importance of quantitative research in business

Importance Of Quantitative Research In Business And Marketing

Statistics reveal that over 88% of marketers believe data-driven strategies are crucial for success, underscoring the pivotal role that quantitative research plays in shaping effective business and marketing strategies. Quantitative research isn’t just a tool; it’s a cornerstone that empowers businesses to make informed choices, identify trends, and gauge customer preferences with precision. In this article, we delve into the compelling reasons why quantitative research stands as a bedrock of modern business and marketing endeavors, exploring its far-reaching impacts and applications.

What is Quantitative Research?

Quantitative research is a systematic and empirical approach to gathering and analyzing numerical data to uncover patterns, relationships, and trends. It involves the use of structured methodologies to collect data that can be quantified and statistically analyzed. By utilizing mathematical and statistical techniques, researchers aim to derive meaningful insights and draw conclusions from the collected data. This method is particularly valuable in providing concrete and measurable information, contributing to evidence-based decision-making in various fields.

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Brief Overview of Its Significance in Business and Marketing

In the dynamic landscape of business and marketing, quantitative research plays a pivotal role in shaping strategies and enhancing decision-making processes. Through quantitative research, businesses can measure consumer behavior, preferences, and attitudes with precision. This data-driven approach enables organizations to understand market trends, evaluate product performance, and assess the effectiveness of marketing campaigns. By quantifying variables and employing statistical analysis, businesses can identify actionable insights that drive growth, optimize operations, and gain a competitive edge in their respective industries.

Comparison with Qualitative Research

While both quantitative and qualitative research are valuable, they differ significantly in their approaches and objectives. Quantitative research focuses on measurable data and statistical analysis, aiming to generalize findings to larger populations. In contrast, qualitative research seeks to delve into the depth and context of individuals’ experiences and opinions through open-ended questions and qualitative data. Quantitative research utilizes closed questions and aims to establish trends and patterns, whereas qualitative research is geared toward understanding the “why” behind behaviors and attitudes.

Key Characteristics and Features

Key characteristics of quantitative research include its reliance on numerical data, statistical analysis, and generalizability. It follows a structured and systematic approach, with a focus on objectivity and replicability. Quantitative research aims to quantify variables, measure relationships, and provide reliable insights that can guide decision-making. By employing established statistical techniques, researchers can draw objective conclusions from the data collected.

Common Quantitative Research Techniques

• Survey Research:  Using structured questionnaires to collect data from a representative sample. Closed-ended questions yield quantifiable responses for statistical analysis.
• Correlational Research:  Investigating the statistical relationships between two or more variables, assessing associations without implying causation.
• Experimental Research:  Manipulating independent variables to establish cause-and-effect relationships, often conducted in controlled settings.
• Cross-Tabulation:  Categorizing and analyzing data based on demographic subgroups to identify trends and patterns.
• Data Cleaning and Analysis:  Preprocessing data to ensure accuracy and remove outliers or errors, followed by statistical analyses such as t-tests or ANOVA to derive meaningful insights.

In summary, quantitative research methodology offers a structured and data-driven approach to exploring phenomena, making it an essential tool for uncovering objective insights and informing decision-making in diverse fields. By comparing and contrasting it with qualitative research, understanding its key characteristics, and exploring common techniques, professionals can harness the power of quantitative research effectively.

I. Market Analysis and Segmentation

Target Audience Identification: Quantitative research plays a pivotal role in market analysis by enabling businesses to precisely identify and understand their target audiences. Through systematic data collection and statistical analysis, organizations can gain insights into demographic attributes, preferences, and behaviors of potential customers. By analyzing numerical data, businesses can identify trends and patterns that help define the characteristics of their ideal customers. This information empowers companies to tailor their marketing strategies, messaging, and product offerings to resonate with the specific needs and preferences of their target audience. For instance, a skincare brand can utilize quantitative research to identify age groups, gender distribution, and income levels of individuals most likely to purchase their products, thereby optimizing their marketing efforts for maximum impact.

Consumer Behavior Patterns: Quantitative research offers a powerful lens through which businesses can analyze consumer behavior patterns. By collecting numerical data on purchasing habits, preferences, and responses to marketing initiatives, companies can uncover valuable insights. For instance, an e-commerce platform can utilize quantitative research to understand which product categories are most popular among different customer segments, helping them allocate resources effectively. By examining shopping cart abandonment rates and conversion metrics, businesses can pinpoint areas for improvement in the online shopping experience and enhance user satisfaction. Moreover, quantitative analysis can reveal the impact of various marketing campaigns on consumer engagement, enabling businesses to fine-tune strategies for maximum ROI.

