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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:

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

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

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

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

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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...

husniteja UIN Jakarta , A'ang Subiyakto

Historically, scholars have been determining deductively critical success factors (CSFs) since the end 1970s. Meanwhile, most of researchers in the information system (IS) project performance studies have been performing inductively this process. Definitely, each of both methods has strengths and weaknesses, but we argue that researchers and practitioners will get an alternative choice if both methods are combined for sharing the caracteristics. This study is conducted to respond this issue. Authors combined both methods using a compatibility analysis to inprove the result validity. This paper contributes to the debates on ensuring the interrelationship between the project succes criteria and their CSFs, incorporating the new factors, decomposing complexity of the project, and understanding focus of the stakeholders in the IS project environment. Although, the proposed method is only a combination of both previous methods, but this proposition is proposed together with a case study to prove its implementation feasibility

Abd Rahman Ahlan

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Project Inputs, Activities, Outputs, Outcomes, Impact and Results

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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.

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What is an input-process-output diagram in a quantitative research?  

Insight from top 4 papers.

An input-process-output diagram in quantitative research is a model that represents the relationship between the input, process, and output variables. It is used to analyze and understand the factors that contribute to the desired outcome. The diagram helps to organize and visualize the flow of information and activities within a system. The input represents the initial data or information that is provided to the system. The process refers to the actions or operations that are performed on the input to produce the desired output. The output is the result or outcome of the process. This diagram is commonly used in various fields, such as conceptual design teams [1] , actuated wall-bounded shear flows [2] , and hydrology [3] [4] . It provides a framework for studying and analyzing complex systems and can be used to identify factors that influence the output.

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The Input-Process-Output (IPO) model evolved into a significant research methodology through its foundational development and subsequent adaptations across various fields. Initially conceptualized by Wassily Leontief in the 1930s, the input-output analysis aimed to illustrate economic interrelationships, leading to the creation of national input-output tables that provided a structured approach to economic modeling. Over the decades, this model has been refined and digitized, allowing for more complex analyses of macroeconomic dynamics and structural stability, as demonstrated in recent studies that integrate systemic and cross-sectoral analyses. Furthermore, the incorporation of foreign sectors into traditional models has expanded the IPO framework's applicability to global economic contexts, addressing import and export dynamics. The model's versatility is further enhanced by its ability to utilize various metrics and adapt to different analytical needs, solidifying its role in both economic and computational research methodologies. Thus, the IPO model's evolution reflects its adaptability and relevance in contemporary research.

Input processing in a research conceptual framework refers to the cognitive mechanisms involved in interpreting and understanding incoming information. This process is crucial in the acquisition of a non-primary language, where learners typically rely on a meaning-based approach when processing input. Additionally, in the context of developing a conceptual framework for research activities targeted at university students and professors, input processing plays a role in enriching knowledge and improving individual and institutional productivity in research. Understanding how individuals process input is essential for refining cognitive process models, which aim to capture the ongoing information transformations at different levels of abstraction. By incorporating insights from various studies, a comprehensive understanding of input processing can be achieved within the broader framework of research activities and cognitive modeling.

Input and output are fundamental components in various fields. In the realm of economic analysis, input-output models play a crucial role in understanding interregional economic relationships and global issues like climate change and international trade . In the context of second language acquisition, input is highlighted as a prerequisite for generating comprehensible output, emphasizing the importance of mastering linguistic elements for effective communication . Furthermore, in the realm of economic measurement, the use of appropriate index numbers, such as the proposed quadratic-mean-of-order-r indexes, is essential for precise evaluation of economic phenomena like output, input, and productivity, with these indexes unifying existing measures and being considered superlative . Additionally, in energy analysis, input-output analysis is deemed suitable for modeling complex energy production systems, including industrial plants and clean energy technologies, showcasing the versatility of input-output methodologies .

The input-process-output (IPO) model is a framework used in various fields to describe the flow of information or data within a system. It consists of three main components: input, process, and output. The input represents the data or information that is provided to the system, which serves as the starting point for processing. The process refers to the actions or operations performed on the input to transform it into a desired output. Finally, the output is the result or outcome produced by the system after the processing of the input. This model is widely used in fields such as computing, cognitive neuroscience, public participation, and economic analysis.

The input-process-output (IPO) model is a paradigm used in various fields, including economics, computing, and public administration. It represents the flow of information or resources within a system. In the context of economic networks, the IPO model is used to analyze the linkages and interactions among different sectors of an economy . In cognitive neuroscience, the IPO model is employed to represent the input-output function of a computational model that preserves patterns of relations in the target domain . In the study of participatory institutions, the IPO model is used to analyze the impact of input factors on process and output factors, with process serving as a mediator between input and output . In the field of regional input-output analysis, the IPO model is used to estimate input-output transactions and calculate the economic impact of various factors . In computer programming, the IPO model is used to plan and document a program's control structure, inputs, processes, and outputs .

