sample of input process output in thesis

How To Make Conceptual Framework (With Examples and Templates)

We all know that a research paper has plenty of concepts involved. However, a great deal of concepts makes your study confusing.

A conceptual framework ensures that the concepts of your study are organized and presented comprehensively. Let this article guide you on how to make the conceptual framework of your study.

Related: How to Write a Concept Paper for Academic Research

Table of Contents

At a glance: free conceptual framework templates.

Too busy to create a conceptual framework from scratch? No problem. We’ve created templates for each conceptual framework so you can start on the right foot. All you need to do is enter the details of the variables. Feel free to modify the design according to your needs. Please read the main article below to learn more about the conceptual framework.

Conceptual Framework Template #1: Independent-Dependent Variable Model

Conceptual framework template #2: input-process-output (ipo) model, conceptual framework template #3: concept map, what is a conceptual framework.

A conceptual framework shows the relationship between the variables of your study.  It includes a visual diagram or a model that summarizes the concepts of your study and a narrative explanation of the model presented.

Why Should Research Be Given a Conceptual Framework?

Imagine your study as a long journey with the research result as the destination. You don’t want to get lost in your journey because of the complicated concepts. This is why you need to have a guide. The conceptual framework keeps you on track by presenting and simplifying the relationship between the variables. This is usually done through the use of illustrations that are supported by a written interpretation.

Also, people who will read your research must have a clear guide to the variables in your study and where the research is heading. By looking at the conceptual framework, the readers can get the gist of the research concepts without reading the entire study. 

Related: How to Write Significance of the Study (with Examples)

What Is the Difference Between Conceptual Framework and Theoretical Framework?

Both of them show concepts and ideas of your study. The theoretical framework presents the theories, rules, and principles that serve as the basis of the research. Thus, the theoretical framework presents broad concepts related to your study. On the other hand, the conceptual framework shows a specific approach derived from the theoretical framework. It provides particular variables and shows how these variables are related.

Let’s say your research is about the Effects of Social Media on the Political Literacy of College Students. You may include some theories related to political literacy, such as this paper, in your theoretical framework. Based on this paper, political participation and awareness determine political literacy.

For the conceptual framework, you may state that the specific form of political participation and awareness you will use for the study is the engagement of college students on political issues on social media. Then, through a diagram and narrative explanation, you can show that using social media affects the political literacy of college students.

What Are the Different Types of Conceptual Frameworks?

The conceptual framework has different types based on how the research concepts are organized 1 .

1. Taxonomy

In this type of conceptual framework, the phenomena of your study are grouped into categories without presenting the relationship among them. The point of this conceptual framework is to distinguish the categories from one another.

2. Visual Presentation

In this conceptual framework, the relationship between the phenomena and variables of your study is presented. Using this conceptual framework implies that your research provides empirical evidence to prove the relationship between variables. This is the type of conceptual framework that is usually used in research studies.

3. Mathematical Description

In this conceptual framework, the relationship between phenomena and variables of your study is described using mathematical formulas. Also, the extent of the relationship between these variables is presented with specific quantities.

How To Make Conceptual Framework: 4 Steps

1. identify the important variables of your study.

There are two essential variables that you must identify in your study: the independent and the dependent variables.

An independent variable is a variable that you can manipulate. It can affect the dependent variable. Meanwhile, the dependent variable is the resulting variable that you are measuring.

You may refer to your research question to determine your research’s independent and dependent variables.

Suppose your research question is: “Is There a Significant Relationship Between the Quantity of Organic Fertilizer Used and the Plant’s Growth Rate?” The independent variable of this study is the quantity of organic fertilizer used, while the dependent variable is the plant’s growth rate.

2. Think About How the Variables Are Related

Usually, the variables of a study have a direct relationship. If a change in one of your variables leads to a corresponding change in another, they might have this kind of relationship.

However, note that having a direct relationship between variables does not mean they already have a cause-and-effect relationship 2 . It takes statistical analysis to prove causation between variables.

Using our example earlier, the quantity of organic fertilizer may directly relate to the plant’s growth rate. However, we are not sure that the quantity of organic fertilizer is the sole reason for the plant’s growth rate changes.

3. Analyze and Determine Other Influencing Variables

Consider analyzing if other variables can affect the relationship between your independent and dependent variables 3 .

4. Create a Visual Diagram or a Model

Now that you’ve identified the variables and their relationship, you may create a visual diagram summarizing them.

Usually, shapes such as rectangles, circles, and arrows are used for the model. You may create a visual diagram or model for your conceptual framework in different ways. The three most common models are the independent-dependent variable model, the input-process-output (IPO) model, and concept maps.

a. Using the Independent-Dependent Variable Model

You may create this model by writing the independent and dependent variables inside rectangles. Then, insert a line segment between them, connecting the rectangles. This line segment indicates the direct relationship between these variables. 

Below is a visual diagram based on our example about the relationship between organic fertilizer and a plant’s growth rate. 

b. Using the Input-Process-Output (IPO) Model

If you want to emphasize your research process, the input-process-output model is the appropriate visual diagram for your conceptual framework.

To create your visual diagram using the IPO model, follow these steps:

• Determine the inputs of your study . Inputs are the variables you will use to arrive at your research result. Usually, your independent variables are also the inputs of your research. Let’s say your research is about the Level of Satisfaction of College Students Using Google Classroom as an Online Learning Platform. You may include in your inputs the profile of your respondents and the curriculum used in the online learning platform.
• Outline your research process. Using our example above, the research process should be like this: Data collection of student profiles → Administering questionnaires → Tabulation of students’ responses → Statistical data analysis.
• State the research output . Indicate what you are expecting after you conduct the research. In our example above, the research output is the assessed level of satisfaction of college students with the use of Google Classroom as an online learning platform.
• Create the model using the research’s determined input, process, and output.

Presented below is the IPO model for our example above.

c. Using Concept Maps

If you think the two models presented previously are insufficient to summarize your study’s concepts, you may use a concept map for your visual diagram.

A concept map is a helpful visual diagram if multiple variables affect one another. Let’s say your research is about Coping with the Remote Learning System: Anxiety Levels of College Students. Presented below is the concept map for the research’s conceptual framework:

5. Explain Your Conceptual Framework in Narrative Form

Provide a brief explanation of your conceptual framework. State the essential variables, their relationship, and the research outcome.

Using the same example about the relationship between organic fertilizer and the growth rate of the plant, we can come up with the following explanation to accompany the conceptual framework:

Figure 1 shows the Conceptual Framework of the study. The quantity of the organic fertilizer used is the independent variable, while the plant’s growth is the research’s dependent variable. These two variables are directly related based on the research’s empirical evidence.

Conceptual Framework in Quantitative Research

You can create your conceptual framework by following the steps discussed in the previous section. Note, however, that quantitative research has statistical analysis. Thus, you may use arrows to indicate a cause-and-effect relationship in your model. An arrow implies that your independent variable caused the changes in your dependent variable.

Usually, for quantitative research, the Input-Process-Output model is used as a visual diagram. Here is an example of a conceptual framework in quantitative research:

Research Topic : Level of Effectiveness of Corn (Zea mays) Silk Ethanol Extract as an Antioxidant

Conceptual Framework in Qualitative Research

Again, you can follow the same step-by-step guide discussed previously to create a conceptual framework for qualitative research. However, note that you should avoid using one-way arrows as they may indicate causation . Qualitative research cannot prove causation since it uses only descriptive and narrative analysis to relate variables.

Here is an example of a conceptual framework in qualitative research:

Research Topic : Lived Experiences of Medical Health Workers During Community Quarantine

Conceptual Framework Examples

Presented below are some examples of conceptual frameworks.

Research Topic : Hypoglycemic Ability of Gabi (Colocasia esculenta) Leaf Extract in the Blood Glucose Level of Swiss Mice (Mus musculus)

Figure 1 presents the Conceptual Framework of the study. The quantity of gabi leaf extract is the independent variable, while the Swiss mice’s blood glucose level is the study’s dependent variable. This study establishes a direct relationship between these variables through empirical evidence and statistical analysis . 

Research Topic : Level of Effectiveness of Using Social Media in the Political Literacy of College Students

Figure 1 shows the Conceptual Framework of the study. The input is the profile of the college students according to sex, year level, and the social media platform being used. The research process includes administering the questionnaires, tabulating students’ responses, and statistical data analysis and interpretation. The output is the effectiveness of using social media in the political literacy of college students.

Research Topic: Factors Affecting the Satisfaction Level of Community Inhabitants

Figure 1 presents a visual illustration of the factors that affect the satisfaction level of community inhabitants. As presented, environmental, societal, and economic factors influence the satisfaction level of community inhabitants. Each factor has its indicators which are considered in this study.

Tips and Warnings

• Please keep it simple. Avoid using fancy illustrations or designs when creating your conceptual framework. 
• Allot a lot of space for feedback. This is to show that your research variables or methodology might be revised based on the input from the research panel. Below is an example of a conceptual framework with a spot allotted for feedback.

Frequently Asked Questions

1. how can i create a conceptual framework in microsoft word.

First, click the Insert tab and select Shapes . You’ll see a wide range of shapes to choose from. Usually, rectangles, circles, and arrows are the shapes used for the conceptual framework. 

Next, draw your selected shape in the document.

