Introduction
Chapter 1 introduces its central thesis by describing the background of the issue and the main purpose. It also offers the research questions and objectives based on the thesis. In addition, it discusses the main research contributions and highlights the practical value.
The contemporary business world is a highly competitive and dynamic environment characterized by multiple actors struggling to gain a competitive advantage. In addition, various factors influence organizations’ work, such as the state of the market, competitors’ performance, current demand, or customers’ needs, which means that the decision-making process becomes more complex and demands improved analytical skills. HR activities are associated with detailed information about employees and their individual and professional experiences.
Many organizations face problems related to data processing and find it challenging to make strategic decisions and achieve the current goals. Transforming HR data into meaningful and useful information is a time-consuming process (Tang et al., 2021). Data visualization tools appear effective and may help enhance processes fundamental to the business world. For this reason, the project focuses on researching data visualization tools and their value in demonstrating information and improving analytical processes. This involves images, charts, and maps to reflect on particular data portions and make them easier to comprehend (Patel, 2021). These tools enhance the understanding of trends, concepts, and patterns aimed at improving analytical outcomes.
Dashboards are viewed as useful data visualization tools reflecting on stakeholdersâ KPIs, which can be useful in decision-making. Given this research spectrum, the proposed study analyzes the area of HR data. The significance of the research is due to the increased practical utility of this tool and a variety of advantages associated with strategic planning and decision-making. One of the reasons explaining the need for such a study is the necessity to offer effective tools to manage big data and ensure they are processed correctly. Constantly increasing information volumes justify the need for these tools. In addition, better HR management is also associated with higher success rates, meaning that research is in-demand.
Problem Statement
Often, HR specialists have to deal with issues that are not complex in nature and associated with their daily tasks. They are in control over employee performance outcomes, measuring workersâ KPIs, and making decisions necessary for the companyâs development. However, HR specialists might fail to use fair reward practices and provide bonuses, promotions, training sessions, and resignations. This may happen due to various factors, for instance, the growth in the workforce, increased task complexity, and big data portions that have to be processed to accomplish positive outcomes (Patel, 2021). Given these conditions, HR specialistsâ activities require many efforts to be performed in accordance with the existing standards and corporate strategies. An employee from this department has to collect and process data extracted from different sources and including various indicators to assess, which is challenging and time-consuming. In such conditions, HR specialists need many competencies essential for performing these activities and transform them into valuable units that may help create a background for a firmâs future success (Patel, 2021). Nonetheless, the lack of available processing and analytical tools can limit the possibilities of successful analysis and cause mistakes in the evaluation process.
The existing academic background proves that utilizing specific analytical algorithms may contribute to simplifying tasks and attaining better outcomes. Data visualization tools can help enhance decision-making linked to the HR sphere (Gupta, 2019). Moreover, these instruments perform a beneficial function in the HR process and its enhancement. The issue of data processing may be resolved by applying visualization instruments because these tools have a favorable impact on structuring and demonstrating information, as well as its interpretation (Kunder & Urolagin, 2021). At the same time, creating a visualization is associated with the need to understand the current state of affairs, available data, and mechanisms to structure it (Gupta, 2019). Given these nuances, the current project also discusses the issue of designing and implementing useful dashboards that can assist HR managers in strategic planning and decision-making.
Research Questions
While taking into account the information provided above, the following two main research questions are set:
- Q1: To what extent does visualization influence decision-making in the HR sphere?
- Q2: What steps are crucial to building a reliable dashboard that can enhance decision-making and strategic planning?
The presented questions may contribute to understanding the stated problem. To answer them, it is essential to review the existing issue of data processing, which is relevant to HR specialistsâ activities due to large volumes of data to take into account. The analysis will also help determine the efficiency of visualization with regard to decision-making, as well the steps needed to perform a reliable dashboard.
Research Objectives
Given the research questions formulated above and the nature of the problem under investigation, the following objectives are introduced:
- To find insight from big data.
- To investigate the literature review data visualization.
- To deliver efficient and effective data visualization with all needed information. Executive Master in Information Systems (EMIS).
- To outline the positive effects of data visualization tools on decision-making, planning, and action-taking.
The given research objectives will help enhance the current understanding of the problem and acquire several benefits. First, a better vision of problems HR managers face in their work will lead to an improved idea of how to resolve them. Second, proving the positive correlation between data visualization and decision making, it is possible to create the basis for the successful integration of these tools into the work of companies and organizations. Finally, by visualizing the information, it is possible to improve HR managers and employee performance and help organizations, employing this visualization to increase their effectiveness and attain various goals.
