Early Data Analysis

In this section, a brief introduction to early data analysis, the process used in early data analysis to develop codes and categories as well as its results are discussed in sections 5.1.1 to 5.1.3.

5.1.1 Introduction

Two popular approaches to grounded theory exists; they are the systematic pro-cedures of Corbin and Strauss (2008) and the constructivist approach of Charmaz (2014). For the purpose of this study, the systematic and analytic procedures of Corbin and Strauss (2008) is used. Within this type of procedure, it is continuously advocated that theory should be inductively generated through the systematic anal-ysis of data to help explain the phenomenon (Creswell, 2007). In the view of Corbin and Strauss (2008), the fundamental part for theory development is the use of the constant comparative method. Therefore, the constant comparative method is used throughout this study. During the early phases of data analysis, each interview is transcribed from which the data is immediately analysed prior to proceeding with the next round of interviews. Methodology and data analysis, therefore, proceed simultaneously. During this phase, twenty categories are generated in a systematic manner. The process of codes and category development and the results of the process are described in sections 5.1.2 and 5.1.3 respectively.

5.1.2 The Process of developing Codes and Categories

The development of codes and categories procedure is in line with the grounded theory methodology (Creswell, 2007; Corbin and Strauss, 2008; Charmaz, 1990). Af-ter completion of the early inAf-terviews, all inAf-terviews are transcribed into documents which are imported into the Nvivo software (QSR International: NVivo, 2012) for early data analysis. This computer software is used to organise, categorise and anal-yse raw data. It also serves the purpose for memoing, interpreting, and visualising data, which are very well known analysis procedures used in grounded theory (Corbin and Strauss, 2008). The Nvivo software (QSR International: NVivo, 2012) is also used in order to conform to ethical considerations since it provides security for stor-ing the database and relevant files together into a sstor-ingle file.

An important first step to the early data analysis is to comprehend the meaning of the interviews in their entirety (Creswell, 2007). All transcripts are reviewed sev-eral times to help identify emergent themes. During the process of reading for ovsev-erall understanding, written records, better known as memos (Corbin and Strauss, 2008), are used to help explore the database. Strauss and Corbin (1998) define memos as the researcher’s “records of analysis, thoughts, interpretations, questions, and directions for further data collections”. Thus, the technique of memoing is used throughout the remainder of the data analysis process.

As the researcher read through the transcribed data, short sections with descrip-tive codes are summarised and categorised on the Nvivo software (QSR International:

NVivo, 2012). This procedure is called open coding. Corbin and Strauss (2008) and Creswell (2007) describes open coding as the first step of theoretical analysis that pertains to the initial discovery of categories of information. Corbin and Strauss (2008) suggests that open coding is used “to produce concepts that seem to fit the data”. Therefore, these concepts or categories are provisional and only aim to open the enquiry from which the researcher can categorise the data into distinct parts and carefully examine, compare for similarities and differences and ask questions that reflect the phenomena found in the data. As suggested by Creswell (2007), codes can be labelled using in vivo codes (i.e. the words often used by participants) or in vitro codes (i.e. the expressions introduced by the researcher). The researcher uses both these code labels during the open coding procedure.

Coding is done using sentence or paragraph analysis (Corbin and Strauss, 2008).

As the researcher progressed through the data, the constant comparative method of analysis is used. Corbin and Strauss (2008) defines the constant comparative method as:

“The analytic process of comparing different pieces of data for similarities and differences...comparing incident against incident for similarities and differences.”

From open coding, axial coding ensued. According to Corbin and Strauss (2008), axial coding is used to begin the process of reassembling the fragmented data ob-tained during open coding. The purpose for axial coding is to develop dimensions and properties for each category (Corbin and Strauss, 2008). This required the re-searcher to examine each open code in detail and to expand, explore and examine the relationship between codes. Very often, the researcher proceed with open and axial coding simultaneously. According to Strauss and Corbin (1998), “axial coding does require that the analyst have some categories, but often a sense of how categories re-late begins to emerge during open coding”. Considering the latter statement, coding intensively and concertedly around single categories even during the early stages of data analysis is expected.

5.1.3 Results of Early Data Analysis

As a result of the development of dimensions of the categories, a list of interview questions is generated for the next round of data collection. During the process of the second stage of coding, codes of significant similarities are combined as one category, other codes are renamed that appeared coherent and for others the code name became the category. The process of constant comparison and theoretical sampling continued throughout the analysis until the point of conceptual saturation is reached(Corbin and Strauss, 2008). Upon generating categories and developing their dimensions, the most critical phase of analysis is to interpret the categories holistically. At this point, all relevant properties and dimensions are critically analysed to obtain a clear understanding of each category from which the core phenomenon became comprehensible (Corbin and Strauss, 2008). New insights into links between these categories emerged from which a list of twenty categories is identified. See table 5.1 for the list and description of the twenty categories developed during open and axial coding.

Table 5.1: Code structure based upon participant comments and interpretation

Category Name Description AM link to the food

industry

How the food industry think AM could benefit their organisation and how they think they will overcome the competitive pressure of producing high-quality product with AM. Possible links (were AM will fit best) towards the food industry.

Asset classes The classes the participants suggest are relevant within the food industry; typical classes that emerge are people, machines and equipment, customers, brands and reputation.

Asset problems Typical problems that food management encounter with assets which include the classes of machines, equipment, and people.

Communication Communication problems which erode within departments;

these include silo thinking and constant conflict between en-gineering or maintenance and quality.

Customer satisfaction Product specifications, regulatory and legislative requirements demanded by customers.

Engineering vs Food Industry

The differences between food manufacturing and industrial man-ufacturing.

Financial Implications Financial issues that often prevent the food industry from in-vesting in new technology or machines or training.

Integration implications Integration suggested by participants: overlapping of systems;

implementation obstacles; consequences for this integration to happen (training, systems, culture)

Integration of AM with TQM

Possible mapping of ISO 55000 with selected standards which also includes any links found between AM and TQM.

ISO 55000 Understanding the integration of ISO 55000.

Management systems (standards)

General information regarding standards, systems relevant to the food industry.

PRP’s and SOP’s Prerequisite Programs and Standard Operating Procedures Standard selection

(business objectives)

Standard selections are related to business objectives (e.g. GFSI certification)

Integration of AM with TQM on Operational

Level

Suggestions from participants on how they think the integration will look like on an operational level.

Leadership Top management responsibilities

Management principles Starting point of implementing sound standards and operating an integrated system holistically within the organisation.

People People should have a common goal, shared vision and work in a holistic environment where everybody must work together as a team.

Person responsible for AM

Person responsible for AM

Training Training is seen as critical in the food industry.

Value chain Business-to-Business vs Business-to-Consumer