Insert the name of the variable inside the shape. You can do this by pointing your cursor to the shape, right-clicking your mouse, selecting Add Text , and typing in the text.

Repeat the same process for the remaining variables of your study. If you need arrows to connect the different variables, you can insert one by going to the Insert tab, then Shape, and finally, Lines or Block Arrows, depending on your preferred arrow style.

2. How to explain my conceptual framework in defense?

If you have used the Independent-Dependent Variable Model in creating your conceptual framework, start by telling your research’s variables. Afterward, explain the relationship between these variables. Example: “Using statistical/descriptive analysis of the data we have collected, we are going to show how the <state your independent variable> exhibits a significant relationship to <state your dependent variable>.”

On the other hand, if you have used an Input-Process-Output Model, start by explaining the inputs of your research. Then, tell them about your research process. You may refer to the Research Methodology in Chapter 3 to accurately present your research process. Lastly, explain what your research outcome is.

Meanwhile, if you have used a concept map, ensure you understand the idea behind the illustration. Discuss how the concepts are related and highlight the research outcome.

3. In what stage of research is the conceptual framework written?

The research study’s conceptual framework is in Chapter 2, following the Review of Related Literature.

4. What is the difference between a Conceptual Framework and Literature Review?

The Conceptual Framework is a summary of the concepts of your study where the relationship of the variables is presented. On the other hand, Literature Review is a collection of published studies and literature related to your study. 

Suppose your research concerns the Hypoglycemic Ability of Gabi (Colocasia esculenta) Leaf Extract on Swiss Mice (Mus musculus). In your conceptual framework, you will create a visual diagram and a narrative explanation presenting the quantity of gabi leaf extract and the mice’s blood glucose level as your research variables. On the other hand, for the literature review, you may include this study and explain how this is related to your research topic.

5. When do I use a two-way arrow for my conceptual framework?

You will use a two-way arrow in your conceptual framework if the variables of your study are interdependent. If variable A affects variable B and variable B also affects variable A, you may use a two-way arrow to show that A and B affect each other.

Suppose your research concerns the Relationship Between Students’ Satisfaction Levels and Online Learning Platforms. Since students’ satisfaction level determines the online learning platform the school uses and vice versa, these variables have a direct relationship. Thus, you may use two-way arrows to indicate that the variables directly affect each other.

• Conceptual Framework – Meaning, Importance and How to Write it. (2020). Retrieved 27 April 2021, from https://afribary.com/knowledge/conceptual-framework/
• Correlation vs Causation. Retrieved 27 April 2021, from https://www.jmp.com/en_ph/statistics-knowledge-portal/what-is-correlation/correlation-vs-causation.html
• Swaen, B., & George, T. (2022, August 22). What is a conceptual framework? Tips & Examples. Retrieved December 5, 2022, from https://www.scribbr.com/methodology/conceptual-framework/

Written by Jewel Kyle Fabula

in Career and Education , Juander How

Last Updated May 6, 2023 10:37 AM

Jewel Kyle Fabula

Jewel Kyle Fabula is a Bachelor of Science in Economics student at the University of the Philippines Diliman. His passion for learning mathematics developed as he competed in some mathematics competitions during his Junior High School years. He loves cats, playing video games, and listening to music.

Browse all articles written by Jewel Kyle Fabula

Copyright Notice

All materials contained on this site are protected by the Republic of the Philippines copyright law and may not be reproduced, distributed, transmitted, displayed, published, or broadcast without the prior written permission of filipiknow.net or in the case of third party materials, the owner of that content. You may not alter or remove any trademark, copyright, or other notice from copies of the content. Be warned that we have already reported and helped terminate several websites and YouTube channels for blatantly stealing our content. If you wish to use filipiknow.net content for commercial purposes, such as for content syndication, etc., please contact us at legal(at)filipiknow(dot)net

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons
• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

2.20: Input-Process-Output Model

• Last updated
• Save as PDF
• Page ID 10612

The input–process–output (IPO) model is a widely used approach in systems analysis and software engineering for describing the structure of an information processing program or another process. Many introductory programming and systems analysis texts introduce this as the most basic structure for describing a process. [1]

A computer program or any other sort of process using the input-process-output model receives inputs from a user or other source, does some computations on the inputs, and returns the results of the computations. The system divides the work into three categories: [2]

• A requirement from the environment (input)
• A computation based on the requirement (process)
• A provision for the environment (output)

For example, a program might be written to convert Fahrenheit temperatures into Celsius temperatures. Following the IPO model, the program must:

• Ask the user for the Fahrenheit temperature (input)
• Perform a calculation to convert the Fahrenheit temperature into the corresponding Celsius temperature (process)
• Display the Celsius temperature (output)
• Wikiversity: Computer Programming
• Flowgorithm – Flowchart Programming Language
• Wikipedia: IPO model ↵

A Comprehensive Guide to Input-Process-Output Models

Updated: January 31, 2024 by Ken Feldman

Are you looking for a business improvement tool that is intuitive, simple to use, and visual in nature? Do you want to explore your internal business process and make sure you understand all of the inputs, outputs, and potential error states? 

If you are answering yes to these questions, then using input-process-output could be the perfect methodology for you. Let’s find out more. 

Overview: What is input-process-output (I-P-O)? 

Input-process-output (I-P-O) is a structured methodology for capturing and visualizing all of the inputs, outputs, and process steps that are required to transform inputs into outputs. It is often referred to, interchangeably, as an I-P-O model or an I-P-O diagram, both of which make reference to the intended visual nature of the method. 

A simple example is shown below from research in healthcare.

https://www.researchgate.net/figure/The-Input-Process-Output-diagram-of-the-proposed-system_fig2_323935725

As the methodology is incredibly versatile, it is used across many industries and sectors with (inevitably) some modifications and adaptations. These can include, for example, the addition of feedback loops from output to input, in doing so creating models analogous to closed-loop control theory.

Typically, we would use I-P-O in the “define” stage of a Six Sigma DMAIC project and follow a specific method for generating the model. The steps are:

• Decide upon the process steps that will be in scope of the I-P-O model. Try to ensure the the scope is manageable with, ideally, less than 10 process steps defined.
• List all of the possible outputs, including potential error states.
• List all of the inputs to your process steps, using clear descriptive language.
• Create a visual I-P-O model.
• Check that the inputs are transformed to the outputs via the process steps as shown in the model. 

Often, it can be helpful to have the team that’s generating the I-P-O model complete a Gemba walk. Visiting the actual place of work and viewing the process in action can tease out some of the less obvious inputs and outputs and contributes to continuous improvement of the existing process steps.

2 benefits and 1 drawback of I-P-O 

Used correctly, the I-P-O model offers a simple, practical, and efficient way to analyse and document a transformation process. Let’s explore some benefits and drawbacks of I-P-O.

1. It’s visual and easy to explain

It’s often said that the best business improvement tools are simple to use, intuitive, and visual, and I-P-O ticks all three of these boxes. A sheet of paper, marker pen, and an enthusiastic team willing to contribute will get you a long way. It’s also versatile, suitable for use with the executive management group as well as the wider business improvement team.

2. It’s easy to execute

There is a clear and simple methodology to generate I-P-O models, and this helps you recognise and document all of the possible inputs, outputs, and error states. As it’s visual, it’s easy to update and change as the team explores many potential inputs and outputs.

3. It’s internally focused without regard for external customers or suppliers   

Developing I-P-O models is usually all about internal business processes, and we often hear this called micro-process-mapping. This typically means we do not consider our external suppliers and customers in the analysis. However, don’t worry, we have complimentary models such as SIPOC and COPIS that help us make sense of the bigger (macro) picture.

Why is I-P-O important to understand? 

For such a relatively simple mapping tool, it provides a really powerful insight into our internal business processes. Let’s dig a little deeper.

It helps with defining your key process input variables

Once we’ve documented and visualised our inputs and outputs, we can turn our attention to determining and controlling which inputs provide a significant impact on the output variation — these are known as our key process input variables . 

It’s aligned with Six Sigma and Lean principles 

In a classic Six Sigma and Lean project approach, we strive to reduce process variation and remove defects and waste. With I-P-O, we identify inputs, outputs, and error states from our processes so we can begin to explore and understand the Y(output) = f ((X) input) equation.

It’s the perfect springboard to create full process maps 

Once we have created I-P-O models, we have the perfect starting place for generating complete process maps . This could be moving on to value stream mapping , spaghetti maps, or one of many other types of process maps that are available.

An industry example of I-P-O 

A government agency with multiple departments was embarking upon a business transformation project to improve customer service times and efficiency. As part of the transformation project, a Six Sigma Black Belt who was assigned to the activity was requested to explore and document existing processes and prepare the teams for process improvement.

The Black Belt chose to create I-P-O models due to the ease of use and versatility of the approach. Each of the business departments designated a team to work on the I-P-O models and, alongside the Black Belt, defined the process scope, ensuring this was of manageable size. 

With the teams in place and scope defined the process outputs were brainstormed and captured visually using whiteboards. The corresponding inputs were added, and the I-P-O models checked for completeness.

Generating the I-P-O models highlighted a number of potential output error states that were subsequently investigated as part of the business transformation project and contributed to improved customer service times. As the models were captured visually on whiteboards, they were easily updated during the project and used to inform staff of their contribution towards continuous improvement.

3 best practices when thinking about I-P-O 

Like many process-driven mapping activities, there are some key things for us to consider when creating I-P-O models. Let’s look at three of these.  