Research Contributions
This research is vital as it contributes to various fields which basically rely on the use of data visualization and decision-making techniques to enhance the HR field. The major fields that will benefit from this research entail the public health field and the information technology field. The adoption of data visualization tools and efficient decision-making techniques by the HR team in this field is essential to realize high results and quality outcomes both in the health and technology field.
Additionally, this research will benefit the Information Technology (IT) Field. It contributes to this field by providing a framework that drives Personas from audience conversations on social media. The framework proposes different machine learning models and statistical text analysis algorithms. This framework could be modified and used in other fields that aim to extract the Persona from audience conversations, such as the IT or business field.
Research Design and Methodology
This research paper will fully analyze the various techniques, methods, and tools that are vital in decision-making and visualization in the HR sphere. Additionally, the research will utilize quantitative methods, such as interviews, questionnaires, and the Delphi method, whereby statistics will be employed in data analysis. Quantitative research entails a description and analysis of the available data and phenomenon using more than a single variable (Glegg et al., 2019). To gather and analyze data, the project requires a combined qualitative-quantitative methodology. During the analysis phase, qualitative methods for analyzing data from multiple sources will be employed, as well as approaches for creating data visualization dashboards (Zhong, 2017). It will aid in the identification of relevant elements related to the problem, which will be used in further study. Second, qualitative approaches will be used to collect data via surveys, observations, and interviews during the analysis phase.
Creating visualization graphs to give HR professionals a visualization tool is part of the design phase. Simultaneously, the assessment phase will be marked by the use of a quantitative technique to evaluate the available visualized dashboards in order to identify their efficacy and pick the best one. Microsoft Power BI is often regarded as a collection of different connectors, apps, and software services that are synchronized to convert data into cohesive, visually immersive, and interactive dashboards. This is performed by combining techniques, processes, structures, and technologies for turning raw data into meaningful and valuable information for better strategic, tactical, and operational insights and decision-making. The design and analysis of different dashboards will be reviewed in order to determine the best presentation for HR needs and to establish a standard criterion for dashboard creation. Pre-processing and data collection from surveys and interviews will be produced for the study and processed so that it may be visualized in a useful and understandable way. In the development phase, several reports and dashboards will be offered.
The Delphi Method
The Delphi method is often associated with the outcome and likelihood of a certain event happening in the future. Usually, a group of professionals engages in the exchange of views, and each of them gives assumptions independently and estimates to a facilitator who then reviews the issues and data. The method also involves anonymity which allows each expert to present their views and opinions freely, thus encouraging an act of openness and avoiding admitting errors by revising the earlier forecasts. In this research, the Delphi technique was implemented using the following steps: Initially, a facilitator who is familiar with data collection and research was chosen to work on all the views that were to be provided by the experts. Later on, a panel of experts involved in activities was identified. The panel of the experts involved customers and other reliable experts from the organization. The experts were then allowed to discuss the problems and provide their anonymous comments on the data visualization and decision-making situation within the HR sphere.
Interviews and Questionnaires
To gather information for the research, the researchers decided to use a questionnaire and also conducted interviews with the HR personnel to evaluate the significance of data visualization in their field. In a polite way, the researchers approached all the respondents and asked for permission if they could have an interview with them. They then prepared a set of questionnaires while making sure that they were brief and consisted of choices for easier answering and time-saving. The answers provided aimed at identifying the significance of data visualization and dashboard in the HR field, especially in enhancing decision-making.
Chapter Summary
This section describes the general research design and methodology of the thesis. It involves various phases that are vital in enhancing the research to realize effective outcomes. For instance, a literature review is carried out to investigate other related studies and define the research gap. Additionally, a study on the current techniques used to analyze the behaviors of social media users is conducted. Another vital step that is carried out in the research is the Persona Skeleton Design. This step aims to design the Persona skeleton or template by investigating what information should be included in the template. Two phases are applied in this step to effectively design the skeleton. In phase 1, a thorough review is conducted to determine the items that public health experts need to understand about the audience to design a relevant message. In phase 2, a modified Delphi is conducted to reach a consensus on the inclusion of these items into the Persona template and evaluate the designed template.
