The level of adoption of analytical tools
Authors:   , Barahona Torres Igor 
Published: September 2023

This PhD thesis is focused on disclosing features that might cause the increase in the use of analytical tools for better decision making. The theoretical part of this research is developed in two phases. At first, an exhaustive literature review was conducted with the purpose of identifying the main features in companies that impact positively the adoption of new analytical tools. This review brought our attention in four key drivers which were the foundation of the theoretical model: management support on data analysis, data-based competitive advantage, systemic thinking and communication outside the company. Secondly, a scale was proposed for classifying companies according with how its analytical capabilities are developed.
The theoretical model and scale required to be validated with data from the real world. Four constructs derived from the model were operationalized in 17 items. An extensive statistical research related with the agreement, convergence, test-retest reliability and factor structure of the dimensions was conducted. Results allowed us to ascertain that our instrument is reliable and valid. Then, the questionnaire was sent to companies located in Barcelona area.
The central part of the research analyzes data obtained from the companies. At first, the statistical engineering, which is interpreted as the link between the statistical thinking (the strategic management) and methods (the day-to-day operations), was adapted as guideline. A set of seven statistical tools were wisely assembled in a sequential order and relevant conclusions were obtained. Later, it was necessary to validate our preliminary conclusions with additional research and make them more robust. A second approach was utilized with this purpose. The evidential reasoning, which is a type of multi criteria decision analysis method, was implemented. Two different approaches lead us to similar results.
At this phase of the thesis unstructured and soft features about the analytical practices were still missing. A complementary approach was needed to include aspects as personal values, beliefs and motivations and identify how they influence on analytical practices of the companies. The laddering methodology was utilized for these purposes. It is defined as a type of in-depth interview that is applied to understand how individuals transform attributes of any given concept into meaningful associations with respect to themselves. Consider this analogy; the data from questionnaires gave us "the picture of forest", then in-depth interviews yielded "the picture of the three".
The last part of the thesis is reserved to provide guidelines to companies interested on increasing their analytical capabilities. Here it is offered a road map composed of five stages. The proposed order is: A company receive its diagnostic and is given a stage in the road map, later guidelines are provided to move the company upwards into the scale. The sequence of diagnostic-guidelines-diagnostic should be repeated until the company reach the highest level in the scale: analytics as competitive advantage.
At the end of the thesis are presented two sets of values and attributes which were found decisive for increasing the adoption of analytical tools. In the first set, three values: honesty, serving the society and leadership impact the statistical thinking (the strategic level) in the company, whereas three attributes: the goal setting, creativity and information from outside are acting on the statistical methods (the operational level). The statistical engineering (the tactical level) establish a link between strategic and operational levels.
All the tools and methods developed in this thesis, including the questionnaire, the scale for ranking the companies, the script for in-depth interviews, the road map for moving upward to higher levels in the scale and its related guidelines, represent an original and helpful toolkit for improving the analytical capabilities in companies.