Trending Questions

The hydrometer method for particle size distribution analysis offers several advantages and limitations, particularly when considering varying speeds of measurement. ## Advantages - **Rapid Analysis**: The hydrometer method allows for quick assessments of particle size distribution, which is crucial in applications like aerosol analysis where time efficiency is essential. - **Versatility**: It can effectively measure a wide range of particle sizes, from nanometers to micrometers, making it suitable for diverse materials. - **Cost-Effectiveness**: Compared to more complex methods like ICP-MS, the hydrometer method is generally less expensive and requires less specialized equipment. ## Limitations - **Speed Sensitivity**: The accuracy of the hydrometer method can be affected by the speed of measurement, as rapid changes in particle dynamics may not be captured effectively. - **Hydrodynamic Interactions**: At higher speeds, hydrodynamic interactions can complicate the interpretation of results, potentially leading to inaccuracies in size distribution. - **Calibration Needs**: The method requires careful calibration to account for variations in fluid properties and particle interactions, which can be challenging. While the hydrometer method is advantageous for its speed and cost, its limitations in accuracy and the need for careful calibration highlight the importance of considering the specific context of particle size analysis.

The Wi-Fi-based class attendance method proposed by Tan and Tang in 2017 presents several limitations that impact its effectiveness. These limitations primarily stem from the inherent challenges of using Wi-Fi signals for attendance tracking in dynamic environments. ## Limitations of Wi-Fi-Based Attendance - **Environmental Interference**: The accuracy of attendance estimation can be compromised by external factors such as adjoining rooms and outdoor areas, which can lead to miscounts of connected devices . - **Network Load Balancing**: Variations in network load can affect the number of devices detected, making it difficult to obtain a reliable count of attendees . - **User Behavior**: Students may connect to the Wi-Fi without actually attending the class, leading to inflated attendance figures . - **Technical Complexity**: Implementing machine learning models to accurately infer attendance from Wi-Fi data requires sophisticated algorithms and may not be feasible in all educational settings . While the method offers a non-intrusive alternative to traditional attendance tracking, these limitations highlight the need for complementary approaches to ensure accurate monitoring of student presence.

Detecting deformation in volleyball impacts through computer vision involves advanced techniques that enhance accuracy and efficiency. Recent studies highlight several effective methods. ## YOLOv4 Framework - The variant YOLOv4 framework is utilized for volleyball trajectory prediction, effectively addressing challenges like rapid movement and varying target scales. It employs a new loss function based on Gaussian distribution to improve detection of small targets, significantly enhancing performance in video analysis. ## Artificial Neural Networks - A method combining artificial neural networks for movement object detection has been developed, focusing on optimizing threshold selection for improved operational efficiency. This approach is crucial for recognizing and analyzing player movements during impacts. ## Support Vector Machines - Support Vector Machines (SVM) have been applied for action recognition in volleyball, addressing high sample sizes and improving recognition rates. This technique is beneficial for analyzing specific actions related to ball impacts. While these techniques show promise, challenges remain in real-time processing and environmental factors that can affect detection accuracy.

Gaussian Processes (GPs) offer significant advantages over traditional methods in modeling and interpolating the ambient magnetic field, particularly in terms of accuracy and computational efficiency. The following sections highlight key aspects of this comparison. ## Enhanced Mapping Accuracy - GPs provide a robust framework for mapping spatial variations in magnetic fields, effectively handling noisy data from magnetometer arrays. Edridge and Kok demonstrated that GPs improve map quality by incorporating relative positions of sensors, even with approximate location data. - Menzen et al. utilized structured kernel interpolation to enhance GP regression, achieving superior accuracy in large-scale magnetic field mapping compared to traditional methods, with the ability to process up to 40,000 measurements rapidly. ## Computational Efficiency - Traditional methods often struggle with scalability, particularly as data volume increases. GPs, especially with approximations like Sparse Variational methods, can efficiently manage large datasets while maintaining predictive performance, as shown in Iong et al.'s work on geomagnetic perturbations. ## Versatility in Applications - GPs are adaptable across various domains, including electromagnetic field reconstruction, where Tesfay and Clavier demonstrated effective spatial estimation using minimal sensor data. This versatility contrasts with traditional methods that may require more extensive data or specific conditions for effective modeling. While GPs present clear advantages, traditional methods may still be preferred in scenarios with limited data or where computational resources are constrained, highlighting the importance of context in selecting modeling approaches.