Chapter 3 shows more details about the methods used in this phase. Consequently, the research will involve a vital step involving framework development. Upon designing the template, we developed a framework that extracts the audience Persona from their conversations on social media. The framework involves different analytical techniques such as natural language processing, clustering algorithm, and statistical text analysis. Chapter 4 shows more details about the used methods in this phase. The research then carried out a Framework Experiment to realize effective results that can be implemented in a real-life situation. After developing the framework, it was implemented in a real case study on the Saudi audience during COVID-19 on Twitter. The output of this phase is different Personas that represent the Saudi audience during COVID-19.
Chapter 5 shows more details about the used methods in this phase. Both quantitative and mixed methods will be used for internal and external evaluations, respectively. An internal evaluation will also be carried out to evaluate the accuracy of the designed models using performance measurement methods such as precision and recall. The evaluation is conducted during the framework experiment in chapter 5. In addition, an external evaluation will be conducted to evaluate the persona by its end-user, which is, in our case, health communicators. This evaluation was conducted during the Delphi study in chapter 3, as the experts evaluated the Persona and designed more than one version. Since the extracted data from Twitter is publicly available, this research is exempt from ethical committee approval to ensure effective research ethics which are in accordance with the required set of rules. However, researchers will ensure the anonymity of the studied users.
Literature Review
In chapter 2 of this research paper, a background of the main concepts of this thesis is provided. Much focus is on various sources related to the concepts of data visualization, dashboard use, and decision-making in the HR field. Moreover, it discusses the related studies and highlights the current research gap, which, in turn, helps cover the crucial aspects of the topic and address relevant findings.
Introduction
This chapter provides a general background about the main concepts and topics available for this thesis. Moreover, it discusses the recent related studies and highlights the current research gap. The following subsection provides an overview of health communication on social media in general and during pandemics. It also discusses the introduction of the health marketing concept and the role it plays in enhancing the communication process. Moreover, it provides a description of the Persona approach and how it has been adopted in health and other fields. Finally, it presents and discusses the recent studies that contribute to both fields and highlights the current research gap that exists. Table 1 demonstrates a summary of the studies in the field of data visualization and its application and how these tools are used for BI. In Appendix A, a full list of references is analyzed.
Table 1. Summary of the studies.
Apart from the general use of data collection and visualization tools in business, there is a plethora of research on the topic of data in HR. Table 2 below presents the summary of the literature on the use of these data collection and visualization tools, including dashboards in HR. The majority of the studies are qualitative and assess the challenges that HR managers face when using dashboards. Since this study is focused on the application of a dashboard as a data visualization tool, the literature review was also summarized based on whether or not it contains an example of a dashboard.
Table 2. Data management tools in HR literature summary.
The existing body of literature acknowledges multiple problems HR managers face today. Today, HRM performs a more important strategic role while working with more complex issues and solving different problems (Patel, 2021). In other words, the scope of challenges that stakeholders face in their daily tasks increases. HR managers have to analyze and interpret large volumes of data to make all systems work reliably and create the basis for future growth (Patel, 2021). To accomplish these tasks, employees of this profile are in constant need of an evidence base with credible findings. Interviews with stakeholders, as a rule, apply efficient data gathering, data assessment, and dashboards evaluation in interpreting different feedback and performing enhancements results evaluation before delivering a final dashboard. Sometimes, this may need about ten instruments for HR specialists to carry out their duties responsibly and avoid critical mistakes, thus boosting performance outcomes. Therefore, due to the aforementioned reasons, there is an urgent need for such algorithms as visualization tools.
The adoption of specific visualization approaches in the HR field has proved to be vital to avoiding mistakes and boosting outcomes. Mahalle et al. (2022) support this idea and say that using extra approaches is an essential part of specialistsâ work today. Under these conditions, data visualization tools acquire the top priority as one of the possible ways to reduce the mistakes rate and increase the productivity of managers working in this sphere (Gupta, 2019). Employing the methods available at the moment, HR managers can cope with the increased diversity of tasks and be more productive in resolving issues emerging every day and influencing specialistsâ performance.
Dashboards and data visualization tools are becoming more popular in the modern business world. A common specialist has to work with various data portions coming from different sources, which is impossible without using an appropriate method (Nadj et al., 2020). Under these conditions, the discussed tools create the basis for future improvement and increased effectiveness by providing individuals with an opportunity to avoid situations related to over-complexity and address several issues simultaneously (Margherita, 2021). From this perspective, dashboards become a possible solution to the problem of increased information portions and the necessity to process them. In addition, the use of dashboards in the HR field has proved to be significant in ensuring easier delivery of services by the human resource personnel hence boosting efficiency both in the healthcare and the IT industries.
