What are the Cognitive Elements in Strategic Thinking: A comparative study of students and professionals in Computer Science and Engineering

Abstract: Research Purpose The concept of strategic thinking is vague in the previous literature, and we lack an understanding of what strategic thinking is. The purpose of this thesis is to contribute towards the understanding of the cognitive elements or components in strategic thinking. To fulfil this purpose, we aim to collect survey and interview data from students studying Computer Science and Engineering at Lund University, and professionals with corresponding majors with several years’ work experience in industries. Research Questions 1. What cognitive elements are identifiable in the explanations of strategic thinking of students studying Computer Science and Engineering? 2. What cognitive elements are identifiable in the explanations of strategic thinking of professionals with a similar educational background with the students? 3. Are there any similarities or differences between the cognitive elements that identified in students and professionals? Methodology This study is based on a combination of deductive and inductive approach. Further, this multi-method study involves survey, interview and a computerised text analysis tool- Pertex. In the first step, students studying Computer Science and Engineering and professionals with corresponding majors were asked to write a text about strategic thinking process that leads to an important decision in the uncertain situation. The participants’ texts were analysed by Pertex. Afterwards, structured interviews including general and specific questions were conducted among those participants. The interview data were analysed by content. Findings For students studying Computer Science and Engineering, analytical, creative elements as well as a new element - iterative are identifiable in their explanations of strategic thinking. Reflective and integrative elements were ranked the most important elements in strategic thinking as the analytical element in interview question two, but were not identified in students’ Pertex and interview question one analysis directly. For professionals with the corresponding majors of students, analytical, creative, flexible, future-oriented, holistic, process-oriented, reflective, synthetic, visionary elements, as well as two new elements - iterative and interactive are identifiable in their explanations of strategic thinking. Regarding the similarities between students and professionals, the analytical element has been identified as an important element in both students and professionals’ explanations of strategic thinking, and the intuitive element has been identified as an unimportant element in both students and professionals. Creative and iterative elements are identifiable in both students and professionals. Regarding the differences, professionals demonstrate a more complex understanding of many elements in strategic thinking, whereas students have a simplified understanding of strategic thinking, only focusing on a few elements. Flexible, future-oriented, holistic, process-oriented, synthetic, visionary and interactive elements are identifiable in professionals in this study, but not in students. Practical Implications Practical Implications This study sheds light on the cognitive elements of strategic thinking identified in computer science and engineering background students and professionals. Further, this study serves as the cornerstone of the ongoing research project on strategic thinking at Lund University School of Economics and Management. Moreover, this study provides value to students and professionals who want to understand and develop their strategic thinking. For organisations, it might be beneficial for universities to develop the curriculum as well as for companies to develop employees’ strategic thinking. Limitations Our research has identified certain limitations. Firstly, there is little literature on strategic thinking on Computer Science and Engineering. We could also have assessed a wider pool of students and professionals in Computer Science and Engineering if we are not constricted to ten weeks. Further, we only analysed the survey text by Pertex. There is no comparison between different text-analysis tools analysing the same text. We also see this as a limitation. Additionally, even though the clusters are automatically generated by Pertex, there is some degree of subjectivity when labelling the clusters. Finally, the language conducted in the survey and interview in English, which might have some impact on the accuracy in expressing their meaning since most participants are Swedish and Chinese.