1. Remember: It’s a team sport; don’t go it alone 

Even relatively simple processes have multiple inputs and outputs. Often we find that different team members have detailed knowledge of specific process inputs and outputs, and we should make good use of this collective knowledge.

2. Make sure the scope is achievable

Don’t be overly ambitious with the scope and try to include too many process steps for your I-P-O model. If you find yourself listing 10 or more process steps, it’s probably time to stop and re-evaluate.

3. Consider all of the inputs and outputs 

Be diligent, get all the team involved, and make sure there is no bias — we don’t want to just list the things we think should be inputs and outputs in an ideal world. In addition, we should consider and document all of the possible output error states.

Frequently Asked Questions (FAQ) about I-P-O

Is i-p-o related to sipoc .

It can be a logical next step to create a SIPOC model from an I-P-O model. With SIPOC, we consider both suppliers (S) and customers (C) in the analysis, the so-called wider or bigger picture. With I-P-O, we concentrate more on the internal business process.

Where do I start with an I-P-O model? 

Start by defining the processes that are in scope, making sure the scope is manageable. Then consider and document all of the possible outputs from the process steps before moving on to capture the inputs.

Do I need a software program to generate I-P-O models? 

Definitely not. You can start with paper, pen, and a pack of sticky notes. However, there are a number of free templates available for download that can help you and your team as you start to populate the I-P-O model.

A final thought on I-P-O

Ease of use and versatility are just two of the major plus points of developing I-P-O models for your internal business processes. Add in their highly visual nature, and this means you can easily engage your team on a journey to continuous improvement.

About the Author

Ken Feldman

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Input-Process-Output Model

Dave Braunschweig

The input–process–output (IPO) model  is a widely used approach in systems analysis and software engineering for describing the structure of an information processing program or another process. Many introductory programming and systems analysis texts introduce this as the most basic structure for describing a process. [1]

A computer program or any other sort of process using the input-process-output model receives inputs from a user or other source, does some computations on the inputs, and returns the results of the computations. The system divides the work into three categories: [2]

• A requirement from the environment (input)
• A computation based on the requirement (process)
• A provision for the environment (output)

For example, a program might be written to convert Fahrenheit temperatures into Celsius temperatures. Following the IPO model, the program must:

• Ask the user for the Fahrenheit temperature (input)
• Perform a calculation to convert the Fahrenheit temperature into the corresponding Celsius temperature (process)
• Display the Celsius temperature (output)
• Wikiversity: Computer Programming
• Flowgorithm – Flowchart Programming Language
• Wikipedia: IPO model ↵

Programming Fundamentals Copyright © 2018 by Dave Braunschweig is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Process Flow for Thesis/Dissertation

This page outlines the procedures that Graduate School Students need to know in order to successfully complete their degree.

Process Flow for Thesis/Dissertation Proposal Defense in the Graduate School PDF

Process flow for Thesis/Dissertation Pre-final and Final Defense in the Graduate School PDF

I Help to Study

Useful information for students

Home » Proposal » Sample input process output in thesis proposal

• Academic Writing
• Assignments
• Business Plans
• Buy Services
• Custom Writing
• Dissertations
• For Professionals
• Help & Assistance
• Useful Services
• Various Papers

Sample input process output in thesis proposal

“FREE copy of the approved Dissertation” to model your projects upon and ensure you’re going within the right direction.

Claim your FREE 2:1

Graded Dissertation Sample (using the permission from the pass-out) to.

• Spark new ideas
• Be aware of structure and format from the dissertation
• Understand what to incorporate in each chapter plus much more.

to Model Your Projects Upon and obtain an Appreciative Smile out of your Professor You Had Been Awaiting Ages

Are you currently the one that is definitely trying to find examples instead of studying theoretical description from the subject?

Are you currently the main one who’s into listening lectures rather of banging your mind within the books?

Regardless of what you want, the theoretical framework examples I will give you’ll be an ideal practical concept of coping with this most challenging part of any project. Example is preferable to principle, let’s observe how..

Sample: Theoretical Framework

Subject: The scholars of information technology are analyzing how their existing system works? The sample of theoretical framework may also utilize input process output diagram to obtain a closer consider the situation.

Theoretical Framework: The way the existing video game system works?

For that possible completing the research, el born area will advice the readers the way the existing computer works and just how the proponent can further improve it. With this, an entire input – Process – Output diagram continues to be organized.

Manual System Input – Process – Creation of the present System

Following this, the spectator will discover which piece wont farmville and which piece needs to be removed or retained around the game.

The procedure continues until among the players win the match by recording the opponent’s flag or conquer his base. Probably the most interesting facet of farmville is the fact that, both opponents cannot see one another, just the spectator can easily see each of them.

Which was a theoretical framework example to steer the scholars as if you to obtain hang on your anxiety about focusing on it. But don’t forget not for doing things because it is!Theoretical framework examples have more weightage for that marks. There’s two big advantages the students can avail. The very first point may be the theoretical example with framework shows the knowledge of the scholars about subject and 2nd advantage would be that the students could be able to score good marks. But this ought to be appreciated while giving the instance that it should be associated with the topic also it should have thorough understanding that you will find elaborating the subject efficiently and effectively. Students are more inclined to choose example in the lecture that is delivered within the class. Since the examples that are delivered within the class is very authentic which is more associated with the subject. In some way, example might be selected in the book however it usually bothers the scholars to discover the correct example after studying the entire chapter which requires a lot time.

Haven’t found your quest subject yet?

Get the Research Subject Brief of 250 words in your field of study in only 3-5 working days including…

• A reason why we decide this subject?
• 2-3 research questions
• Key literature sources identification
• Raw sample size definition
• Explanation from the methodology for collecting the information

Related Articles:

Latest Posts

• Privacy Policy

© 2016 | IHelptoStudy.Com

Please Wait!

• Skip to main content
• Skip to primary sidebar

IResearchNet

Input-Process-Output Model

Much of the work in organizations is accomplished through teams. It is therefore crucial to determine the factors that lead to effective as well as ineffective team processes and to better specify how, why, and when they contribute. Substantial research has been conducted on the variables that influence team effectiveness, yielding several models of team functioning. Although these models differ in a number of aspects, they share the commonality of being grounded in an input-process-output (IPO) framework. Inputs are the conditions that exist prior to group activity, whereas processes are the interactions among group members. Outputs are the results of group activity that are valued by the team or the organization.

The input-process-output model has historically been the dominant approach to understanding and explaining team performance and continues to exert a strong influence on group research today. The framework is based on classic systems theory, which states that the general structure of a system is as important in determining how effectively it will function as its individual components. Similarly, the IPO model has a causal structure, in that outputs are a function of various group processes, which are in turn influenced by numerous input variables. In its simplest form, the model is depicted as the following:

Academic Writing, Editing, Proofreading, And Problem Solving Services

Get 10% off with 24start discount code.

Input —> Process —> Output

Inputs reflect the resources that groups have at their disposal and are generally divided into three categories: individual-level factors, group-level factors, and environmental factors. Individual-level factors are what group members bring to the group, such as motivation, personality, abilities, experiences, and demographic attributes. Examples of group-level factors are work structure, team norms, and group size. Environmental factors capture the broader context in which groups operate, such as reward structure, stress level, task characteristics, and organizational culture.

Processes are the mediating mechanisms that convert inputs to outputs. A key aspect of the definition is that processes represent interactions that take place among team members. Many different taxonomies of teamwork behaviors have been proposed, but common examples include coordination, communication, conflict management, and motivation.

In comparison with inputs and outputs, group processes are often more difficult to measure, because a thorough understanding of what groups are doing and how they complete their work may require observing members while they actually perform a task. This may lead to a more accurate reflection of the true group processes, as opposed to relying on members to self-report their processes retrospectively. In addition, group processes evolve over time, which means that they cannot be adequately represented through a single observation. These difficult methodological issues have caused many studies to ignore processes and focus only on inputs and outputs. Empirical group research has therefore been criticized as treating processes as a “black box” (loosely specified and unmeasured), despite how prominently featured they are in the IPO model. Recently, however, a number of researchers have given renewed emphasis to the importance of capturing team member interactions, emphasizing the need to measure processes longitudinally and with more sophisticated measures.

Indicators of team effectiveness have generally been clustered into two general categories: group performance and member reactions. Group performance refers to the degree to which the group achieves the standard set by the users of its output. Examples include quality, quantity, timeliness, efficiency, and costs. In contrast, member reactions involve perceptions of satisfaction with group functioning, team viability, and personal development. For example, although the group may have been able to produce a high-quality product, mutual antagonism may be so high that members would prefer not to work with one another on future projects. In addition, some groups contribute to member well-being and growth, whereas others block individual development and hinder personal needs from being met.

Both categories of outcomes are clearly important, but performance outcomes are especially valued in the teams literature. This is because they can be measured more objectively (because they do not rely on team member self-reports) and make a strong case that inputs and processes affect the bottom line of group effectiveness.

