Presenting a dashboard framework model based on human resource productivity management

Barmaki, Hasan; Faezy Razi, Farshad; Rashidi, Ehtesham

doi:10.22034/jvcbm.2025.484989.1448

Presenting a dashboard framework model based on human resource productivity management

Document Type : Original Article (Quantified)

Authors

Hasan Barmaki ¹

Farshad Faezy Razi ²

Ehtesham Rashidi ³

¹ PhD student in Public Management of Organizational Behavior, Semnan Branch, Islamic Azad University of Semnan Iran.

² Assistant Professor, Department of Industrial Management, Semnan Branch, Islamic Azad University, Semnan, Iran.

³ Assistant Professor, Department of Management, Semnan Branch, Islamic Azad University, Semnan, Iran.

https://doi.org/10.22034/jvcbm.2025.484989.1448

Abstract

Abstract
The aim of this research is to present a management dashboard framework model based on human resource productivity. The present research is applicable in terms of its purpose, and exploratory in terms of its data analysis process. The statistical population includes managers, vice presidents, supervisors, and heads of MAPNA Company, determined by a simple non-random method and the Cochran formula, with a sample size of 267 people; and the data collection tool is a researcher-made questionnaire validated through expert opinions and reliable with a Cronbach's alpha of 0.78; and structural equations were used to analyze the data. SPSS and PLS statistical software were used to analyze the data. The results showed that individual factors affect organizational factors with a coefficient of (0.405) and a t value of (4.051), organizational factors affect managerial factors with a coefficient of (0.233) and a t value of (3.789), commitment affects managerial factors with a coefficient of (0.085) and a t value of (2.298), motivation and satisfaction affect managerial factors with a coefficient of (0.234) and a t value of (4.877), and managerial factors affect human resource productivity with a coefficient of (0.449) and a t value of (4.305); and the calculated GOF value is equal to 0.65, which indicates a strong fit of the model.
Introduction
An organization is a complex ecosystem of services, customers, personnel, equipment, data, and information in which it is produced. In the past, organizational management focused on financial management indicators, but today, integration between organizational intelligence (experts), business intelligence (data types), and competitive intelligence (constant communication with internal and external customers) can be achieved through proper management. This integration provides an opportunity for the organization to have a view of real performance against strategic goals and change in the innovative organization. Access to strategic and timely information is essential for making correct and critical decisions to achieve this (Bach et al, 2019). To exchange such information and management priorities between different levels of operations using intelligent tools, dashboards can be very valuable and effective. Management dashboards are new software systems that help organizations enrich their goals using information and their analysis (Hashemi et al, 2017). Dashboards allow managers to define, monitor, and analyze key performance indicators to align goals and activities, visualize all organizational activities, and create a common display environment between goals and activities for effective and efficient decision-making (Nouri & Motadel, 2022).
Dashboards help managers identify trends, patterns, and visual anomalies in the business, which is important for visual information design. Several different goals are expected from dashboards, including adaptability and consistency, planning and control, and communication (Amin et al, 2022). Dashboards are expected to improve decision-making by enhancing cognition and investing in human cognitive abilities. Hence, interest in dashboards has recently increased, as evidenced by the expansion and increase in dashboard solution providers in the market (Vergo, 2022). Considering the above, the main research question is: What is the HRM dashboard framework model?
Theoretical framework
Organizational dashboard
Organizational dashboard is a tool rich in indicators, reports, and charts that operate dynamically so that managers can observe the organization's performance at any time (Dadseresht et al, 2020). Dashboards are business tools and include a set of performance indicators, key performance indicators, and other business-related information. Key performance indicators basically indicate the degree of success of the business in achieving the organization's strategic goals and are therefore subject to attention and review (Faramaezi et al, 2014).
Productivity
