Cognitive theories suggest that visualization enhances decision-making by creating cognitive shortcuts that facilitate effective thinking and reduce biases. Data visualization is widely used across various projects to improve decision-making, with numerous studies exploring its role in shaping analytical processes. The objective of this research is to investigate the impact of portfolio managers’ familiarity with data visualization on the development of heuristic methods based on expert knowledge and successful decision-making. The study introduces an innovative approach by investigating the relationship between expertise in mental analysis and decision-making, particularly in the context of visualization. To achieve these objectives, relevant literature and questionnaires were utilized. Companies in Iran’s oil and gas sector and high-level infrastructure industries were selected in 2023 using random sampling to ensure diversity in size and type. Hypothesis testing was conducted using SPSS version 27 and Smart PLS 3 software to assess the current and optimal states of the research variables, followed by structural equation modeling for further evaluation. The findings highlight that the association between portfolio managers’ familiarity with data visualization and heuristics methods based on expert knowledge positively influences successful decision-making by portfolio managers and the success of the portfolio.
This research aims to examine the mechanism of visualization in the mental understanding of portfolio managers, investigating the familiarity with portfolio managers with data visualization, explore the performance of heuristics methods and mental shortcuts in decision-making by portfolio managers, and evaluate the effectiveness of implementing data visualization in project-oriented organizations.
This study explores the cognitive heuristics employed by portfolio managers, a subject that has received limited attention in the literature. Studies have shown that individuals often unconsciously avoid deep analytical thinking when making decisions, relying instead on cognitive shortcuts to expedite the process (Pathak et al., (2024). A fundamental aspect of decision-making is the use of these cognitive shortcuts, as individuals tend to minimize cognitive effort and seek efficient methods to streamline decision-making. However, while heuristics can facilitate rapid decision-making, they also introduce potential errors, as noted by Tversky and Kahneman (1974), making their implications a crucial consideration.
Portfolio management is significantly influenced by the human element, including the responsibilities and cognitive tendencies of decision-makers (Behrens & Ernst, 2014). To enhance decision-making, portfolio managers must study the creation of cognitive shortcuts that may facilitate effective decision-making. Portfolio managers must possess more than a fundamental knowledge of data visualization. As Tergan and Keller (2005) suggested, they should use cognitive capacities to enhance decision-making abilities and improve their interpretation of available data. Consequently, it is crucial to understand the determinants that impact the creation of novel approaches rooted in expert knowledge. Therefore, there is a need for investigation and study in the field of cognitive shortcuts based on expert knowledge by portfolio managers. Data-driven decision-making has been considered as a silver bullet by management to better decision making, but cognitive heuristics and biases often test human judgment even with data visualization, leading to suboptimal results. With large amount of data, our brains use heuristics as mental shortcuts. Though helpful in facilitate complex decision-making situation in portfolio projects, it can introduce systematic errors and make biases obscure to impede correct decision-making. Familiarity with visualization of data emerges as a powerful tool in opposing these biases, enhancing clarity, and promoting management decision-making. This study analyzes the impact of portfolio managers’ proficiency in data visualization usage in the decision-making process of portfolio management, as well as its implications on portfolio success.
The theoretical model and conceptual framework of this research are derived from the model proposed by Killen et al. (2020), with further development of their hypotheses. The model is constructed as follows:
Based on the conceptual model, the research hypotheses are as follows:
H1: A positive correlation has been observed between successful portfolio management decision-making and portfolio success (Killen et al., 2020).
H2: Successful decision-making is positively influenced by visualization usage in portfolio management (Killen et al., 2020).
H3a: Decision-makers’ familiarity with visualization positively influences decision-makers success (Kilen et al., 2020).
H3b: There is a positive relationship between familiarity with visualization and the visualization usage on successful decision-making (Killen et al., 2020).
H4a: Heuristic portfolio management decision-making based on expert knowledge is negatively linked to successful decision-making (Killen et al., 2020).
H4b: There is a positive relationship between heuristic decision-making based on expert knowledge and the visualization usage in successful decision-making (Killen et al., 2020).
H5: There is a positive relationship between heuristic decision-making based on expert knowledge and familiarity with visualization (Bohle Carbonell et al., 2016).
This investigation employed a descriptive, survey, and correlational approach to investigate the research topic. To test the research hypotheses, a questionnaire was designed to gather the opinions of the statistical population. The questionnaire, constructed on a seven-point Likert scale, was designed to test and confirm or reject the research hypotheses. The questionnaire was designed using the research of Li (2020) and Bohle Carbonell et al. (2016) as references with adjustments and feedback obtained from experts.
Given Iran’s position as a major oil and gas producer with unique market dynamics, this sector provides an appropriate context for examining how portfolio managers’ familiarity with data visualization influences the development of heuristic methods.
The entered data undergoes examination and analysis in three stages:
(a) The status of variables and descriptive statistics of the participants were analyzed using descriptive statistics tests and determining frequency, percentage, and mean values.
(b) To rigorously test the hypotheses and analyze variable relationships, inferential statistical tests were employed using SPSS version 27 software and Smart PLS 3 software to examine hypotheses and address research variable conditions. This involved elucidating the present and ideal condition of the study variables, determining differences or lack thereof in perspectives through one-sample T-tests, independent and paired T-tests, LSD tests, and regression coefficients for both single and multiple variables. The impact of independent variables and their dimensions on the dependent variable was explored.