Steiner’s Formula

Consistent with the IPO framework, Ivan Steiner derived the following formula to explain why teams starting off with a great deal of promise often end up being less than successful:

Actual productivity = potential productivity – process loss

Although potential productivity is the highest level of performance attainable, a group’s actual productivity often falls short of its potential because of the existence of process loss. Process loss refers to the suboptimal ways that groups operate, resulting in time and energy spent away from task performance. Examples of process losses include group conflict, communication breakdown, coordination difficulty, and social loafing (group members shirking responsibility and failing to exert adequate individual effort). Consistent with the assumptions of the IPO model, Steiner’s formula highlights the importance of group processes and reflects the notion that it is the processes and not the inputs (analogous to group potential) that create the group’s outputs. In other words, teams are a function of the interaction of team members and not simply the sum of individuals who perform tasks independently.

Limitations of the IPO Model

The major criticism that has been levied against the IPO model is the assumption that group functioning is static and follows a linear progression from inputs through outputs. To incorporate the reality of dynamic change, feedback loops were added to the original IPO model, emanating primarily from outputs and feeding back to inputs or processes. However, the single-cycle, linear IPO path has been emphasized in most of the empirical research. Nevertheless, in both theory and measurement, current team researchers are increasingly invoking the notion of cyclical causal feedback, as well as nonlinear or conditional relationships.

Although the IPO framework is the dominant way of thinking about group performance in the teams literature, relatively few empirical studies have been devoted to the validity of the model itself. In addition, research directly testing the input-process-output links has frequently been conducted in laboratory settings, an approach that restricts the number of relevant variables that would realistically occur in an organization. However, although the IPO model assumes that process fully mediates the association between inputs and outputs, some research has suggested that a purely mediated model may be too limited. Therefore, alternative models have suggested that inputs may directly affect both processes and outputs.

Without question, the IPO model reflects the dominant way of thinking about group performance in the groups literature. As such, it has played an important role in guiding research design and encouraging researchers to sample from the input, process, and output categories in variable selection. Recent research is increasingly moving beyond a strictly linear progression and incorporating the reality of dynamic change. In addition, alternatives to the traditional IPO model have been suggested in which processes are not purely mediated.

References:

• Hackman, J. R. (1987). The design of work teams. In J. Lorsch (Ed.), Handbook of organizational behavior (pp. 315-342). New York: Prentice Hall.
• Ilgen, D. R., Hollenbeck, J. R., Johnson, M., & Jundt, D. (2005). Teams in organizations: From input-process-output models to IMOI models. Annual Review of Psychology, 56, 517-543.
• Steiner, I. D. (1972). Group process and productivity. New York: Academic Press.
• Group Dynamics
• Industrial-Organizational Psychology

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Hum Kinet
• v.78; 2021 Mar

Applying the Input-Process-Outcome Model to Team Learning in Sport Sciences: An Exploratory Qualitative Study on Twenty Soccer Players

Thibault kérivel.

1 University of Nantes, "Movement, Interactions, Performance" Lab. UFR STAPS, 25Bis Boulevard Guy Mollet, 44322, Nantes France

2 University of West Brittany, Research Center for Education Learning and Didactics, 20 avenue Victor Le Gorgeu, 29200, Brest France

Cyril Bossard

Gilles kermarrec.

In sport science literature, referring to the Input-Process-Outcome (IPO) model, few studies demonstrated links between team training and team learning despites several calls for empirical studies. Thus, this study aimed at exploring systemically the building process of the interpersonal coordination by focusing on (1) a specific antecedent (i.e., video feedback during practice), (2) the influence of this antecedent on the team learning process, and (3) outcomes from this process as shared cognitive contents. Thus, this study was original by examining empirically the IPO model in a sport-training context. Our study showed that Input influenced the specific learning Process during practice (five processes) and video feedback sessions (five processes) and produced Outcomes (six typical shared cognitive contents). Finally, results are discussed in relation to team learning processes theoretically identified in the literature and an IPO soccer model adapted to team learning in a soccer context is proposed.

Introduction

The social-cognitive and the ecological dynamics approaches are two well-known theoretical perspectives describing interpersonal coordination in team sports (Araujo and Bourbousson, 2016). These theoretical perspectives led to specific pedagogical models, i.e., Teaching Games for Understanding (TGfU) and Constraints-Led Approach (CLA) aiming at answering practical questions as: how to train interpersonal coordination in team sports? How to improve tactical decision making in team sports? Which pedagogical constraints lead to adaptation and learning? What kind of feedback can help players? ( Kermarrec and Roure, 2016 ; Renshaw et al., 2016 ). Similarities exist between these pedagogical models: in opposition with traditional analytic training approaches, they emphasize the importance of representative tasks such as small-sided games (SSG) in soccer (e.g., Davids et al., 2013 ). Nevertheless, there are major differences in theoretical principles used to guide pedagogical practice and strategies ( Renshaw et al., 2016 ), so that teachers and coaches should make different choices when they intervene and deliver instructions or feedback ( Batista et al., 2019 ), with hypothetical consequences on the learning process ( Kermarrec and Roure, 2016 ; Price et al., 2019). Nevertheless, both the CLA and TGFU focused on the relationship between training strategies and learning outcomes such as team behaviors ( Travassos and al., 2014 ), team efficacy and team members’ knowledge ( Stolz and Pill, 2014 ), so that the learning process has been neglected. There has been a shift in research focus from the ‘process/product’ paradigm to the question about how learners individually construct their knowledge in relation to their learning environment ( Tan et al., 2012 ). Thus, this study aimed at exploring the learning process and shared cognitive outcomes emerging while players were practicing a soccer SSG and were guided through immediate video feedback.

Learning processes as a critical consequence of team training strategies

Team learning processes represent the building process of interpersonal coordination during a team training session ( Decuyper et al., 2010 ). According to McEwan and Beauchamp (2014) , studying team learning processes should be critical for a better understanding of team training. The theoretical Input-Process-Outcome model (IPO) was recently considered as relevant to investigate relationships between team training (I), team learning (P) and team coordination (O) ( Decuyper et al., 2010 ; McEwan and Beauchamp, 2014 ). Input is defined as the set of elements in the environment that could have an influence on the learning process and its outcome. Although previous research has not considered the importance of the process by studying only the relationship between the input and outcomes or output ( Salas et al., 2008 , 2014 ), IPO research should try to put in light the role of team learning processes ( Edmondson et al., 2007 ). Considering the IPO model as a holistic system, Decuyper et al. (2010) suggested that seven team-learning processes could be distinguished within the IPO system ( Table 1 ).

Seven processes in the Team Learning activity ( Decuyper et al., 2010 )

More recently, the IPO model has been theoretically adapted from ergonomics to sport sciences ( McEwan and Beauchamp, 2014 ). Based on this model, team learning processes are described to better understand the construction of interpersonal coordination in a team sports setting ( Table 2 ).

Theoretical description of team learning processes in sport ( McEwan and Beauchamp, 2014 )

Mc Ewan’s (2014) model tried to preserve the dynamics of the IPO system. It promoted four stages / phases (i.e., preparation, execution, evaluation, adjustment) which could contribute to a better analysis of a team training session. Those two models ( Decuyper et al., 2010 ; McEwan and Beauchamp, 2014 ) help fully understand the importance of the team learning process within the IPO model. Nevertheless, despite these theoretical advances, there is a lack of empirical investigations considering Input, Process and Outcome in a team sport-training context.

Feedback as a critical tool within team training strategies

Among the elements identified as input within training strategies, the use of instructions and feedback (i.e. moment of instructions and feedback, type of instructions and feedback, content of instructions and feedback) has recently been questioned ( Batista et al., 2019 ; Price et al., 2019). Teaching Games for Understanding is one of the most recognized team training models, aiming at developing teammates knowledge about their role, co-constructing a common goal, and achieving relevant tactical behaviour ( Gréhaigne et al., 2001 ; Griffin and Butler, 2005 ). In the TGfU perspective, feedback is delivered through questioning and debates of ideas, and explicit learning processes are favoured ( Kermarrec and Roure, 2016 ; Raab, 2007 ). Literature reviews showed that those training approaches favoured knowledge construction, but findings are non-consistent when researchers investigated players’ efficacy or team performance outcomes ( Gréhaigne and Nadeau, 2015 ; Kermarrec and Roure, 2016 ; Raab, 2007 ; Stolz and Pill, 2014 ). Moreover, numerous recent studies have demonstrated that video feedback combined with questioning improves decisionmaking in tennis (Garcia-Gonzales et al., 2014) and athletes’ tactical knowledge in volleyball ( Gil-Arias et al., 2015 ) as well as in tennis ( Moreno et al., 2016 ).

Taking into account those advances and limitations, an innovative team training strategy recently emerged as “meaningful experience pedagogy” ( Kermarrec and Bossard, 2017 ). The Meaningful Experience Pedagogy (MEP), in comparison with traditional analytic training approaches, urges the use of small-sided games (SSG) in soccer ( Kermarrec and Roure, 2016 ). Based on a naturalistic ergonomic approach (T-NDM Approach, Klein et al., 2008 ), MEP is a blend of practice and video feedback ( Kermarrec and Bossard, 2017 ): practice should lead to compiling extensive experiences, while video feedback should lead to reviewing significant experiences. Moreover, coaches’ interventions consist in delivering short video feedback focusing players’ attention on each other’s positions and functional relationships between teammates. Theoretically, video feedback should be useful for coaches and teachers to sustain analogic learning processes and to highlight successful configuration of play. Nevertheless, very few empirical studies have been conducted to investigate learning processes and outcomes in MEP training sessions. Thus, based on the IPO model’s hypothesis, this study aimed at exploring the building process of the interpersonal coordination of soccer players based on MEP, by focusing on (1) a specific antecedent (i.e., video feedback), (2) the influence of this antecedent on the team learning process, and (3) resulting outcomes as shared cognitive contents.