Productivity is the effective and efficient use of inputs and resources to produce or provide outputs. Inputs and inputs are resources such as energy, raw materials, capital, and labor used to create outputs or outputs, which are goods produced and services provided by an organization; in other words, productivity is obtaining the maximum possible profit by utilizing and optimally using labor, human power, talent, and skills, land, machinery, money, equipment, time, space, etc. in order to improve well-being (Torani & Aghaei, 2019).
Barmaki et al, (2024) examined the factors affecting the design of the management dashboard framework with an emphasis on the components of human resource productivity. The research findings showed that after collecting data, conducting interviews, and conducting the Delphi method; the final model was classified into 4 main components, 15 dimensions, and 53 indicators. Among them, group dynamics and team spirit, motivations for success and talent assessment and completion of talent banks from employees, documentation and continuity in learning, creating motivation and success among employees with a weighted average of 4.9 are the most important factors identified.
Mohmedi et al, (2024) studied the presentation of an organizational agility model in order to improve human resource productivity. The results in the qualitative section showed that there are 7 components and 52 indicators in the model. The components include 1- Causal factors (human and organizational factors); 2- Central phenomenon (organizational agility); 3- Strategies (agility strategy); 4- Context and facilitator (agility drivers); 5- Obstacles (human resource management challenges); and 6- Consequences (human resource productivity). The results in the quantitative section showed that human and organizational factors affect organizational agility and thereby improve the level of the organization's agility strategy and, as a result, employee productivity. The results also showed that agility drivers and challenges in human resource management affect productivity through agility strategy.
Research Methodology
The present research is applicable in terms of its purpose, and exploratory in terms of its data analysis process. The statistical population includes managers, vice presidents, supervisors, and heads of MAPNA Company, determined by a simple non-random method and the Cochran formula, with a sample size of 267 people; and the data collection tool is a researcher-made questionnaire validated through expert opinions and reliable with a Cronbach's alpha of 0.78; and structural equations were used to analyze the data.
Research Findings
SPSS and PLS statistical software were used to analyze the data. The results showed that individual factors affect organizational factors with a coefficient of (0.405) and a t value of (4.051), organizational factors affect managerial factors with a coefficient of (0.233) and a t value of (3.789), commitment affects managerial factors with a coefficient of (0.085) and a t value of (2.298), motivation and satisfaction affect managerial factors with a coefficient of (0.234) and a t value of (4.877), and managerial factors affect human resource productivity with a coefficient of (0.449) and a t value of (4.305); and the calculated GOF value is equal to 0.65, which indicates a strong fit of the model.
Conclusion
The present study was conducted with the aim of presenting a management dashboard framework model based on human resource productivity. The results of this study are consistent with the results of Barmaki et al, (2024), Mohmedi et al, (2024), Salgado et al, (2022), Nouri & Motadel (2022), Abbasi (2021), Vázquez-Ingelmo et al, (2020), Bach et al, (2019), Munthe et al, (2019), Hashemi et al, (2019), and Hashemi et al, (2018). Vázquez-Ingelmo et al, (2020) showed that these eight criteria: 1. Planning for human resources, 2. Organizing human resources, 3. Hiring human resources, 4. Human resources salaries and rewards, 5. Human resources safety and health, 6. Personal characteristics of the HR manager, 7. Management skills of the HR manager, and 8. Personal abilities of the HR manager are introduced as the most effective factors on decision-making processes in learning ecosystems.
According to the results of the research, the following suggestion was made:
In career development, by eliminating the slowness of repetitive tasks and operations, an attempt is made to give more variety to the job. Personal or career development is an ongoing process of assessing the educational needs of each individual and planning to meet these needs. This process helps employees to reflect on their knowledge, performance or success; and plan their personal and educational progress.

