Identification and Leveling of Production Technology Selection Indicators in Iran’s Steel Industry Using Qualitative Content Analysis

Abstract

The main objective of the present study was to identify and level the indicators for selecting production technology in Iran’s steel industry using qualitative content analysis. The first step of the research was related to identifying production technology selection indicators in industries, and the statistical population included articles related to the identification of production technology selection indicators in industries. The population size was 124 articles, and the sample size was considered to be 71 articles. The sampling method in this section was judgmental sampling. The second to fourth steps of the research addressed the localization, analysis of influencing and influenced relationships, and leveling of production technology selection indicators in Iran’s steel industry. The statistical population in this section included academic scholars and industry experts who were knowledgeable about production technology selection indicators in Iran’s steel industry. The sample size in this section was determined to be 12 experts. The sampling method in this stage was purposive sampling. The data collection method and instrument in the first step consisted of a library-based study. The data collection instrument in this section involved note-taking and extraction from various articles and books related to the subject. The data collection method in the second, third, and fourth steps was field-based, and the data collection instrument in these steps was a questionnaire. The data analysis method in the first step was the qualitative content analysis approach. In qualitative content analysis, efforts are made to identify and extract content categories present in communication messages through open coding, axial coding, and selective coding. Ultimately, by entering the codes into specialized NVivo software, the indicators were screened and finalized through an agreed-upon coding process. In the second step, the Delphi method was used to localize production technology selection indicators in Iran’s steel industry. In the third step, in order to examine the influencing and influenced relationships among production technology selection indicators in Iran’s steel industry, cross-impact analysis was conducted using MICMAC analysis for structural analysis in futures studies. To identify important and key components, both the Direct Method and the Indirect Method were applied. In the fourth step, Interpretive Structural Modeling (ISM) was used to level the production technology selection indicators in Iran’s steel industry. Based on the results, the production technology selection indicators in Iran’s steel industry—including technical and operational risk, production flexibility, digitalization capability and intelligentization through artificial intelligence, product quality, operating cost, availability and sustainability of feedstock, price acceptability and volatility of inputs, and supply chain security of equipment—were identified at Level 1 and recognized as the most influenced indicators in the selection of production technology in Iran’s steel industry.