Participants

Twenty students participated in this study. All of them were enrolled in a soccer course within the third year of a sport sciences post-graduate curriculum at a French university. Players ranged in age from 20 to 23 years and they had been playing soccer in the French Soccer Federation championships for 10 to 14 years; their performance level was from regional to national. The aim of the study was briefly explained to each participant and they all signed a consent form. Two experienced teachers designed the training protocol; both of them were certified in sport sciences and in soccer coaching.

The soccer course was a twenty-four week course during the third year of sport sciences curriculum. During the first course unit, two teachers composed two teams with equal competencies. One teacher was in charge of the second-year soccer course and the second teacher supervised the third-year soccer course. Players’ individual skills had been assessed during the second-year soccer course. More specifically, individual technical and tactical skills were evaluated during an offensive stage in small-sided games, and a test score was delivered to each student at the end of the second-year course. Based on this dataset, the two teachers were able to compose two teams of equal competencies. The aim of the course was to improve the collective performance of each team ( Figure 1 ). The study was conducted during the sixth (i.e., training coordination) and the seventh course unit (i.e., assessing coordination).

The procedure’s description

A representative SSG in soccer

The training task was a small-sided game (SSG). Since a team as a complex system requires coordination for success, small-sided games are considered a representative task device for team training ( Davids et al., 2013 ; Stolz and Pill, 2014 ). The practice is 9 vs. 9 in a small-sided field that is divided into seven areas as shown in Figure 2 . Consequently, each player within a team was positioned in a specific area according to their role. Moreover, two joker players in flank areas could help the team in possession of the ball. The goal for each team was to reach one of the two targets. The offensive team had to pass over the defensive lines into the next space for a teammate using only the one touch pass. Only one defender player from each defensive area could leave his own space and try to recover the ball in the attackers’ space. Thus, when a team got the possession of the ball, offensive players had to pass the ball successively through one or two lines of defenders. The task’s objective was presented to all participants: they had to improve the ball’s progression between each line (defender to midfield, midfield to forward).

The Baby-foot, a Small-Sided Game

After a warm-up, the training session lasted 60 min within the sixth unit course. Players practiced the task for 20 min without any feedback: this was considered to be the pre-test for the study. The main training period lasted approximately 40 min with specific teacher’ interventions and feedback. The evaluation session in the task lasted 20 min within the seventh unit and was considered to be the posttest.

Team training strategy

The training session was based on the MEP principles. First, team training needs a lot of time to practice. Second, the teacher had to organize three short interruptions of the game to deliver video feedback. Video tools included a wide-angle camera set at 4 m height. This camera was linked to a touch pad by a Wi-Fi signal. The teacher could stop the game, gather players around the touchpad, let them review their last trial, and encourage them to play again. The teacher could also highlight a configuration of a play through frozen images. He could indicate successful configuration of play. Two researchers supervised the intervention to reduce the gap between the planned training procedure and the real procedure.

Data collection

Verbal data were collected during individual post-training interviews ( Macquet and Stanton, 2014 ), also called self-confrontation interviews ( Macquet, 2013 ) or video-cued interviews ( Kermarrec and Bossard, 2014 ). The interviews were conducted as soon as possible after the evaluation session (i.e., within 48 or 72 h). The interview focused on the feedback delivery phases (from 1 to 5 min each) and on the three game phases (approximately 7 min each) following teacher’ feedback interruptions. The interviewer used specific questions to encourage the participant to comment on his/her own experiences during the training session (i.e., What did you aim to do? What were you thinking about? What were you looking at?). The participant could stop the video as he/she wanted and take more time to describe a specific event or a meaningful moment. The interview took place immediately after the training session or some days after, depending on the availability of participants. Finally, 18 interviews were conducted and recorded (we did not meet players who played as jokers). They lasted between 30 and 45 min. From this material, we prepared a table for each players’ activity.

Data processing

Each player’s verbal report about the training session was processed separately. Firstly, the first researcher divided the transcripts into meaningful units according to the hypothetical aspects of the team training system (i.e., the IPO model). Thus, we especially focused on team learning processes during feedback delivery (e.g. “ I can visualize the game situation… ”; “ We are sharing solutions… ”), players’ learning processes while practicing the task (e.g., “ I’m talking to him to guide his play; I’m focusing on my partner’s run… ”), and the emerging cognitive outcomes (e.g., “ I think we should better go to the ball side… ”; “ I know we should move more and more… ”). Some affective outcomes (e.g., “ I don’t feel confident enough ”) were rarely elicited in this case study so that they were not pursued for further analysis. There were135 units related to team learning processes during feedback delivery; 431 units related to team learning processes while practicing the task; and 306 units described cognitive outcomes mobilized by participants. Secondly, researchers examined part of the selected units step by step depending on their meaning. For this data analysis step, as in Macquet’s (2013) and Kermarrec and Bossard’s (2014) method, an inductive content analysis ( Strauss and Corbin, 1998 ) was carried out. Team learning processes during feedback delivery were divided into seven empirical categories. Team learning processes while practicing the task were divided into five empirical categories. Cognitive outcomes were divided into six empirical categories. Thirdly, to ensure the validity of data analysis, three researchers were involved in data processing. The second researcher reviewed the meaningful units’ selection, and the inductive content analysis. The third researcher helped in case of disagreement between the first and the second researcher. They discussed until a consensus was reached. The reliability of the coding procedure was finally assessed with the Cohen’s (1960) Kappa test. The agreement rate was 92.28%, κ =. 88, with a confidence interval of 0.85 to 0.95 for the behavioural processes’ categories. The agreement rate was 90.40%, κ =. 88, with a confidence interval of 0.84 to 0.92 for the cognitive outcomes’ categories.

This section describes the team learning processes which players used during feedback delivery. Then, we focused on their practice experiences and their use of team learning processes while they went back to the training task. Finally, content of cognitive outcomes from the group of participants is presented.

Team Learning Processes during feedback delivery

Analysis and comparison of the players’ meaningful units related to feedback delivery revealed five team learning processes. The following table is the presentation of five teamlearning processes during VFB delivery.

Players’ experiences in a training session were influenced by feedback delivered of a teacher. Video feedback led players to specific team learning processes. Mainly, VFB helped them visualize their mistakes, previous situations or problems in the game, such as the opposite team organisation. Thanks to these “pictures” they were able to share, understand, plan, and validate solutions they had experienced during the practice phase.

IPO Model adapted to team learning in a soccer context

Learning processes the players used to improve coordination during practice

Analysis of the players’ meaningful units related to task practice revealed five types of learning processes. Table 4 presents the team learning processes gives an example of each of them and shows to what extent they were elicited and shared.

Team learning processes during the task practice

Note: F. US = Frequency of meaningful units (%), N. Part. = Number of participants concerned within a training condition .

Reviewing carefully the content of each category, researchers identified five types of learning processes when players had benefited from VFB. They went back to the game with some “pictures in the head”. Those pictures should help them to communicate, perceive and adapt to teammates, and to valid collective behaviour they have previously shared, validated and planned during the feedback. Furthermore, the initial practice phase led them to identify their own difficulties, and to feel task constraints. That's why feedback could have been helpful to visualise the problem and then to build shared solutions.

Content of cognitive outcomes

The analysis of the content of meaningful units related to cognitive outcomes led to six empirical categories: they are detailed in Table 5 .

Cognitive outcomes emerging during the training session

Examining carefully the content of each cognitive outcome category, researchers classified them into more general categories, related to Team Cognition advances. To play towards the front over defensive lines referred to the goal of the game, and the other categories referred to different means or knowledge the players built and shared to reach the goal ( Table 3 ). Furthermore, frequencies of cognitive outcome categories (from 11 to 29%) and the number of players (from 12 to 17 inside a training group) included in each category highlighted how much the different contents were shared.

Presentation of five team-learning processes during feedback delivery

The aim of our study was to explore the relationships between video feedback, team learning processes and shared cognitive contents, as an empirical investigation of the IPO model perspective. To this end, we investigated coordination building within teams during a soccer SSG. Our results highlighted 10 learning processes which were specific to the video feedback stage or to the practice stage. Most of these learning processes were shared between more than 10 players from our participants. Players mainly used the visualizing process during video feedback delivery (60% of the MU), and perceiving and adapting to teammates’ behaviors when practicing the SSG (55% of the MU). Our results can be discussed in two points: (1) highlighting the team-learning processes empirically identified; (2) the emergence of shared cognitive contents.

Team-learning processes: from theory towards empirical insights

Previously, researchers presented teamlearning processes in IPO from a theoretical perspective ( Decuyper et al., 2010 ; McEwan and Beauchamp, 2014 ). We explored this topic within an empirical and naturalistic approach. Thus, from our empirical results the following sections aim to show main similarities and differences with previous theoretical modeling. The learning processes identified in our setting are discussed in 4 temporal stages, in accordance with the dynamics of the training – learning process.