This has led to boosted sales and increased the chances of the organization competing favorably in the global market. Dashboards can be viewed as a potent and flexible data visualization tool allowing HR managers to structure available information and create the most relevant solutions vital for promoting positive change and attaining desired outcomes (Hehman & Xie, 2021). For this reason, the modern business world has become more focused on integrating such approaches into the work of companies as a method to generate a competitive advantage and attain success. Often, the majority of the organizations that have adopted the use of dashboards and visualization tools in the HR field have been associated with effective delivery of services and excellent performance in the long run. The popularity and spread of visualization tools are also explained by their positive influence on decision-making, strategic planning, and action-taking processes. Some scholars note a direct correlation between the use of dashboards and better decision-making (Ma & Millet, 2021). Managers relying on dashboards or similar tools usually demonstrate higher performance and results compared to specialists who disregard this method.
Under these conditions, it is possible to assume that specific visualization tools promote business intelligence and create the ground for making better decisions, which is vital in the modern business world. Such methods are critical for integrating innovative methods as they can be performed by using innovative strategies and devices (Gowan et al., 2022). In such a way, it is possible to conclude that numerous investigators view dashboards as potent tools vital for better decision-making in the HR sphere. Their employment can help to attain better results and avoid mistakes in planning.
Business Intelligence
Large amounts of data are managed and analyzed by organizations. An increasing number of requests, preparations, and customer assistance are placing a strain on the IT industry. To reduce the time spent on ad hoc reporting by analysts, as well as to reduce the amount of time spent on helpdesk support, business intelligence (BI) initiatives must be implemented (StjepiÄ et al., 2021). Making better decisions for a company begins with gathering and analyzing data via the use of business intelligence. In the IT industry, business intelligence encourages offices to focus on their own results.
Using the dashboards, corporate executives may monitor critical performance metrics. There are various types of business intelligence applications that may be used in the workplace, such as forecasting, analytics, dashboards, querying, and data mining (Donaldson, 2021). Organizational roles, authority structures, and visual representations work together to create a business intelligence framework that can fit a business strategy and make it easier to put into action the necessary business intelligence algorithm. A whole BI framework is made up of the framework’s architectural components, according to Watson and Wixom, as cited by Rouhani and Zamenian (2021). To enable the execution of a BI strategy that is scalable, sustainable, and can be integrated with a business’s strategy, major actions must be incorporated into a software development life cycle, for instance, in stages.
Business intelligence includes any technology, economics, politics, or law, as well as trends in the broader social and cultural contexts such as demography. For executives and owners, business intelligence serves as a collection of tools that aid in the decision-making process (Olouasa, 2020). These tools are based on real data that save time in the search for a better strategy and better projects that will provide greater outcomes. Firm intelligence aids in the process of developing methods to improve a business since it stems from a fundamental personality that is focused on change in an organization. Business intelligence includes systems that aggregate, alter, and show composed data from many sources.
Additional definitions are also applied to describe business intelligence. Agiu et al. (2014) view it as the âusage of high-class software or business applications or the use of values to make better decisions for the organizationâ (p. 23). It is also referred to as a set of concepts, methods, and processes to improve decision-making (Dobrev & Hart, 2015). Multiple sources of information are used to provide a better understanding of business dynamics and accompanying impacts.
Visualizations
Data visualization is critical to the entire employee lifecycle, from hiring and retention to onboarding and performance. HR dashboards and visualization have long served as indispensable tools for HR professionals (Sousa et al., 2019). Dashboards can be purchased or compiled individually by using Excel or other common tools. However, HR software needs a human resources management system with customizable dashboards. The advantages of utilizing these tools may help gain useful business information. With innovative technological solutions and a data-driven corporate climate, HR specialists, managers, and employees have to deal with large volumes of data and learn how to use it. The inability to access and evaluate all important data points explains the need to optimize this field.
