Design Science Research Approach Towards the Construction of Competitive Intelligence Process Model in the Era of Big Data

Big Data Analytics (BDA) is considered as one of the most recent suites of innovations that benefits a diverse range of businesses and industries. Advanced software systems significantly reduce analytic time, giving organisations the ability to make quick decisions that help to enhance business performance. Uncovering hidden patterns from large amount of data brought out the popularity of BDA term. These analyses can observe the patterns of consumers and customer's trends and provide meaningful insights for marketing teams within organisations. Accordingly, organisations have realised that they must adopt new technological approaches to market their products to create a competitive advantage. Marketers in different sectors put together valuable customer databases to deliver tailored offers and campaigns. The benefits of BDA have also informed new approaches for businesses to stand out. Identifying the right target customer has become one of the strategic objectives within several organisations. Knowing your customers and browsing their behaviour is a phenomenal competitive intelligence, triggering brand loyalty and customer satisfaction. Having top-notch competition and market intelligence requires powerful capabilities. Most organisations pursue to harness that data and extract value from it. To this end, the question is "how"?<br/><br/>This study aims to explore how BDA can be integrated into the competitive intelligence process to achieve best practice of business performance through an adequate decision-making structure. It looks into the development of a conceptual model that incorporates BDA stages and competitive intelligence processes. The context for this research is focused on real estate businesses in the UAE, nonetheless, the findings from this study can also be generalised to other subject areas. The approach used for developing the model is design science research. A comprehensive thematic literature review was conducted to assess existing models and identify main variables of the competitive intelligence process. The result of the literature review reveals that there did not exist a comprehensive model incorporating the dimensions of BDA and competitive intelligence. The variables and their interactions from the literature informed a proposed conceptual model (CCIP-BDABM), that fills the literature gap and provides solutions for the practice.<br/><br/>Semi structured interviews conducted with leading experts within the intended user community to demonstrate the proposed CCIP-BDABM feasibility and efficacy. Twenty-seven iterative interviews have been conducted aiming to assess and evaluate the model. The outcomes of this study contribute to constructing an artefact model of solutions for adopting BDA in the competitive intelligence process. The real estate industry, in UAE, will benefit from elements, cycles, stakeholder and deliverables as a guidebook of big data investment. Also, the model developed represents conceptual gains for the theory. Moreover, the design science research methodology is historically applied in information systems research projects, however similar to very few existing literatures that adopted design science research, this study also provides a methodological novelty through the application of DSR in an unfamiliar context.

Name dc:type.qualificationname

Doctoral Thesis

Level dc:type.qualificationlevel

Student thesis

Year dc:date.issued

2022

Author dc:creator

• Albahsh, Eman Albash

Advisors dc:contributor.advisor

• Hosseinian Far, Amin
• Sarwar, Dilshad

• Competitive Intelligence (CI)
• Competitive Intelligence Process (CI Process)
• Big Data (BD)
• Big Data Analytics (BDA)
• Decision-Making
• Decision-Makers
• Top Management Support
• Organisational Agility (OA)
• Knowledge
• Dynamic Capabilities
• Competitive Intelligence Environment
• Talent Management
• Marketing Mix
• Design Science Research (DSR)
• Data Collection
• Data Capturing
• Data Storing
• Data Sorting
• Data Analytics
• Data Automation
• Marketing Reports
• Channel Dissemination

Language dc:language

eng

Identifier

oai:pure.atira.dk:studenttheses/4e863ca2-ab6d-49d2-938d-97225ac4655b

OAI identifier oai:identifier

oai:pure.atira.dk:studenttheses/4e863ca2-ab6d-49d2-938d-97225ac4655b