Feeling the problem: practicing the game

First, our study showed that players mobilized processes oriented toward feeling task constraints (20% of the MU) and tried to assess individuals’ difficulties within the task (11%). These learning processes could be categorized as processes theoretically identified as “mission analysis” or “system monitoring” in McEwan and Beauchamp (2014) proposals. Furthermore, our study showed that players focused on space perception and the opposite team organization to feel the problem. Paying attention to another team’s organization was identified as a cross boundary process in Decuyper et al. (2010) . Theoretically, this process should help identify problems, promote collective innovation and transfer activity between the training situation and the performance situation. In accordance with MEP, practice should help players better understand a training situation ( Kermarrec and Bossard, 2017 ).

Visualizing the problem: video feedback

In our study, video feedback favored visualization processes. This visualization process is not far from the process of collective reflexivity. According to Decuyper et al. (2010) collective reflexivity is the development of a “clear vision” of the current state of the collective (e.g. the team), of the state desired by the collective and of how they will reach that state. This process helps keep the team in the “right direction” during team learning activity and promotes collective adaptation. According to McEwan and Beauchamp (2014) visualization sharing allows players to plan future actions. Our results also suggest that video feedback is a relevant tool to help the player to construct a shared picture of a collective action. It could be hypothesized that such a shared picture could help athletes improve their decision-making (Garcia-Gonzales et al., 2014; Kermarrec and Plassart, 2017).

Team adaptation: practicing the game

The team behavior adaptation emerged when players went back to the game and made effort to perceive and adapt their behavior to teammates’ behaviors (55%), to assess and validate a collective behavior (9%), to communicate between themselves (5%). These different Team Learning Processes refer to the process of collective activity and should contribute to the storage and retrieval process identified by Decuyper et al. (2010) . When team members work together for a common purpose, they try to increase the quality of interpersonal coordination. In the execution stage according to McEwan and Beauchamp (2014) , team adaptation is sustained by communication, cooperation and coordination. These results demonstrated that team behavior adaptation needed different learning processes. Previous team sport training frameworks did not consider such a variety of processes. In the ecological dynamics approach behavior adaptation is viewed as perceptionaction coupling and an implicit learning process ( Davids et al., 2013 ), while in the Teaching Games for Understanding approach team adaptation is focused on explicit learning (i.e., assessment and verbal communication). In accordance with the MEP approach, this study underlines the interest of a large range of learning processes and highlights the importance of mutual adaptation between teammates.

Understanding, planning, sharing and validating a solution: video feedback

Thus, our data highlighted that video feedback also helped learners to understand (13%), plan (15%), share (5%) and validate solutions (7%), which is in accordance with coconstruction ( Decuyper et al., 2010 ). Coconstruction is a process of interaction between team members that promotes the shared construction of meaningful elements. Furthermore, during feedback delivery, players activated assessment processes. The assessment process in our study can be assimilated to team performance evaluation processes (performance and system monitoring) highlighted by McEwan and Beauchamp (2014) . These processes allow players to control the advancement in the problem solving process and to assess their own performance. During the co-construction stages we highlighted that students shared solutions and constructed consensual knowledge to perform the game. This co-construction process reminds processes such as problem solving, backing up and intra-team coaching, identified in the adjustment stage ( McEwan and Beauchamp, 2014 ). These processes supported by video feedback could explain why video feedback improves knowledge sophistication, structuration and accuracy in team sports ( Gil-Arias et al., 2015 ; Moreno et al., 2016 ).

Shared cognitive contents emerged from practice and video feedback

Our results suggest that specific input (SSG and video feedback) supported learning and favored shared cognitive outcomes. Literature shows that there is a relationship between team training protocols and effects in terms of cognitive affective or behavioral outcomes ( Davids et al., 2013 ; Salas et al., 2008 , 2014 ; Stolz and Pill, 2014 ). In our study these cognitive outcomes were categorized as “shared goals” or “shared knowledge”. These results are in line with other studies about team training effects on knowledge sharing ( Stolz and Pill, 2014 ). In previous research (for a review, see Gréhaigne and Nadeau, 2015 ; Stolz and Pill, 2014 ), knowledge was coconstructed in a training program if athletes were asked to verbalize it. In accordance with Meaningful Experiences Pedagogy ( Kermarrec and Bossard, 2017 ), our study demonstrated that knowledge also emerged from practice, and that brief video feedback was sufficient to support knowledge building. From our study we could also highlight the role of the training design in sharing diffusion of cognitive contents inside teams: 15 of the 18 teammates shared similar goals and knowledge. Nevertheless, the context of our study (i.e., a sport sciences post-graduate curriculum) probably contributed to the similarity of the contents, while in elite teams training, sharing generally results in complementary goals knowledge. Goals in elite teams are dependent on status or positions held in sports teams ( De Keukelaere et al., 2013 ).

A dynamic-cyclic representation of the learning – training process

In our study, we highlighted that the IPO system could work in four stages, i.e.: problem identification through practice, problem visualization through video feedback delivery, coconstructing team coordination through practice, picturing of a solution through video feedback. These stages are not so far from TGfU strategy associated steps: practice of the representative task, observation and evaluation of difficulties, analysis of the task and co-construction and verbalization of solutions, applying solutions within the task ( Gréhaigne and Nadeau, 2015 ). Results from our study show that instead of a sequential step-by-step representation of the learning-training process, it seems that the IPO system functions as a dynamic and cyclic (allosteric) system.

Nevertheless, in our study, the process of task constraints identification and analysis is not exclusively activated off-field as McEwan and Beauchamp (2014) suggested. Players “felt the problem” during practice. In the same perspective, our study revealed that players activated assessing processes during feedback delivery and also during practice (assessing individuals’ own difficulties within the task; assessing and validating a collective behavior). Thus, the findings drew attention to the fact that players assessed their performance continually during training sessions. These main differences between our empirical results and previous theoretical proposition could be explained by the specificity of our context. Our study tried to better understand team-learning processes in a teamsport training context, while Decuyper et al. (2010) described these processes in a work context. Furthermore, our study allows to better understand team learning processes in a soccer training context in post-graduate students, while McEwan and Beauchamp (2014) described, among other, team processes in a performanceoriented team.

Conclusions

Our explorative study aimed to better understand the team learning processes within an innovative team training approach (i.e., MEP), using video feedback. Based on a qualitative methodology, our results elicited various learning processes drawn by task practicing and the receipt of video feedback. Only few previous studies demonstrated relationships within the IPO system despite several calls for empirical studies ( Decuyper et al., 2010 ; Hughes et al., 2016 ; McEwan and Beauchamp, 2014 ). Our study showed that Input such as video feedback influenced Processes and led to shared cognitive Outcomes. From a practical point of view, we propose to the practitioners to pay attention to the learning processes as a bridge between the task design, feedback delivery and expected outcomes. Examining the role of video feedback and other input in learning processes as well as behavioural and cognitive outcomes would be a worthwhile purpose for research in sport and other contexts.