When it comes to making information accessible, visualization is a natural choice. Additionally, HR teams adopt data visualization techniques to extract and explore data from their data sets (Margherita, 2021). In addition, since the initial wave of unrestrained optimism about the effect of big data, a more ethical approach to data collection has replaced the original wave of unbridled excitement. Analysis models derived from data science processes that are difficult to understand by non-experts are also under growing criticism. Transparency of data is a priority for these groups. As a result, there is an opportunity to examine raw data themselves, counteracting the possibility of biased interpretations (Vellido, 2020). Even if data is error-free, manually crunching it is a time-consuming task. Thus, one needs a system or application that can automate data analysis and visualization procedures. Modern HR technologies allow for moving beyond spreadsheets, PowerPoint presentations, and database reporting tools to more sophisticated methods (Kennedy & Dunn, 2018). A new generation of interactive HR dashboards provides real-time, configurable data visualizations that cover every aspect of the employee lifecycle.
Decisions are made both in the long and short term, depending on this data. Human resource specialists have been in need of data over the last few years (Zhang et al., 2021). The difficulty in interpreting data comes from the fact that it might be large. As a result, it is simpler to understand and visualize data with the help of data visualization tools. Some of the useful instruments in the human resource field entail Zoho Reports, Domo, Microsoft Power BI, Tableau, and many other data visualization solutions available in the market. Human resources analytics may be found in many areas of a business, from recruiting and retaining employees to managing their performance and detecting their satisfaction levels (Santos et al., 2019). This eventually leads to enterprises achieving a reasonable outcome. After learning about the importance of analytics in decision-making, one can conclude that more money will have to be set aside for this area in the coming years.
At the same time, despite the urgency of utilizing visualization tools, many HR professionals are not sufficiently skilled to create these instruments on their own or operate with them professionally. Data visualization in the HR field can be assessed as an invaluable tool when it comes to studying and conveying information (Shet et al., 2021). Well-designed schemes performed with the help of visualization make the necessary information easily accessible, transparent, visually attractive, and engaging to the target audience in a way that traditional texts or number-based presentations cannot. Thus, a dashboard may be described as an instrument that implies creating a visual representation of all HR data points in an easy-to-understand format. By providing the audience with the visual depiction of the most important indicators across the employee lifecycle, these tools help various interested parties, including business owners, leaders, and ordinary employees. The main benefit is an opportunity to analyze and interpret large volumes of data to make informed and reasonable decisions. Additionally, dashboards are a good method to interact with subordinates and transfer the necessary objectives to achieve.
Many businesses have transitioned away from making strategic and tactical decisions, expecting and even mandating that data drive them. Due to the ever-increasing volume, speed, and diversity of data generated in the HR field, organizations now need HRs with analytical skills to filter and extract useful information (Reina & Scarozza, 2021). Big data overload and confusion may be thwarted and inoculated with effective data visualization. Other aspects of data science can also benefit from visualization approaches. In many firms, advanced machine learning and artificial intelligence approaches are utilized with the data obtained from and about their personnel. This practice allows for addressing a number of critical perspectives â performance assessment, increasing engagement, motivating subordinates, and other significant aspects of the HRM process, which, if successfully performed, directly influence market success. As a result, a wide range of tasks to implement explains the need to introduce appropriate tools into the workflow and ascertain their effectiveness in the context of the given parameters for analysis.
Visualization Tools
Microsoft Power Bi
Microsoft Power BI has proved to be a vital tool in business intelligence and data analytics. An organization’s raw data may be turned into meaningful information with Microsoft BI, which is a cloud-hosted business intelligence and data analytics tool (Becker & Gould, 2019). Because it has a wide range of data tools and can connect to a wide range of data sources, it is regarded as a powerful tool as it is easy to use and can save data in Excel. Additionally, its frequent updates make it more adaptable and relevant to new data, has superior visualization capability, and it is relatively inexpensive because it has two versions. Potential drawbacks include: the free desktop version has a restricted storage capacity; it contains a set of pre-created inflexible formulae; while it is possible to design an individual tool, most are stiff.
Whatagraph
Whatagraph is the second data visualization tool to be applied in business settings. Ideas in Whatagraph are brought to life via the use of visual data, such as the strategic banners that are used to communicate a specific piece of information about a product or service (Alugubelli, 2018). The visual banner of the Whatagraph encourages effective management of information transformation into a new dimension through the ease of the Whatagraph planning to reach a greater geographical region. Choosing a graph for a company’s operations that covers a big geographic region makes it a company’s shift in how it handles things. Adding to this, it is more efficient and dependable.
