Ethical Fashion Report 2017 Critique

The study contains comprehensive details on the policies and procedures of different brands, allowing customers to make more informed decisions. It evaluates businesses based on forty basic standards spread across four themes: supplier partnership and auditing, workforce empowerment, regulations, and understanding the suppliers. Furthermore, the study includes information on the status of worker abuse in the Asia Pacific region. According to Lee, Seifert, and Cherrier (2017, p.172), the Ethical Fashion Report is critical for positive buying and the prevention of worker and customer manipulation. According to Lee Seifert and Cherrier (2017, p.170), the report findings are reliable indicators of high performance of the New Zealand fashion brands in attempts to address the issue of worker exploitation within the supply chain. Moreover, New Zealand fashion brands score above the international average. The three areas of change from last year are transparency, provision of a living wage, and traceability.

Research Methodology

The study adopted a mixed research methodology since it used both descriptive and analytical research methods. According to Csikszentmihalyi and Larson (2014, p.42), descriptive research method helps in carrying out the surveys which were vital for identification of the facts about company operations. It aimed to describe the companies’ state of affairs as they are. On the other hand, analytical research method helped in using the facts collected from the surveys to analyse the present situations under each category. Moreover, the analytical research methodology was vital for the provision of a critical evaluation of the brands. Since the study seeks to assess the current situation, conduct an analysis, and site areas of improvements, the applied research methodology was the most appropriate for achieving the research objectives (Csikszentmihalyi and Larson 2014, p.38). Moreover, consumers can only make informed choices if they understand the current situation in the supplier chain of the brands they consume and how such conditions have changed over time. Therefore, applying any other type of the research methodology would have not effectively revealed the state of worker exploitation in the supplier chains.

Data Collection

To collect the relevant data for the report, Baptist World Aid Australia assessed publications, independent reports, and data on every brand. Surveys covering the forty specific criteria were sent to brands. Ferrara, De Meo, Fiumara and Baumgartner (2014, p.318) point out that follow ups through letters, phone calls, and emails were very critical in ensuring a higher response rate. However, the fact that the research team did not conduct background checks, companies could give wrong data to deceive their consumers. Thus, it’s difficult to ascertain the validity of information collected from company websites without carrying out factory floor inspections. It’s because companies may use wrong information on their websites and reports to deceive their customers and promote their brands.

The research team could have used other data collection methods like company social media activity and online surveys to collect more in depth information about the brands. Moreover, face to face interviews with the brand ambassadors, company managers, and employees would not only improve the response rate but also would have helped in the collection of accurate and reliable data (Nunan and Di Domenico 2013, p.509). Information regarding worker unions and policies in place could have been collected better by carrying out benchmark assessments, focused group discussions, and questionnaires on workers of the selected companies. The data is compared with last year research findings, thereby showing a time change of one year.

Sampling Methods

The research adopted a mixed sampling method to select the participants in the research. From the research methodology, it evident that stratified sampling was used to group brands in their different categories and samples selected from the categories. Since the research relied heavily on the availability of information, annual turnover, voluntary request to be included in the survey, it can be concluded that purposive or convenience sampling technique was also applied. Also, voluntary sampling was also adopted in the sample selection.

However, using annual turnover as a factor would differentiate parent companies and brands since some brands may perform better than the others (Lee et al. 2017, p.172). The reliability of the information may be questionable in a case where a company had more than two brands included in the sample. Since only 100 out of the numerous brands were selected for the research, the sample is invalid. This is because the sample size was smaller based on the study population. Again, the set criteria for sample selection permitted the inclusion of more than one brand from the same parent company, hence, indicating bias in sample selection.

Bias, Logical Fallacies, and Assumptions

Although researchers always try to reduce bias that may arise when collecting data, the research team did not do much to prevent errors that may have arose due to assumptions, bias, and subjectivity. For instance, the collection of data from company sites and forwarding for approval from the companies cannot guarantee the accuracy of information. This is because all respondents (companies) may not be willing to reveal information that is contrary to what is posted on their websites. Ormston, Spencer, Barnard and Snape (2014, p.32) argue that there may be social desirability bias since every brand would want to be accepted by their customers.

The fact that the supply chain, publishing supplier, and operational information are important to workers, consumers and unions make it easy for brands to manipulate the information they release to the public through their websites and other forms of communication to the public. Although some thematic areas like the transparency of a company, it must be noted that transparency is only a means to the desired change and not the end process (Ormston et al. 2014, p.32). Thus, by assuming that transparent brands perform better and thereby being graded higher is only a fallacy and can lead to bias. Thus, brands may appear transparent but not responsive to workers ‘grievances. The assumption that transparency leads to a structural or systematic change in the fashion industry may result in wrong conclusions that highly transparent brands are leading in the systematic change process (Yannakakis and Martínez 2015, p.13).

Ethical Requirements

There were no ethical considerations in the research. There is no evidence of ethical approval for research that involves human beings like in this case. Moreover, the respondents were not anonymous thereby breaching the ethical code of confidentiality and anonymity (Lee et al. 2017, p.172). This is because the raw survey data from the respondents have been attached to the report for public viewing hence going against the confidentiality and anonymity requirements in research.

References

Csikszentmihalyi, M. and Larson, R., 2014. Validity and reliability of the experience-sampling method. In Flow and the foundations of positive psychology (pp. 35-54). Springer Netherlands.

Ferrara, E., De Meo, P., Fiumara, G. and Baumgartner, R., 2014. Web data extraction, applications and techniques: A survey. Knowledge-based systems, 70, pp.301-323.

Lee, M.S., Seifert, M. and Cherrier, H., 2017. Anti-consumption and Governance in the Global Fashion Industry: Transparency is Key. In Governing Corporate Social Responsibility in the Apparel Industry after Rana Plaza (pp. 147-174). Palgrave Macmillan US.

Nunan, D. and Di Domenico, M., 2013. Market research & the ethics of big data. International Journal of Market Research, 55(4), pp.505-520.

Ormston, R., Spencer, L., Barnard, M. and Snape, D., 2014. The foundations of qualitative research. Qualitative research practice: A guide for social science students and researchers, 2, pp. 23-45.

Yannakakis, G.N. and Martínez, H.P., 2015. Ratings are overrated!. Frontiers in ICT, 2, p.13.