• Araújo D, Bourbousson J. Theoretical perspectives on interpersonal coordination for team behavior, in Interpersonal Coordination and Performance in Social Systems, eds P. Passos, K. Davids, and J. Y. Chow (London: Routledge); 2016. pp. 126–139. –. [ Google Scholar ]
• Batista J, Goncalves B, Sampaio J, Castro J, Abade E, Travassos B. The influence of coaches’ instruction on technical actions, tactical behaviour, and external workload in football small-sided games. Monten J Sports Sci Med. 2019; 8 (1):29–36. doi: 10.26773/mjssm.190305. [ CrossRef ] [ Google Scholar ]
• Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960; 20 :37–46. [ Google Scholar ]
• Davids K, Araújo D, Correia V, Vilar L. The science of team games: How small-sided games enhance acquisition of movement and decision-making skills. Exerc Sport Sci Rev. 2013; 41 (3):1–8. [ PubMed ] [ Google Scholar ]
• De Keukelaere C, Kermarrec G, Bossard C, Pasco D, Loor P. Forms, Contents, and Dynamics of Sharedness in an Elite Sport Team. Trav Humain. 2013; 76 (3):227–255. [ Google Scholar ]
• Decuyper S, Dochy F, Van den Bossche P. Grasping the dynamic complexity of team learning: An integrative model for effective team learning in organizations. Educ Res Rev-neth. 2010; 5 :111–133. doi: 10.1016/j.edurev.2010.02.002. [ CrossRef ] [ Google Scholar ]
• Edmondson AC, Dillon JR, Roloff KS. Three perspectives on team learning. Acad Manag Ann. 2007; 1 :269–314. doi: 10.1080/078559811. [ CrossRef ] [ Google Scholar ]
• García-González L, Moreno A, Gil A, Moreno MP, Del Villar F. Effects of Decision Training on Decision Making and Performance in Young Tennis Players. An App Res Jour of App Sp Psych. 2014; 26 (4):426–440. doi: 10.1080/10413200.2014.917441. [ CrossRef ] [ Google Scholar ]
• Gil-Arias A, García-González L, Del Villar F, Moreno A, Moreno MP. Effectiveness of video feedback and interactive questioning in improving tactical knowledge in volleyball. Perc and Mot Skills. 2015; 121 (3):635–53. doi: 10.2466/30. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gréhaigne JF, Nadeau L. Teaching and learning tactics in team sports: from forgotten precursors to current perspectives. eJRIEPS. 2015; 35 :106–140. [ Google Scholar ]
• Gréhaigne JF, Godbout P, Bouthier D. The teaching and learning of decision-making in team sports. Quest. 2001; 53 :59–76. [ Google Scholar ]
• Griffin LL, Butler J. Teaching Games for Understanding Theory, Research, and Practice. Champaign, IL: Human Kinetics; 2005. [ Google Scholar ]
• Hughes AM, Gregory ME, Joseph DL, Sonesh SC, Marlow SL, Lacerenza CN, Benishek LE, King HB, Salas E. Saving Lives: A Meta-Analysis of Team Training in Healthcare. J Appl Psychol. 2016; 101 (9):1266–304. doi: 10.1037/apl0000120. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kermarrec G, Bossard C. Dosseville F. Garncarzyk C. La prise de décision en football. PUC: Caen; 2017. Intuitive and coordinated decision-making: contribution to the tactical training of football players. &. [ Google Scholar ]
• Kermarrec G, Roure C. Training and teaching decision-making in team sports games. The use of small-sided games to test theoretical models and results of research in sports science. eJRIEPS. 2016; 37 :58–79. [ Google Scholar ]
• Kermarrec G, Bossard C. ICSport 2014-Proceedings of the 2th International Conference on Sport Sciences and Technological Supports. Rome, Italy: 2014. Shared Understanding and Coordination in Team Sports. Contribution of Viewpoints Changes and Shared Information Displays for Team Situation Awareness Training; pp. 26–28. October. [ Google Scholar ]
• Klein C, DiazGranados D, Salas E, Le H, Burke CS, Lyons R. The effect of team building interventions on team outcomes: An update and extension. Small Gr Res. 2008; 40 (2):181–222. [ Google Scholar ]
• Macquet AC. Getting on the same page: a method to study the consistency of coaches’ and athletes’ situation understanding during training sessions and competitions. The Sport Psychol. 2013; 27 :292–295. [ Google Scholar ]
• Macquet AC, Stanton NA. Do the coach and athlete have the same picture of the situation? Distributed situation awareness in an elite sport context. Appl Ergon. 2014; 45 :724–733. [ PubMed ] [ Google Scholar ]
• McEwan D, Beauchamp MR. Teamwork in sport: A theoretical and integrative review. Int Rev Sport Exer Psy. 2014; 7 :229–250. [ Google Scholar ]
• Moreno MP, Moreno A, García-González L, Hernández AUC. An intervention based on video feedback and questioning to improve tactical knowledge in expert female volleyball players, Perc. & Mot. Skills. 2016; 122 (3):911–32. doi: 10.1177/0031512516650628. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Price A, Collins D, Stoszkowski J, Pill S. Coaching games: comparisons and contrasts. J Hum Kinet. 2018; 6 (1):126–131. doi: 10.1123/iscj.2018-0015. doi: 10.1123/iscj.2018-0015. [ CrossRef ] [ CrossRef ] [ Google Scholar ]
• Raab M. Think SMART, not hard - a review of teaching decision-making in sport from an ecological rationality perspective. Phys Educ Sport Peda. 2007; 12 (1):1–22. [ Google Scholar ]
• Renshaw I, Araújo D, Button C, Chow JI, Davids K, Moy B. Why the Constraints-led Approach is not Teaching Games for Understanding: A Clarification. Phys Educ Sport Peda. 2016; 21 (5):459–480. [ Google Scholar ]
• Salas E, Cooke NJ, Rosen MA. On teams, teamwork, and team performance: Discoveries and Developments. Hum Factors. 2008; 50 (3):540–547. [ PubMed ] [ Google Scholar ]
• Salas E, Shuffler ML, Thayer AL, Bedwell WL, Lazzara EH. Understanding and Improving Teamwork in Organizations: A Scientifically Based Practical Guide. Hum Resour Manage. 2014; 54 (4):1–25. [ Google Scholar ]
• Stolz S, Pill S. Teaching games and sport for understanding: Exploring and reconsidering its relevance in physical education. Eur Phys Educ Rev. 2014; 20 (1):36–71. doi: 10.1177/1356336X13496001. epe.sagepub.com [ CrossRef ] [ Google Scholar ]
• Strauss A, Corbin J. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Sage: Thousand Oaks; 1998. [ Google Scholar ]
• Tan CWK, Chow JY, Davids K. How does TGfU work? Examining the relationship between learning design in TGfU and a non-linear pedagogy. Phys Educ Sport Peda. 2012; 17 (4):331–348. [ Google Scholar ]
• Travassos B, Gonçalves B, Marcelino R, Monteiro R, Sampaio J. How perceiving additional targets modifies teams’ tactical behavior during football small-sided games. Hum Movement Sci. 2014; 38 :241–250. doi: 10.1016/j.humov.2014.10.005. [ PubMed ] [ CrossRef ] [ Google Scholar ]

Learn how to use the input-process-output (IPO) model

You might have read that the input-process-output (IPO) model can be helpful in business and other organizations, but you’re not sure precisely what it means and how to apply it. That’s understandable — the phrase might sound like a complex computer system or an intimidating theory.

The reality is that the IPO model is very easy to understand. While it has roots in computer programming, it appears in many different industries and settings because it’s an effective way to plan, analyze, and improve how work gets done.

After reading this post, you’ll understand what the input-process-output model is all about and be ready to apply it to your business processes. You’ll learn:

• What the input-process-output (IPO) model is

How to use the IPO model

Benefits of the ipo model, examples of input-process-output in different industries, best practices when thinking about ipo, what is input-process-output (ipo).

Input-process-output (IPO) — also called an IPO model or IPO diagram — is a visual tool used to describe a workflow, the flow of information, or activities within a system. An IPO diagram helps you identify all the factors that influence a process and all the process’s outcomes, and it gives you a structured approach to analyzing and improving the system.

The IPO diagram consists of three columns listing inputs on the left, describing the process in the middle, and then tracking the outputs on the right. By diagramming a process in this way, almost any business — including computer science, systems analysis, and business analysis — can better identify the cause of a problem and improve a system’s performance.

As part of Six Sigma methodology , the IPO model fits within a more complex technique called DMAIC — which stands for Define, Measure, Analyze, Improve, and Control. The input-process-output model is an important part of the Define stage of DMAIC because it helps clarify and define a project’s goals, scope, and boundaries. This clarity helps to establish a solid foundation for the subsequent stages.

IPO assumes that if we control causal factors, we can also control their effects. Drawing the workflow using an IPO diagram can help visualize any system where it’s difficult to see how all the pieces connect. Although it’s primarily descriptive, teams can use IPO for analytical, quality control, or planning purposes. It’s useful in production, manufacturing, teamwork, and many other areas.

Creating your own initial IPO model is relatively easy. The IPO diagram gives you a simple framework for listing the inputs and outputs of any system. The details will depend on your industry and the specific process you want to explore.

Getting the details right — and knowing the difference between inputs, process steps, and outputs — can require careful thinking. Let’s examine each category:

• Input can include data, information, or resources that enter the system.
• Process can include activities, transformations, or operations performed on the inputs.
• Output can include results, products, or outcomes produced by the processes.

In manufacturing, a general, high-level IPO diagram might look something like this:

Although this diagram establishes a cause-and-effect pattern from left to right, you don’t necessarily need to start with inputs. If you’re planning a new process, try starting with the desired output and working back. The output should be thorough, specific, and measurable. Output is usually focused on four categories of standards that organizations are trying to achieve — quality, cost, time, and safety.

One drawback of the IPO method is it’s hard to account for random variables. To identify all possible inputs, consider people, environmental factors, methods, measurements, materials, and machines. As you define the process, break it down into clearly defined steps. Once the model is complete, you can analyze the process to determine relationships and identify areas for improvement.

There are many benefits to using the IPO model, such as:

• It’s easy to use . You don’t need to know how to use software or have specific expertise and training. Anyone can create an IPO diagram and spend time thinking about factors in a process.

• It’s versatile . It’s used in different industries and can be applied to almost any business process. You don’t have to follow a set of prescribed rules. You can apply this framework creatively, depending on your goals.

• It helps define key process input variables. Use the model to gain insights into how different factors contribute to the overall output and identify how to improve your input.

• It helps streamline operations . Identify bottlenecks, redundancies, or inefficiencies in operations. This understanding allows teams to reconfigure processes, allocate resources effectively, and streamline operations for better performance and productivity.

• It helps with problem-solving. Businesses can analyze the IPO diagram to identify potential causes and effects when an issue or challenge arises. They can pinpoint areas where disruptions may occur, allowing them to troubleshoot and rectify the problem more effectively.

• It can enable training and documentation. The model provides a visual representation of a business process, making it easier to convey information and train new employees. IPO can be a reference tool for documenting and preserving knowledge about processes within an organization.

• It helps clarify process steps and why. It can help eliminate what does not produce a good outcome.

• It’s a good communication tool. The model allows teams or stakeholders to visualize and discuss the flow of information within a system. It facilitates effective communication and collaboration by providing a common understanding of how inputs become outputs. This shared understanding enables teams to work together more efficiently and make informed decisions.

• It’s a good introduction to more complicated Six Sigma process mapping. The SIPOC (Supplier, Input, Process, Output, Customer) diagram adds the supplier to the start of the process chain and the customer to the end of it. This method can extend the IPO diagram beyond internal processes to map out a larger chain of cause and effect. IPO is also a helpful concept to master before exploring value stream mapping .