Keywords

Management dashboard

Productivity

Human resources

Organizational factors

Management factors

Subjects

Human resource management

Abbasi, K. (2021). Investigating the effect of establishing a management dashboard on gaining an organization's competitive advantage through SWOT matrix analysis and competitive profile matrix (case study of the Urmia Municipality's Construction and Regeneration Organization). National Conference on Organization and Management Research. SID. https://sid.ir/paper/900475/fa (In Persian).

Alizadeh Sani, M., & Tabibi, M., & Nazari, R. (2018). Key indicators of human resources dashboard in educational and research organizations, Second International Conference on Management and Business, Tabriz, https://civilica.com/doc/828316. (in Persian).

Amin, M.M., & Sutrisman, A., & Dwitayan, Y. (2022). Single Page Application for Business Intelligence Dashboard. Conference: 5th FIRST T1 T2 2021 International Conference (FIRST-T1-T2 2021). DOI:10.2991/ahe.k.220205.055

Barmaki, H., & Faezy Razi, F., & Rashidi, E. (2024). Factors affecting the design of the management dashboard framework with an emphasis on the components of human resources productivity. Management and Educational Perspective, 6(3), 21-38. doi: 10.22034/jmep.2024.446695.1329

Bach, K., & Marling, C., & Jarle Mork, P., & Aamodt, A. (2019). Design of a clinician dashboard to facilitate co-decisionmaking in the management of non-specific low back pain. Journal of Intelligent Information Systems 52(2). DOI:10.1007/s10844-018-0539-y

Dadseresht, S., & Kiamanesh, F., & Baghernejad, S.S., & Amini Siahkolroudi, A. (2020). Dashboard Development in Business Intelligence-Based Organizations, Seventh International Conference on New Findings in Science and Technology with a Focus on Science in the Service of Development, Tehran, https://civilica.com/doc/1039010. (in Persian).

Eckerson, W. W. (2011). Performance dashboards: measuring, monitoring, and managing your business. Hoboken, N.J: Wiley.

Hashemi, S. A. A., & Alvedari, H., Daraei, M. R., & Razini, R. (2019). Designing a Model for Assessment of Organizational Dashboard Development with Business Intelligence Logic in Organizations Case study: Supplying Automotive Parts co. (SAPCO). Strategic Management Thought, 13(1), 295-320. doi: 10.30497/smt.2019.2559. (in Persian).

Hashemi, S.A.A., & Al-Odari, H., & Haina, M., & Razini, R. (2018). Designing a model to evaluate the development of an organizational dashboard with business intelligence logic in organizations. Smart Business Management Studies, 6(21), 93-130. doi: 10.22054/ims.2018.8514. (in Persian).

Mohmedi, A., & Zolfaghari, R., & Gholam hossein zadeh, M. (2024). Presenting the agility model of the organization in order to improve the productivity of human resources. Management and Educational Perspective, 6(3), 314-335. doi: 10.22034/jmep.2024.474819.1409. (in Persian).

Munthe, A.R.S., & Baswardono, W., & Satria, E. (2019). Designing the HRIS digital dashboard model using a CBHRM approach. Journal of Physics Conference Series. 1402(2):022081. DOI:10.1088/1742-6596/1402/2/022081

Nazarpouri, A. H., & Mohamadyari, Z., & Biranvand, N. (2022). Investigating the Effect of Marketing Dashboard Development on Organizational Competitiveness by Explaining the Mediating Role of Marketing Memory and Competitive Intelligence. New Marketing Research Journal, 12(1), 43-68. doi: 10.22108/nmrj.2021.129835.2511. (in Persian).

Nikkhah, Y. (2022). The mediating role of organizational agility in the relationship between outsourcing and organizational productivity in gas company. Journal of value creating in Business Management, 2(2), 16-33. doi: 10.22034/jbme.2022.335002.1017. (in Persian).

Nouri, R., & Motadel, M. (2022). Provide management dashboard design framework, application of QFD in intelligent business systems. Business Intelligence Management Studies, 10(39), 291-328. doi: 10.22054/ims.2021.62775.2025. (in Persian).

Otoo, F.N.K. (2019) "Human resource development (HRD) practices and banking industry effectiveness: The mediating role of employee competencies", European Journal of Training and Development, Vol. 43 Issue: 3/4, pp.250-271, https://doi.org/10.1108/EJTD-07-2018-0068

Rouhani, S., & Rabi'i Sawoji, S. (2019). Design and implementation of organizational dashboards, (1st ed.). University of Tehran Publishing Institute.. (in Persian).

Torani, H., & Aghaei, V. (2019). The role of strategic planning dimensions on organizational productivity. Management Journal on Organizational Training, Year 8, Number 1, pp. 61-87.. (In Persian).

Vázquez-Ingelmo, A., & García-Holgado, A., & García-Peñalvo, F.J., & Therón, R. (2020). A Dashboard to Support Decision-Making Processes in Learning Ecosystems: A Metamodel Integration. Conference: ESSE 2020: 2020 European Symposium on Software Engineering. DOI:10.1145/3393822.3432326

Velcu, O., & Yigitbasiouglu, O. (2010). Dashboards adoption in the business controller's decision making process. In 7th International Conference on Enterprise Systems, Accounting and Logistics, Athens, Greece

Vilarinho, S., & Lopes, I., & Sousa, S. (2017). Design Procedure to Develop Dashboards Aimed at Improving the Performance of Productive Equipment and Processes. Procedia Manufacturing, 11, 1634-1641. https://doi: 10.1016/j.promfg.2017.07.314