# Data-Driven Decisions in Smart Cities: A Digital Transformation Case Study
## 3 Methodology, Material, and Methods
### Materials and Methods  
Due to the engineering nature of this research, we followed a design science approach to help achieve practical organizational objectives by creating and evaluating a purposeful IT artifact [33]. ADR consisted of four stages, each anchored with a set of seven principles, as shown in Figure 2.  
* Stage 1: Problem Formulation  
The first stage of a researcher's problem is perceived or predicted based on business needs. Research opportunities are designed based on existing theories and techniques, and this phase is based on two different principles:  
(1) Practice-inspired research and (2) theory-ingrained artifact. In this stage, the relevance of the research question, \"How does BDA, as a data-driven decision-making enabler, influence decision-making in smart cities?\" was set from practical and theoretical perspectives. From a practical standpoint, one of the critical objectives of Nafeza, as an enterprise system, is to optimize the overall clearance time to the minimum possible level. Decisions and actions to reduce the clearance time must be based on factual information derived from system-generated data. Looking at Nafeza as an optimization enterprise system makes it share the objectives of a broader class of similar cases (e.g., typical supply chain systems). On the other hand, the theoretical foundations of data analysis (descriptive, diagnostic, predictive, and prescriptive) generally aim to elicit information that serves decision-making purposes. To quantify the influence of relying on Nafeza data analytics on the efficiency of decisions, we relied on measuring three statistical measures: the clearance time, the number of smuggling cases, and the number of legal violations. These three measures were calculated before and after using Nafeza analytics. These three measures were chosen within the scope of this article because they reflect two important factors in evaluating the efficiency and transparency of trade across border procedures between countries. * Stage 2: Building, Intervention, and Evaluation (BIE)  
The second phase of ADR used stage 1 framing problems and theoretical assumptions to generate the initial design of IT artifacts. IT artifacts were further shaped by the use of the organization and the subsequent design cycles as iterative interweaves building, intervention, and evaluation (BIE). A vital feature of this phase was the identification of  
Figure 2: Action design research stages and principles [17]. novelty resulting from the artifact design of the organization's intervention. This difference highlights two key decisions affecting the ADR research and design team, either an IT-dominated BIE or an organization-dominated BIE. This phase was based on three different principles:  
(1) Reciprocal shaping, (2) mutually influential roles, and (3) authentic and concurrent evaluation. ADR stages features various stakeholders' high degree of participation (including the three levels of strategic, operational, and public decision-makers). The Nafeza BI analysis team and end-users participated in the ADR process. Nafeza's data analytics component aims to support the three levels of decision-makers with fact-based information to improve the overall performance of the clearance process. Since clearance time is the common criterion between the three levels of decision-makers, it was considered the primary criterion for measuring the impact of this information on decision-making. This stage involved three different cycles, as shown in Figure 3.  
BIE Cycle 1 (IT-dominated)  
This stage involved all the necessary preparatory work for data analytics, such as the general design of system structure, defining the primary data source (mainly Nafeza operational database and historical data), the initial design of the data marks and cubes, and the ETL tools. BIE Cycle 2 (Organization-dominated)  
In addition, the development of the needed dashboards was included in this cycle.