The input-process-output model emerged in the twentieth century as a fundamental model for describing complex computer systems. However, IPO quickly found applications outside of computer programming as a practical methodology in general systems theory and design. Many businesses found that by diagramming processes, they could improve quality and efficiency. IPO diagrams are now used in various industries. Some of the most common are:

• Manufacturing. The IPO model can show the most important factors influencing production outcomes. Companies can then identify bottlenecks, optimize resource allocation, improve quality, and enhance efficiency.
• Commerce. In online order processing, the input happens when a customer places an online order. The process happens when the order is received, the cost is calculated, and payment is processed. The output includes an order confirmation, an inventory check, shipping, and a confirmation email.
• Software development. A software development process could include gathering requirements, designing, coding, testing, and deployment. In software, it’s a good idea to start with output, break down the input variables, and determine the process of code needed to generate a fully functioning application as the output.

You might also encounter the use of IPO diagrams in the social sciences, food and hospitality, economics, finance planning, and other areas. The principles of the model are foundational enough that they can be applied almost anywhere.

While practices differ depending on the industry, if you are looking to improve the performance of a team or system, there are a few basic principles to follow when applying this model.

To avoid common pitfalls, plan ahead and check that your diagram is:

• Achievable. Define an attainable scope for your processes and make sure your processes are within that scope.
• Comprehensive. Consider all the possible outputs before capturing or changing your inputs. Likewise, check that you have anticipated all possible inputs, both those you add intentionally and others that might be harder to control.
• Inclusive. Make sure to involve the whole team to avoid bias on what should go into the inputs and outputs.

Get started with the input-process-output model

The IPO model is a simple but effective way to think carefully about cause and effect in small and large systems. Whether you want to improve teamwork, discover the cause of a problem, increase revenue, or design a more efficient system, this framework can help you achieve your business goals.

When you’re ready to get started, decide what business process you want to analyze and sketch it out with paper and pen using this method. Then explore Adobe Workfront , which can help you design a workflow using the input-process-output model and identify all the factors in your work processes that lead to success.

Adobe Workfront

Workfront is enterprise work management software that connects work to strategy and drives better collaboration to deliver measurable business outcomes. It integrates people, data, processes, and technology across an organization so you can manage the entire lifecycle of projects from start to finish. By optimizing and centralizing digital projects, cross-functional teams can connect, collaborate, and execute from anywhere to help them do their best work.

Take a product tour or watch the overview video to learn more.

https://business.adobe.com/blog/basics/dpu-dpmo-ppm-rty

https://business.adobe.com/blog/basics/what-is-workflow-automation

https://business.adobe.com/blog/basics/workplace-collaboration-tips

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Implementation of Input-Process-Output Model for Measuring Information System Project Success

Journal Paper

Measurement of the information system (IS) project success has become the interesting topic for researchers and practitioners since the Standish Group published their findings in 1994. Project success theory is the main concept in this topic, but this theory is still an ambiguous concept and lack in agreement among researchers and practitioners. They are also still tend to focus on single or partial dimension. Therefore, they did not get a clear picture of the system measurements. This study developed an alternative model of the project success measurement based on input-process-output (IPO) model. The development was conducted using comparison, adoption, adaptation, and combination the previous theories and models: Davis's IPO model, the project success theories, Delone and McLean' model, and the project classificatory framework. As indicated in most studies that most of models are developed using the previous theories and models rather than on empirical proofs. The result is a IS project success model consisting of 9 variables and 36 relationships among the variables. Although, the model is only a conceptual model, but it was developed completely and coherently considering three main aspects of project success measurement, namely: processional and causal models, project success theories, and the influence concept of project environment.

Related Papers

A'ang Subiyakto

In this research, adoption of the DeLone and McLean (D&M) information system (IS) success model and its adaptation with the project success theories were used to explore state of an IS project success and to examine factors which affect the success. A survey towards the internal project stakeholders in a university was carried out with a response rate of 48% (n=62). Partial least squares-structural equation modelling (PLS-SEM) analysis then was applied because of the sample size. Majority respondents (80.7%) represented that the success level is more than 50% where information quality, system quality, service quality, system use, and user satisfaction substantially explain 58.8% of variance in the success variable. Although, a numeral of the findings was reproducible with the prior studies, these findings also presented inconsistencies, particularly connected to aspects of information quality and organization utilization. Consequently, researchers and practitioners will remain firm to profit from the data catered in this study and it is hoped that future research will establish upon the findings described herein as efforts are pulled in to make the IS project success particularly in the sampled institution.

This article illustrates how influential arguments were used to validate an information system (IS) project success model using the inductive-qualitative method rather than continual hypothesis testing. The purpose of the study was to explore the validity and feasibility of the model using a focus group study (FGS) to respond that many IS scholars were under more and more pressure in their research model validations. Most of them claimed that the quantitative validation was only the method to validate their models. Although they may have performed the qualitative validation, only a few of them realized that they have applied this method. This article will be valuable, especially to prove, describe, and illuminate the context and condition where the model validation had been performed qualitatively before the scholar carried out quantitatively the validation. The results represent the four model validation points regarding the modeling process, methodological aspect, and resource ava...

Open J. Inf. Syst.

Mark Harwardt

This work investigates the effects that different success criteria and their dimensions may have on the success of IT projects. It focuses on a model that represents the management&#39;s view of the success of an IT project. This is of particular interest due to demand for developing and examining such a model. To show the effects of the success criteria and their dimensions a survey of 646 participants was conducted. The effects of the criteria and dimensions on IT project success were subsequently studied with structural equation modeling. Because of some inconsistencies within the original model of IT project success a deducted model had to be developed. Some of the success criteria and dimensions had to be rearranged or removed from the original model due to the results of the study. The new model shows that the perception and the results of a project have a significant impact on the success rating of an IT project.

maziyar maleki

Abd Rahman Ahlan , A'ang Subiyakto , husniteja UIN Jakarta

This study examines the input factors that were reputed theoretically affecting the information system (IS) project success in term of the processional and causal perspectives. Adopting three of the four dimensions from the McLeod and MacDonell’s (M&M’s) classification project framework dimensions, the study is initiated by inviting the internal project stakeholders in a sampled institution. A stratified sampling then identified 130 people who experienced in the projects as the sample, contacted 90 of the samples via e-mail and distributed the paper-based questionnaire into 40 certain people especially who are on the managerial level. A number of 62 (48%) valid responses, then were analyzed using the partial least squares-structural equation modeling (PLS-SEM) software, i.e. SmartPLS. The significances of the whole path coefficients, the acceptances of the overall hypotheses, the relevances of the three predictors relevances, and the moderate coefficient determination of the IS project success variable may present acceptability of the proposed model for the subsequent studies.

A'ang Subiyakto , Abd Rahman Ahlan

This paper elucidates the sequential revisions of an information system (IS) project framework across the research model development and its examinations. The authors adopted, adapted, and combined five concepts of the project management discipline and the information processing theory to revise the framework. Besides the use of this multi-dimensional perspective, the authors were also succeeded to present an interrelation between the framework and the examined model within a coherent representation. It was one of the essential points of this model development study, in particular for presenting the research focus. It may be trivial issue for the experts in the research fields, but the coherent illustration is one of the critical issues in the validity measurement of a model, whereas the inexpert ones may need a guideline to represent the interrelationship. Such points became the main contribution of this study to fill the gap in the literatures, particularly in the lack of comprehensive detail of a research model development.

International Journal of Information Technology Project Management

Alan Peslak

One of the most important issues for organizations and information technology professionals is the success of information technology (IT) projects. This study reviews a survey of financial executives and examines their views on aspects of project management and project success. First, it was found that overall systems development projects are viewed as being successful by organizations. Next, a series of analyses were performed to assess several variables’ impact on IT project success. Skilled project measurement was found to result in higher IT project success. Restrictions on IT application development were found to correlate to lower IT project success. The most important project consideration did not affect project success. Finally, a significant positive relationship was found between the IT project success and overall IT returns. The implications, limitations, and conclusions of these findings are discussed. The study can be used as a basis for further exploration on project m...

Physical Review B

Giuseppe Iannaccone

World Journal of …

Alejandra Fernandez Fresno

RELATED PAPERS

Cristian Estades

Journal of Psychosexual Health

Eepsita Mishra

Philippe Fajon

Instituto de Economía y Sociología, INTA

Juan Medina

Journal of Infection

tatiana Galperine

Ginanjar Pamungkas

Food Science and Nutrition Studies

Pradeep Perera

Acta Neuropathologica Communications

David Gutmann

JOURNAL OF APPLIED MANAGERIAL ACCOUNTING

Muhammad Slamet

CONVICT CRIMINOLOGY FOR THE FUTURE

Jeffrey Ian Ross

Konrad Stadler

The American Journal of Clinical Nutrition

Philip Rijken

ACM SIGART Bulletin

Joel Gyllenskog

International Journal of Dentistry and Oral Science

Suresh Venugopalan

Private Standards and Global Governance

Tim De Meyer

Mathematical sciences

abdelbasset felhi

Procedia Computer Science

Tobias Braun

Josefina Santos Carmelina Menchú García

Divan edebiyatı sözlüğü

KAZIM MİRŞAN

办理迪肯大学毕业证书成绩单 澳洲Deakin毕业证书办理

Aviation, Space, and Environmental Medicine

Reza Bidaki

International Journal of Architecture, Engineering and Construction

simon khoury

The Lancet Global Health

Valentina Cambiano

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024