s (2002) four potential threats to research reliability including: 1- participants’ errors, 2- participants’ bias, 3- observers’ error and 2- observers’ bias. The main sources of data in this study were archival. The data about companies’ operational data were collected from the IMechE’s archival data and the financial data were collected from two financial databases (i.e. Fame and Amadeus). Considering other options for data collection (e.g. a survey), the possibility of having participant bias and participant error would have been higher. Also, the selected databases have no bias towards the companies whose data is used in this study; therefore, the possibility of having observer’s bias and observer error is also low in this study.

With regard to the generalisability of research findings, since the sample of the study represents a small portion of the entire UK manufacturing companies, the findings of this study are not applicable to all UK manufacturing companies. However, the findings of the study can show what financial benefits can be achieved by companies with strong performance in specific operational practices.

The next chapter explains the results of statistical analysis on the UK manufacturing companies’ data to support or not support the developed hypotheses outlined in chapter two.

4. Data analysis and findings