Designing a piano fitting in the outer wing box of an aircraft involves several critical considerations to ensure structural integrity, weight efficiency, and thermal management. ## Structural Integrity - The piano fitting must be integrated into the wing box without compromising its structural performance. Utilizing composite materials can enhance stiffness while minimizing weight, which is crucial for maintaining aerodynamic efficiency. ## Thermal Management - Thermal deformation is a significant factor; thus, the design should incorporate mechanisms that allow for expansion and contraction. A floating layer system can be employed to accommodate thermal changes, ensuring that assembly stresses are minimized. ## Optimization Techniques - Employing Reliability Based Design Optimization (RBDO) can help manage uncertainties in the design process, ensuring that the piano fitting meets performance criteria under various conditions. In contrast, while these design considerations focus on performance and efficiency, they may also lead to increased complexity in manufacturing and assembly processes, which could pose challenges in practical applications.

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2 The input–process–output model

The reading (Walley, 2017) introduced you to the operations input–process–output model.

Figure 1 shows the input–process-output model. The left-hand arrow, labelled ‘Input’, points to a rectangular box labelled ‘Process’. The right-hand arrow is labelled ‘Output’. Underneath the Input arrow there is a list entitled ‘Managing Input Resources’: facilities, equipment, staff, customers, suppliers, transport, materials, energy and information. Underneath the process box there is a list entitled ‘Managing Processes’: process flow, work-in-progress, process design, planning and scheduling, progressing and control and system improvement. Underneath the output box there is a list entitled ‘Managing Outputs’: Products and services, customer satisfaction, unit costs and environmental impact.

This framework shows that the operations management role is divided into three areas:

• Managing input resources – Operations managers must ensure that the right resources, such as people, equipment and materials, are available in the right quantity at the right time for the operation’s needs.
• Managing processes – All operations managers are responsible for processes. Processes are defined as a series of interlinked activities or steps that consume resources to meet a goal or output.
• Managing outputs – The operations function is responsible for meeting customers’ needs by delivering required products or services. The effectiveness and efficiency of the operation dictates how much resource is needed and this feeds straight through to unit cost and (where relevant) profitability.

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• Information System as an Input-Process-Output Model

One way of viewing possible relationships between data, information and knowledge is to consider an information system from the perspective of an IPO (input-process-output) model. On the input side we have data, as discussed previously. These data are then massaged or manipulated in some way (e.g., sorting, summarizing, filtering, formatting) to obtain information. Note that the transformation of data into information may be completed by a person (e.g., using a calculator) or by a computer program, although for our purposes we are typically more interested in situations where computer programs are employed.

A simple example could be the “what if” type of analysis that an electronic spreadsheet package offers. We can use our current understanding of a situation to develop a model of how sales will go up or down by a certain factor based on the amount we spend on advertising and other factors such as price.

The resulting information is used by a human to reach decisions (how many people to hire, how many products to produce, how much to spend on advertising). The outcomes of these decisions are observable results, such as sales volume during a certain time period, or the number and size of back-orders, etc. If these objective outcomes (results) are monitored and examined, then knowledge may be gained (e.g., how to avoid inventory shortages, or how to balance inventory carrying costs against costs associated with product shortages).