II. Product Development and Innovation

Test and Refine Product Concepts: Quantitative research serves as a reliable tool for testing and refining product concepts. Businesses can conduct surveys or experiments to gather numerical data on consumer responses to different product prototypes or features. By analyzing quantitative data, organizations can ascertain which attributes resonate most with their target audience. This approach empowers companies to optimize product designs, functionalities, and features based on concrete feedback. For instance, a tech company developing a new smartphone can utilize quantitative research to gather data on user preferences regarding screen size, battery life, camera quality, and other key features, ensuring that the final product aligns with consumer expectations.

Predict Market Acceptance: Quantitative research aids businesses in predicting the potential market acceptance of new products or services. By conducting surveys and analyzing numerical data, companies can gauge consumer interest, willingness to adapt, and purchase intent. This data-driven approach provides insights into the viability and demand for new offerings, helping companies make informed decisions about resource allocation, production, and marketing strategies. For example, an automobile manufacturer planning to introduce an electric vehicle can employ quantitative research to assess potential customers’ attitudes toward electric vehicles, charging infrastructure availability, and price sensitivity.

III. Performance Measurement and Optimization

Evaluate Marketing Campaigns: Quantitative research is a vital tool for evaluating the effectiveness of marketing campaigns. By collecting numerical data on key performance indicators (KPIs) such as click-through rates, conversion rates, and customer engagement metrics, businesses can measure the impact of their marketing efforts. Through statistical analysis, organizations can identify which campaigns resonate most with their target audience and generate the highest returns. This data-driven evaluation enables companies to allocate resources strategically and refine their marketing strategies for optimal outcomes.

Track Customer Satisfaction: Quantitative research enables businesses to systematically track and measure customer satisfaction levels. By employing structured surveys and questionnaires, organizations can gather numerical data on customer experiences, feedback, and perceptions of their products or services. Analyzing this data provides insights into areas of improvement and areas of strength. For instance, a hospitality chain can utilize quantitative research to assess guest satisfaction with accommodation, amenities, and customer service, identifying opportunities to enhance guest experiences and loyalty.

In summation, Quantitative research is an invaluable asset in the realms of business and marketing. Its applications span market analysis, consumer behavior insights, product development, and performance evaluation. By leveraging numerical data and statistical analysis, businesses can make informed decisions, drive innovation, and stay ahead of the curve in today’s competitive landscape.

IV. Advertising and Promotion

Message Effectiveness Assessment: Quantitative research is a valuable tool for assessing the effectiveness of advertising messages. By conducting surveys or experiments, businesses can gather numerical data on how different messages resonate with their target audience. This data-driven approach allows organizations to measure metrics such as recall, comprehension, and emotional impact. Analyzing these metrics enables businesses to determine which messages are most memorable, understandable, and persuasive to their audience. For instance, a cosmetics brand launching a new makeup line can utilize quantitative research to assess which ad copy and visuals generate the highest levels of consumer engagement and message retention.

Media Channel Optimization: Quantitative research plays a crucial role in optimizing media channels for advertising campaigns. By collecting numerical data on consumer preferences, behavior, and media consumption habits, businesses can make informed decisions about where to allocate their advertising budget. Through statistical analysis, organizations can identify which media channels (e.g., television, social media, print) are most effective at reaching their target audience. This information helps businesses maximize their reach and engagement by tailoring their advertising efforts to the platforms preferred by their audience. For example, a technology company launching a new gadget can employ quantitative research to determine whether its target audience is more active on social media or tech-related websites, allowing them to allocate resources accordingly.

Quantitative research empowers businesses to make data-driven decisions in the realms of advertising and promotion. By analyzing numerical data and conducting systematic assessments, organizations can refine their messaging strategies and optimize their media channel choices for maximum impact and ROI.

V. Customer Satisfaction and Feedback

Surveys and Feedback Analysis: Quantitative research is instrumental in assessing customer satisfaction and analyzing feedback. By conducting structured surveys and questionnaires, businesses can collect numerical data that quantifies customers’ opinions and experiences. These surveys can cover various aspects of the customer journey, such as product satisfaction, service quality, and overall experience. The collected data can then be analyzed using statistical techniques to identify trends, patterns, and correlations. This analysis provides valuable insights into areas of strength and areas that require improvement, allowing organizations to make informed decisions based on data-driven feedback. For example, an e-commerce company can utilize quantitative research to gauge customer satisfaction levels after making a purchase and analyze factors that contribute to positive or negative experiences.

Continuous Improvement Initiatives: Quantitative research plays a pivotal role in driving continuous improvement initiatives. By systematically collecting and analyzing numerical data from customer feedback, businesses can identify areas where enhancements are needed. These insights can guide strategic decisions to refine products, services, and processes. Additionally, organizations can use quantitative data to set measurable performance benchmarks and track progress over time. For instance, a hotel chain can implement quantitative research to monitor customer feedback related to cleanliness, staff friendliness, and amenities. By analyzing this data, the chain can identify trends and take proactive steps to enhance guest experiences.