Sisense
A further tool for visualizing data is Sisense, which may be found in visualizations dealing with business applications, analytics, and business intelligence (BI). Sisense works together with other visualization tools to produce the final product (Alugubelli, 2018). Data put into the system may be more effectively visualized for a better user experience. As a result, this visualization tool is more accessible and dependable than other tools since it is inexpensive.
Comparison Table
Table 3 below represents various tools for data collection and visualization and comparison of them based on application, cost, and usability. Evidently, there are three main data visualization tools organizations and HR managers can use: Microsoft Power BI, Sisene, and Whatagraph. From this table, one can conclude that Microsoft Power BI offers the best in terms of tools and a variety of applications in comparison to cost.
Table 3. Tools for data collection and visualization.
The Delphi Method
The Delphi method is one of the methodological approaches that are applied over the course of this study. The Delphi method is an algorithm utilized for forecasting, the basis of which is several rounds of questionnaires distributed to industry professionals (Mezzy, 2021). The uniqueness of the Delphi method is in the fact that information is presented in rounds, each expert answers a set of questions, and then these are summarized. The aggregated results summary is presented to the experts again to gain their collective opinion on the issue, and the questionnaire is distributed again. This process continues until all experts reach an agreement on the issue. The Delphi method is a combination of the expert analysis and wisdom of crowds and is one of the best techniques to research issues where concrete data cannot be collected and where only the expertsâ predictions can be used to assess the current situation. The consensus among the experts is the goal of each Delphi study, as this provides a certain guarantee that the findings are reliable and can be used for forecasting.
There are several types of the Delphi method, which are policy, classical, and decision-making. Policy studies are conducted when there is a need to create a strategy for addressing a defined problem (Mezzy, 2021). Classical studies are conducted when the facilitator wants to make a forecast about the future. Finally, the decision-making studies are completed when there is a need to make an informed decision and only expert opinion and forecasting are available information sources.
The first step in its application is selecting the group facilitator and the set of specialists in the field who will be answering the questions. Depending on whether the study is conducted face to face or using online tools, the facilitator either presents the questionnaire and instructions to the audience or sends them the form and the instructions. The role of the facilitator is in enabling the process, providing the experts with questions and data about research aims, and summarizing the results.
The experts may have varied backgrounds, but they have to work in the same field and answer the questions based on their personal opinion, experience, and forecasts about the future of their industry (Mezzy, 2021). Once the facilitator collects the answers after the first round, they aggregate the answers and send the copies of these and the summary to each expert. The experts are encouraged to provide their commentary and opinions. Once the round ends, the responses are returned to the facilitator, who studies them and makes a decision on whether another round is necessary or the study has found the answer to the research question. The Delphi method study may have an unlimited number of rounds; however, these should be reasonable to ensure that no bias or overthinking affects the results.
References
Agiu, D., Mateescu, V., & Muntean, I. (2014). Business intelligence overview. Database Systems Journal, 5(3), 23-36.
Alugubelli, R. (2018). Visualization for data analytics and data science. Journal of Emerging Technologies and Innovative Research. JETIR, 5(3), 586-594.
Becker, L. T., & Gould, E. M. (2019). Microsoft Power BI: Extending excel to manipulate, analyze, and visualize diverse data. Serials Review, 45(3), 184-188. Web.
Dobrev, K., & Hart, M. (2015). Benefits, justification and implementation planning of realâtime business intelligence systems. Electronic Journal of Information Systems Evaluation, 18(2), 105-119.
Donaldson, W. (2021). Evidence of an archetypal management framework: An exploratory examination of commonality among existing management frameworks and resulting efficacy. Small Business Institute Journal, 17(1), 23-33. Web.
Glegg, S., Jenkins, E., & Kothari, A. (2019). How the study of networks informs knowledge translation and implementation: A scoping review. Implementation Science, 14(1), 1-27. Web.
Gowan, T. A., Horel, J. D., Jacques, A. A., & Kovac, A. (2022). Using cloud computing to analyze model output archived in Zarr format. Journal of Atmospheric and Oceanic Technology, 39, 449-462. Web.
Gupta, V. (2019). An analysis of data visualization tools. International Journal of Computer Applications, 178(10), 4-7. Web.
Hehman, E., & Xie, S. Y. (2021). Doing better data visualization. Advances in Methods and Practices in Psychological Science, 4(4), 1-18. Web.
