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ITHappens Thesis Series: Rick van Gisbergen

The world we live in is becoming more data-driven. A lot of data is collected from smartphones and smart devices. Besides, online a lot of data is collected as well with for example cookies. The collection of valuable data enables companies to use this as an opportunity to gain more insights and make better organizational decisions (Li et al., 2022). This article is a summary of my master thesis for Information Management at Tilburg University. In my research, I explored potential factors involved in the effect of Big Data on decision-making outcomes. I developed a framework based on the outcomes of this exploration. The goal of this article is to inspire other researchers and students to research the topic of Big Data and organizational decision-making.

About the author

This article is based on the outcomes of the master thesis of Rick van Gisbergen (the author of this article). I wrote my thesis for the Master of Information Management at Tilburg University. I also was an active member of Asset | SBIT at the time I wrote this article.

Why this topic?

I chose this topic because he has been interested in the topic of Big Data since high school. In a course called informatics, I was introduced to the concept of Big Data. Furthermore, during my bachelor of Industrial Engineering, I expanded my knowledge of data analytics and use cases in production- or logistics firms. During the Master of Information Management, I got the chance to research the topic of Big Data and data analytics in organizational decision-making. Because of his long interest in the topic, I chose to delve into this topic.

If you are looking for a suitable thesis subject or want to gain some inspiration, then also have a look at ITHappens’ guide to the ‘finding a suitable thesis subject article‘!

The use of Big Data for increasing Decision Quality

The importance of data-driven decision-making for gaining a competitive advantage is widely recognized by organizations, as evidenced by studies such as Barney (1991) and McAfee & Brynjolfsson (2012). While nearly half of firms report making better decisions with data-derived insights, only 25% claim improved organizational results through data analytics implementation (Phillipps, 2013). The challenges lie in organizations’ limited understanding of data analytics, hindering their ability to gain unique insights and improve outcomes (Wamba et al., 2017). Deloitte’s (2022) worldwide survey reveals that almost 90% of respondents have a suboptimal operating model for providing effective analytical insights, emphasizing the need for organizational improvements.

Interested in how analytics in organizations work? Have a look at how advanced analytics are used in businesses at ITHappens’ article about ‘The Astonishing State Of Advanced Analytics‘.

Bahrami et al. (2022) and Ghasemaghaei & Calic (2019) found that the volume of data positively influences analytical insights and decision quality. The concept of Big Data, characterized by volume, variety, velocity, veracity, and value, requires effective utilization to generate insights. However, challenges arise in processing massive datasets, necessitating more complex approaches like machine learning (Ghasemaghaei & Calic, 2019; Mikalef et al., 2019). The use of Big Data alone is not a solution; it depends on factors such as data quality, data diagnosticity, and big data analytics capabilities (See figure 1). Data diagnosticity, indicating data-derived insights’ validity, reliability, and interpretability, positively influence decision quality (Grange et al., 2019). Additional mediating factors, such as data analytics capabilities, contribute to the relationship between Big Data Utilization and decision quality (Ghasemaghaei et al., 2018; Li et al., 2022). However, research gaps exist, urging further exploration of potential mediators and the need for case studies.

The adoption of Big Data analytics across industries is growing, promising insights and improved decision-making. Yet, organizations face challenges in fully leveraging Big Data’s potential due to insufficiently explored mediating factors (Abbasi et al., 2016; Li et al., 2022). While existing research acknowledges the positive correlation between Big Data Utilization and Decision Quality, the specific factors mediating this relationship remain underexplored (Ghasemaghaei & Calic, 2019; Li et al., 2022). The study’s purpose is to fill this knowledge gap through exploratory research within Netherlands-based commercial firms, contributing insights specific to a developed country context. By uncovering mediating factors, the research aims to enhance decision-making theories, provide practical insights for organisations, and propose a conceptual model for optimal Big Data Utilization.