(c) The investigation included using a structural equation modeling approach to examine the conceptual model and conduct a model fit test (Kline, 2005; Ullman, 2016). The present study verified the scales by confirmatory factor analysis (CFA) following the guidelines of Hu and Bentler (1998) for evaluating structural equation models. The CFA yielded a comparative fit index (CFI) of 0.95, indicating a good match, and a CFI of 0.9, also indicating a good fit. The acceptability criteria for the root mean square error of estimate (RMSEA) and standard root mean square residual (SRMSR) are below 0.08. The CFA findings were derived using Smart PLS 3 software, including dependent, mediating, independent, and moderating factors.
In order to explore the impact of portfolio managers’ familiarity with data visualization on the development of heuristic methods based on expert knowledge and successful decision-making, a comprehensive approach was adopted. The process began with a thorough assessment of existing literature, analyzing pertinent texts and research works to identify crucial elements and indicators. Expert opinions were solicited via surveys to validate these indicators. Subsequently, the importance of these indicators and their weights were determined with the assistance of experts in the oil and gas sector and related industries in Iran, using the Fuzzy AHP method. Finally, the Fuzzy TOPSIS approach was employed to rank and evaluate the oil and gas sector according to the identified signals. This research leveraged the library research method, targeting a statistical population of 140 project managers and experts in the oil and gas sector, and upper and lower infrastructure industries in Iran. To assess the familiarity of portfolio managers with data visualization and the effectiveness of heuristic methods, components and indicators were initially identified through a review of relevant literature and texts. These indicators were then refined and finalized through expert opinions. Using the FAHP approach, the importance of these indicators and their weights was determined with the help of industry experts. Lastly, the Fuzzy TOPSIS method was applied to prioritize and evaluate the oil and gas sector based on these identified indicators.
The findings reveal that the use of heuristic methods, grounded in expert knowledge and data visualization, has a positive and significant relationship with successful decision-making and project success. Skilled project managers, particularly those specializing in oil and gas projects, make effective decisions leveraging their work experience, visualization usage, and familiarity with it. This leads to enhanced decision-making and improved project outcomes. Upon reviewing theoretical foundations and previous research, several key indicators and sub-indicators were identified and validated through expert opinions. These include:
• Heuristics decision-making: Underlying skills, metacognitive abilities, and innovation capabilities.
• Visualization familiarity: Types of visualizations, standards of visualization, and comprehension of visualizations.
• Visualization usage: Project comparison, evaluation of coherence between projects, synergy among projects, identification of project bottlenecks, and project performance.
Exploring expertise in the field of intuitive (heuristics) decision-making and its impact on decision-making success, particularly in the context of visualization, is a primary strength of the current research.
Contractor 1 achieved the first rank in the oil and gas sector, signifying exceptional performance in its projects and making it a lucrative investment. Investigations revealed that this contractor held training courses on data visualization for its managers and special experts, utilized consultants with extensive experience in portfolio projects, and prepared the report format based on these courses and consultations, implementing them in the ERP system.
In contrast, contractor 5 had the lowest rank in the oil and gas sector. It is recommended for this company to adopt data visualization usage and utilize experienced managers in this field. The study provides new insights into the differences in decision-making approaches of portfolio managers using data visualization and their impact on decision-making and project success. The findings indicate that decision-making is associated with the decision-maker’s familiarity with various visualizations and highlights the intuitive role in decision-making success. By integrating data visualization and leveraging expert knowledge, portfolio managers can enhance their decision-making processes, ultimately leading to successful project outcomes.
The primary limitations of this research include several factors that affected the scope and depth of the study. Time constraints and a lack of cooperation from some users limited the data collection process. Additionally, there was a lack of mental and psychological cohesion and integrity among participants, which may have impacted the consistency and reliability of the findings. Access to comprehensive data and resources was also limited, particularly in the chosen areas of green supplier selection. Another significant limitation was the lack of familiarity with academic research in the central theme of the study at the university level, which may have hindered the depth of analysis. Other limitation was the restriction of SPSS 27 to basic inferential tests (e.g., t-tests, regression) and lacks advanced machine learning capabilities and Limited Smart PLS 3 support for complex longitudinal or multilevel modeling compared to covariance-based SEM tools. Furthermore, the non-standardization of administrative functions and processes at the center posed challenges for maintaining a consistent research timeline.
Future studies could examine the combined effects of innovative and analytical approaches—particularly through data visualization—on enhancing the quality and speed of decision-making in portfolio management. Additionally, research should consider integrating other factors influencing heuristic decision-making, such as judgment in project portfolio decisions, to provide a more nuanced understanding of decision-making processes (Lewis et al., 2023). Furthermore, future investigations could explore the role of hidden decision-making variables, especially in crisis scenarios within portfolio management, to assess their impact on heuristic decision-making. Studies should also evaluate the diversity of portfolio management job positions, alongside expertise and individual preferences, as suggested by Bohle Carbonnell (2016), to determine how these aspects shape portfolio decision-making strategies. An essential methodological recommendation is to ensure that psychological health assessments are completed prior to finalizing questionnaires, as psychological well-being significantly influences decision-making consistency and reliability. Incorporating various visualization categories within the research framework would validate participants’ familiarity, application, and skill level in utilizing data visualization techniques for portfolio decision-making. Future studies could combine R/Python (for robust analytics) with CB-SEM tools for comprehensive analysis. Finally, applying multi-criteria decision-making approaches, such as the SWARA method, could provide complementary insights into optimizing portfolio management strategies. Future research should consider how industry-specific factors such as competition intensity, technological advancements, and market uncertainty interact with data visualization and heuristic approaches to shape successful portfolio decisions.
Referencia:
Farnoudkia, M.H., Khalilzadeh, M. & Bahari, A. The impact of portfolio managers’ familiarity with data visualization on the development of heuristics methods. Cogn Process (2026). https://doi.org/10.1007/s10339-025-01313-5
