Data Analysis

For this research it was planned to conduct qualitative interviews with key managers, and an internal focus group was scheduled as follow up. In the qualitative interviews, key managers’ understanding and perspectives were solicited to understand their key concerns, expectations and the social learning capability. Afterwards, the results were discussed by the internal focus group.

Content analysis is used mainly for quantitative research where textual information may be initially classified into well-defined categories and statistical analysis performed later on. This orientation is known as quantitative analysis of qualitative data (Elo and

Kyngäs, 2008, p. 108) and is appropriate to this interpretative research study. Both qualitative interviews and focus group involve predominantly textual data and it was perceived as desirable to take some form of semi-automatic approach to classify, categorise and perform semantic analysis. The content analysis tools, Leximancer and NVivo, were employed.

For text data analysis, content analysis is a common research method that is highly flexible (Cavanagh, 1997, p. 10). It comes from a family of methods of intuitive,

interpretive, systematic and textual analyses, with a long application history in research in Scandinavia going back to the eighteenth century. Researchers can choose a

specific type according to the degree of theoretical requirement and substantive social interests in the context of research and the depth of the problems in terms of scope (Weber, 1990, pp. 10-40). Moreover, there are no concrete definitions and procedures in content analysis to limit its wider application (Tesch, 1990, p. 186). In principle, there are two non-mutually exclusive perspectives in content analysis, that is, qualitative and quantitative for exploratory and interpretative research (Tashakkori and Teddlie, 2010, pp. 564-567).

Qualitative content analysis addresses language as communication, considering the semantic meanings underlying acquired textual information (Neuendorf, 2002, pp. 100- 120), which can be in the form of verbal expression, printed documents or electronic messages. In this orientation it is more than simply counting words, comprising an intense examination of language in order to categorise vast amounts of textual information into structural categories. The categories represent either explicit or

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underlying knowledge in a given study environment (Downe-Wamboldt, 1992, p. 314). Therefore, qualitative content analysis is the subjective interpretation of collected textual information using systematic coding process hoping to identify hidden themes and patterns.

There are three approaches to data categorisation (Hsieh and Shannon, 2005, p. 1280). First is conventional content analysis, starting with observations in research such that codes are derived from data in the process of analysis. The second is directed content analysis, in which codes derive from theories and relevant research. The last is

summative content analysis, in which all codes are suggested by researchers or literature reviews.

In the qualitative stream of content analysis, researchers attend to interpretive labels, closely monitoring small textual matters, re-articulation and re-interpretation of given texts. This allows the pre-set codes defined by quantitative content analysis to be flexible in handling different situations, which in turn promotes a better interpretative exploratory perspective. Therefore, it is important to make use of certain features of qualitative content analysis for this interpretative exploratory study of the Kunshan factory (Krippendorff, 2004, fig. 3.4).

The reliability of the instruments used in content analysis has to be addressed by researchers, including the coding scheme and data sheet, to permit replicable and valid inferences from the results obtained from the classified data. Reliability in content analysis has two separate factors (Lombard, Snyder-Duch and Bracken, 2002, p. 588) and one test of the coded set output from the content analysis process. A common approach is to set up teams of coders and to monitor discrepancies to keep them to a minimum. The other factor is the reliability of coding instrument itself (Hayes and Krippendorff, 2007, p. 78).

3.10.1 Threats to validity and reliability of the content analysis

The coding scheme for quantitative content analysis is set up before coding

commences. There is a coding scheme that operationalises contextual concepts in an amorphous way, so that all coding categories are relevant and valid. Relevancy means the availability of hypothesis testing and validity (Neuendorf, 2002, p. 112). Validity is examined in several ways. The first is face validity, pointing to the extent of a

measurement tool’s ability to contain the essence of the concept under measurement. A practical coding scheme could have coding categories at the highest possible scales: nominal, ordinal, interval, and ratio scales, such that the behavioural deviation can be captured by a highly granular scales, and an example is the Likert scale of 7 to 11

(Lombard, Snyder-Duch and Bracken, 2004). Coding schemes must start with clear definitions, easy-to-follow instructions and be illustrated with unambiguous examples. This enriches the associated reliability. In case of later amendments to the coding scheme, all coded data must be refreshed accordingly.

Qualitative content analysis shares similar validity and reliability measurements with quantitative content analysis. Nonetheless, the focus is on the creation of a broad picture of a given situation. There are several additional criteria for validity and reliability (Neuendorf, 2002, p. 112), namely ‘truth value,’ ‘credibility’, ‘transferability’, ‘dependability’, and ‘confirmability’.

Reliability may of several types, such as information stability, interpretation reproducibility and measurement accuracy (Krippendorff, 2004, pp. 130-132), yet reliability alone is not a guarantee of validity. Reliability data are not limited to

information recorded by individual human beings. Business accounts, medical records and court ledgers are examples of works from institutions (Krippendorff, 2004, fig. 11.1).

3.10.2 Overcoming the disadvantages of content analysis

There are several ways to increase the reliability of coding. One method of doing so is through selecting disclosure content categories through highly relevant and seminal literature to obtain a clear definition of codes. The next technique is to build up reliable coding sheets through clearly specified decision categories and rules, while the third is training in coding to ensure an acceptable standard.

Krippendorff (2004, pp. 130-136) recommends the following sequence of mitigating actions to remove known threads in content analysis:

(1) Unitizing, which involving physical or contextual, systematic distinguishing of segments of texts, images, voices, and other observables, that of interest to an analysis; (2) Sampling allows content analyst to economize on research efforts by limiting observations to a manageable subset of units that is statistically or conceptually representative of the set of all possible units, the population and universe of interest; (3) Kikumura Recording and coding relies on coding instructions, bridges the gap between unitized texts and someone's reading of them, between distinct images and what people see in them, or between separate observations, and their situational interpretations. One reason for this analytical component is researcher's need to create durable records of otherwise transient phenomena, such as spoken words or passing images; (4) Reducing means contracting data to manageable

representations: relying on established statistical techniques or other methods for summarizing or simplifying data, efficient representation; (5) Abductive inferring contextual phenomenon relies on analytical

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the gap between descriptive accounts of texts and what they mean, refer to, entail, provoke, or cause. Abduction is a form of logical inference that goes from observation to a hypothesis that accounts for the reliable data (observation) and seeks to explain relevant evidence; (6) Narrating gives answer to the research questions and relies on narrative traditions or discursive conventions established within the discipline of content analyst. (Krippendorff, 2004, pp. 130- 136)

The following are the key precautionary actions and points to be borne in mind to avoid the disadvantages of this research at the Kunshan factory:

1. It is an analytical action for a content analyst to access textual data 2. A clear examination question is critical for analysts to answer when

examining collected textual data

3. A context for making sense of the body of text and data

4. An analytical construct capable of operationalising the knowledge in content analysis

5. Interventions to address the research question, so as to accomplish content analysis

6. Validating coding to justify the content analysis.