Kennedy, M., & Dunn, T. J. (2018). Improving the use of technology enhanced learning environments in higher education in the UK: A qualitative visualization of studentsâ views. Contemporary Educational Technology, 9(1), 76-89. Web.
Kunder, L., & Urolagin, S. (2021). Behavioral analysis using gamification and data visualization. In X. Z. Gao, R. Kumar, S. Srivastava, & B. P. Soni (Eds.), Applications of artificial intelligence in engineering (pp. 247-265). Springer.
Ma, Q., & Millet, B. (2021). Design guidelines for immersive dashboards. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 65(1), 1524-1528. Web.
Mahalle, P. N., Shinde, G. R., Pise, P. D., & Deshmukh, J. Y. (2022). Data visualization tools and data modelling. In G. R. Shinde, P. D. Pise, & J. Y. Deshmukh (Eds.), Foundations of data science for engineering problem solving (pp. 49-72). Springer.
Margherita, A. (2021). Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 32(2), 100795. Web.
Mezzy, C. (2021). A Delphi study: Developing a career transition model for professional baseball players (Publication No. 28770378) (Doctoral dissertation, Drexel University). ProQuest Dissertations Publishing.
Nadj, M., Maedche, A., & Schieder, C. (2020). The effect of interactive analytical dashboard features on situation awareness and task performance. Decision Support Systems, 135, 113322. Web.
Olouasa, L. S. (2020). Effect of competitive intelligence strategy on growth of local airlines operating in Kenya [Unpublished doctoral dissertation]. Africa Nazarene University.
Patel, S. (2021). Data warehousing and visualization tools for finance. Gujarat University, 1-5.
Previde, P., Thomas, B., Wong, M., Mallory, E. K., Petkovic, D., Altman, R. B., & Kulkarni, A. (2018). GeneDive: A gene interaction search and visualization tool to facilitate precision medicine. In R. B. Altman, A. K. Dunker, L. Hunter, M. D. Ritchie, T. A. Murray, & T. E. Klein (Eds.), Pacific Symposium on Biocomputing 2018: Proceedings of the Pacific Symposium (pp. 590-601). World Scientific Publishing.
Reina, R., & Scarozza, D. (2021). Human resource management in the public administration. In M. Decastri, S. Battini, F. Buonocore, & F. Gagliarducci (Eds.), Organizational development in public administration (pp. 61-101). Palgrave Macmillan.
Rouhani, S., & Zamenian, S. (2021). An architectural framework for healthcare dashboards design. Journal of Healthcare Engineering, 2021(1964054), 1-12. Web.
Santos, M. Y., Costa, C., Galvão, J., Andrade, C., Pastor, O., & Marcén, A. C. (2019). Enhancing big data warehousing for efficient, integrated and advanced analytics. In C. Cappiello & M. Ruiz (Eds.), International Conference on Advanced Information Systems Engineering (pp. 215-226). Springer.
Shet, S. V., Poddar, T., Samuel, F. W., & Dwivedi, Y. K. (2021). Examining the determinants of successful adoption of data analytics in human resource management â A framework for implications.Journal of Business Research, 131, 311-326. Web.
Sousa, M. J., Pesqueira, A. M., Lemos, C., Sousa, M., & Rocha, Ă. (2019). Decision-making based on big data analytics for people management in healthcare organizations. Journal of Medical Systems, 43(9), 1-10. Web.
StjepiÄ, A. M., PejiÄ Bach, M., & Bosilj VukĆĄiÄ, V. (2021). Exploring risks in the adoption of business intelligence in SMEs using the TOE framework. Journal of Risk and Financial Management, 14(2), 58. Web.
Tang, J., Zhou, Y., Tang, T., Weng, D., Xie, B., Yu, L., & Wu, Y. (2021). A visualization approach for monitoring order processing in e-commerce warehouse. IEEE Transactions on Visualization and Computer Graphics, 28(1), 857-867. Web.
Vellido, A. (2020). The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Computing and Applications, 32(24), 18069-18083. Web.
Zhang, Y., Xu, S., Zhang, L., & Yang, M. (2021). Big data and human resource management research: An integrative review and new directions for future research. Journal of Business Research, 133, 34-50. Web.
Zhong, L. (2017). Using learning analytics to improve instructional support design for online learning. Journal of Educational Technology Development and Exchange (JETDE), 10(2), 25-36. Web.