A diagram of data analysis

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Figure 1 initial conceptual framework

Results: new factors

The results, drawn from a comprehensive evaluation and discussion, reveal several factors not previously identified in the literature (See figure 2). Factors such as Data Integration and Decision Acceptance are discovered to mediate the relationship between Big Data Utilization and Decision Quality. The research acknowledges the importance of factors like Data Stewardship, Situational Disruptiveness, Decision-maker Personality, and Decision-making Process Quality, which either directly or indirectly influence decision-making quality.

The study compares its findings with existing literature and identifies similarities and differences. Notably, the research aligns with Janssen et al. (2017) on the influence of data governance, with Data Stewardship considered a part of data governance. Similarly, the importance of integrating and standardizing various data sources, highlighted by Janssen et al. (2017), aligns with the study’s outcome that Data integration affects the capture of valuable information, contingent on the presence of Technical Skills. Moreover, the research supports the Technology Acceptance Model’s proposition by Venkatesh & Davis (2000) and Venkatesh & Bala (2008) regarding the role of Data Quality in building trust and acceptance of data-derived outcomes among decision-makers. Additionally, the study concurs with Garmaki et al. (2023) on the significance of a data-driven culture, linking Decision-maker Personality to Decision Acceptance.

However, the research contributes novel insights by uncovering factors not previously explored. Data Stewardship is identified as contributing to improved Data Quality through careful data integration. Situational Disruptiveness is revealed to negatively impact both Data Quality and Decision Quality, emphasizing the challenges posed by unpredictable environments. The integration of various data sources is shown to enhance Contextual and Representational Data Quality, with Technical Skills facilitating this integration. Lastly, Decision Quality is linked to the effectiveness of the decision-making process, emphasizing the importance of a high-quality decision process. In conclusion, the study aligns with existing literature while making valuable contributions, fulfilling its goal of exploring mediating factors between Big Data Utilization and Decision Quality.

A diagram of data processing

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Figure 2 new conceptual framework

How does this contribute to the academic and business world?

The current study is a valuable addition to the existing literature on decision-making and Big Data, particularly by conducting exploratory research into the mediating factors between Big Data Utilization and Decision Quality. While the focus is exclusively on commercial companies based in the Netherlands, the study highlights the potential for these organizations to enhance their data analysis and decision-making processes based on the research findings. It emphasizes the context-specific nature of the results and cautions against making generalizations for different contexts.

Want to read the thesis? Or do you have any questions regarding the thesis subject? Send a message to me on LinkedIn!


Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big Data research in information systems: Toward an inclusive research agenda. Journal of the association for information systems, 17(2), 3.

Bahrami, M., Shokouhyar, S., & Seifian, A. (2022). Big Data analytics capability and supply chain performance: the mediating roles of supply chain resilience and innovation. Modern Supply Chain Research and Applications, 4(1), 62-84.

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of management, 17(1), 99-120.

Deloitte. (2022). Global IDO Survey Report 2022 (FY21/22 Report).

Garmaki, M., Gharib, R. K., & Boughzala, I. (2023). Big Data analytics capability and contribution to firm performance: the mediating effect of organizational learning on firm performance. Journal of Enterprise Information Management.

Ghasemaghaei, M., & Calic, G. (2019). Does Big Data enhance firm innovation competency? The mediating role of data-driven insights. Journal of Business Research, 104, 69-84.

Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. (2018). Data analytics competency for improving firm decision-making performance. The Journal of Strategic Information Systems, 27(1), 101-113.

Grange, C., Benbasat, I., & Burton-Jones, A. (2019). With a little help from my friends: Cultivating serendipity in online shopping environments. Information & Management, 56(2), 225-235.

Janssen, M., Van Der Voort, H., & Wahyudi, A. (2017). Factors influencing Big Data decision-making quality. Journal of Business Research, 70, 338-345.

Li, L., Lin, J., Ouyang, Y., & Luo, X. R. (2022). Evaluating the impact of Big Data analytics usage on the decision-making quality of organizations. Technological Forecasting and Social Change, 175, 121355.

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big Data: the management revolution. Harvard business review, 90(10), 60-68.

Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big Data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276.

Phillips, T. (2013) The analytics advantage we’re just getting started.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.

Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big Data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.

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