• 10058 reads
• The overall approach of the text
• The distinction between information systems and information technology
• Introduction
• Being a systems innovator
• Systems innovators are designers
• Innovations are new answers to problems
• Innovations are also reactions to change
• Exciting times for systems innovators
• General insights into human (and information) systems
• General implications for a systems innovator
• How can I innovate?
• What do innovations achieve?
• Innovations achieve new products and profits
• Innovations increase effectiveness
• Chapter editor
• What is an information system?
• IT is not information system
• Information technology
• More output with the same input
• Same output with less input
• Regulatory compliance
• Financial measures
• Return on Investment
• Managerial performance measures
• Information usage
• Customer and employee satisfaction
• Case questions
• Development process: from idea to detailed instructions
• Technology and innovation
• Alignment on objectives
• Useful life
• Iterative development
• Alternative approaches: “Big Bang”
• Alternative approaches: Prototyping
• Requirements elicitation
• Requirements prioritization
• Functional requirements formulation
• Quality requirements formulation
• Importance of architecture
• Selecting, extending or creating an architecture
• Physical interaction
• Interaction flow
• Media content
• Internal design
• Why we test
• The V model of verification, validation, testing
• Prepare the test
• Execute the test and record the results
• Find and correct errors
• Executing the other tests
• How much testing is enough?
• Prototyping
• Problems with the waterfall life cycle
• Development vs. maintenance
• Testing during maintenance
• The development of process management
• What is a process and what are the different types of processes?
• Process orientation as prerequisite for process management
• What is business process modeling and what is it good for?
• Modeling with ePK
• Software for process modeling and process support
• Process analysis and the benchmarking of processes
• A roadmap to process management
• Why IS projects fail
• Methodologies defined
• Waterfall model methodology
• Evolution of Methodologies
• Methodologies basic structure
• Selecting a Methodology
• Quality Selection criteria of Methodologies
• Methodologies Misuse
• Key concepts
• Discussions questions
• Demand for Knowledge Harvesting
• Employee Contribution and Resistance
• Issues of Organizational Culture
• Current Practices in Knowledge Harvesting
• Implementation Differences
• Bridge Building
• Recommendations
• Bibliography
• Sidebar: Exponential growth
• Progress in electronic technology
• Progress in storage technology
• Sidebar: Commonly used prefixes
• Software progress Internet resource:
• Batch processing
• Time sharing
• Personal computers
• Local area networks Sidebar: Internet client options
• Internet based software
• Open source software Internet resource:
• User supplied content
• Composite applications Sidebar: Using a Web service to add audio to an application
• Software as a service Internet resources:
• Mobile, portable and location-aware applications
• The long tail Internet resources
• Collaboration support applications Internet resources:
• Will the progress continue? Internet resource:
• Bumps in the information technology road
• Capacity to handle video traffic
• Data center capacity and electric power
• Vested interests can impede progress Internet resource
• Intellectual property restrictions Internet resource:
• Security and privacy
• Rational view
• Alternative views
• Decision environments (degree of structure)
• Data, information and knowledge
• The role of feedback
• Controlling
• Automating decisions
• Supporting complex decisions
• Knowledge management
• Business intelligence
• Purpose of this chapter
• What is not covered in this chapter
• How to use this chapter
• The Role of Data Management Technologies in Achieving Organizational Efficiencies
• Data management domain examples
• What are data?
• Information and meaning
• What must be represented?
• Granularity
• Assigning values
• Derived data values
• Representing composite entities
• Relationships
• one-to-many
• many-to-many
• Constraints
• Traditional strategy and killer applications
• Moore’s Law
• Metcalfe’s Law
• Coasian Economics
• The flock-of-birds phenomenon
• The Fish-tank Phenomenon
• How Moore’s Law Affects Music and Gambling
• Metcalfe’s Law – Networks in Music and Wagering
• Coasian Economics: Transaction Costs in Online Music and Wagering
• The Flock-of-Birds Phenomenon: Lawlessness in Music and Gambling
• The Fish-tank Phenomenon: The Power of Creative Individuals in Music and Wagering
• Summary and conclusions
• The supply chain: The focus of external integration
• History of B2B systems
• What is a B2B system?
• E-marketplaces
• Web services are based on four emerging Internet standards:
• High setup costs
• Unsustainable growth
• Unconvincing pricing
• Not enough profit
• Ineffective public infrastructure
• Restrictive regulations from domestic governments
• Organizational inertia
• Buyer's fear about the capability of the B2B solution provider
• Buyer's fear about the integrity of the B2B solution provider
• Supplier's fears of competitive bidding
• Few benefits to suppliers
• Opportunities: New business models enabled by B2B systems
• Future of B2B systems
• Chapter editors
• How hardware, networking and software technologies affect file sharing
• How Peer-to-Peer File Sharing Works
• Peer-to-Peer Impacts
• What is an organization?
• Management and the Division of Labour
• What Do Managers Do?
• Development of information and communication technology
• An interim summary
• Virtual Organizations – Outsourcing and Off-shoring
• Open source and related models
• Critique and Conclusions
• Principles for Managing the Informal Aspects
• Responsibility
• Principles for Managing the Formal Aspects
• Principles for Managing the Technical Aspects
• Key Concepts: IT architecture, infrastructure and applications
• Information Technology Infrastructure Library (ITIL): An IT management framework
• Defining what constitutes system failure: Confidentiality, integrity and availability
• Potential causes of systems failure
• Information Assurance Policies, Procedures, Standards and Education
• Risk assessment: New systems and old
• Managing change and system configurations The Potential Costs of Poorly Managed Change: Results of an Untested Software Upgrade
• System monitoring and incident response
• System backups Nairobi Fire and the Loss of Irreplaceable Documents
• Planning for disaster recovery and business continuity
• Mitigating risks with technical controls
• Moving Forward as a Systems Innovator
• The Promise of Information Technology
• The Promise of Business
• The Challenges of Information Technology
• Some Resources for Systems Innovators
•  Back Matter

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