Quantitative research empowers businesses to proactively address customer satisfaction and feedback. Through structured surveys and rigorous analysis, organizations can gain actionable insights that drive continuous improvement efforts, resulting in enhanced customer experiences and increased loyalty.

Advantages of Quantitative Research

I. Objectivity and Reliability

Quantitative research is characterized by its objectivity and reliability. By relying on numerical data and statistical methods, researchers can minimize the impact of personal biases and subjectivity on the results. The structured nature of quantitative research ensures consistency in data collection and analysis, leading to reliable findings. Objective measurements and standardized procedures contribute to the credibility of the research outcomes, making them more trustworthy for decision-making.

II. Data-Driven Decision Making

One of the significant advantages of quantitative research is its ability to facilitate data-driven decision-making. The empirical approach of gathering and analyzing numerical data allows organizations to base their decisions on concrete evidence rather than speculation or intuition. Businesses can make informed choices about product development, marketing strategies, customer satisfaction initiatives, and more by relying on the insights derived from quantitative research.

III. Scalability and Generalizability

Quantitative research offers the advantage of scalability and generalizability. With a representative sample and rigorous research design, findings from quantitative studies can be extended to larger populations or broader groups. This capability to draw insights about a larger segment of the population is invaluable for businesses seeking to understand customer preferences, market trends, and behaviors on a larger scale.

IV. Statistical Analysis for Insights

Quantitative research employs sophisticated statistical analysis techniques to extract insights from data. These statistical methods allow researchers to uncover patterns, relationships, and associations that might not be immediately apparent. Whether it’s identifying correlations between variables, testing hypotheses, or detecting trends over time, statistical analysis enhances the depth and breadth of insights obtained from quantitative research.

V. Long-Term Trend Identification

Another advantage of quantitative research is its potential for identifying long-term trends. By collecting numerical data over extended periods, researchers can detect patterns and changes that unfold gradually. This longitudinal perspective enables businesses to adapt to evolving market conditions, track shifts in consumer behavior, and make strategic adjustments over time.

Incorporating quantitative research into your strategy equips you with objective, reliable insights that inform decision-making, foster understanding of broad trends, and drive data-driven initiatives. While quantitative research is instrumental in uncovering facts and trends, it’s essential to complement it with qualitative methods to explore the underlying “why” and gain a comprehensive understanding of complex phenomena.

Steps in Conducting Quantitative Research

1. Problem Formulation and Research Questions

The first step in conducting quantitative research is to clearly define the problem or research question you want to address. This involves identifying a specific topic or issue that you want to investigate using quantitative methods. The research question should be focused, clear, and relevant to the field of study.

2. Hypothesis Development

Once the research question is defined, formulate one or more hypotheses that provide a clear statement of the expected relationship between variables. Hypotheses guide the research process by outlining the expected outcomes that will be tested and analyzed during the study.

3. Sampling Strategy and Data Collection

Selecting a representative sample from the target population is a crucial step. The sampling strategy determines how participants will be chosen to ensure that the findings can be generalized to the broader population. The sample size and sampling method (random sampling, stratified sampling, etc.) should be carefully considered to minimize bias and enhance the study’s external validity. After selecting the sample, data is collected using structured instruments such as surveys, questionnaires, or experiments.

IV. Data Analysis Techniques

Data analysis involves applying appropriate statistical techniques to the collected data. The choice of analysis methods depends on the research questions, hypotheses, and types of data collected. Common data analysis techniques include descriptive statistics (mean, median, mode), inferential statistics (t-tests, ANOVA), regression analysis, and correlation analysis. The goal is to uncover patterns, relationships, and associations in the data.

V. Interpretation of Results

Interpreting the results involves making sense of the data analysis in relation to the research question and hypotheses. Researchers examine statistical outputs and draw conclusions about whether the hypotheses were supported or rejected based on the data. It’s important to discuss the implications of the findings, the significance of the relationships observed, and any limitations of the study.

Throughout the steps, researchers need to adhere to ethical guidelines, ensure data privacy and confidentiality, and maintain the rigor of the research design. The interpretation of results should consider the broader context of the field of study and contribute to the body of knowledge in that area.

Conducting quantitative research requires careful planning, methodological expertise, and a systematic approach to ensure that the study is scientifically sound and the results are reliable and valid. Integrating qualitative insights and findings from other research methods can provide a comprehensive understanding of complex phenomena and contribute to well-rounded conclusions.

Challenges and Considerations

I. Sample Size and Representativeness

One of the challenges in quantitative research is determining an appropriate sample size that accurately represents the population of interest. A small sample size might not provide reliable results, while an excessively large one can be resource-intensive and unnecessary. Achieving a balance between sample size and representativeness is crucial to ensure the findings can be generalized to the broader population.

II. Survey Design and Questionnaire Construction

Designing effective surveys and constructing well-structured questionnaires is a critical consideration. Poorly designed surveys can lead to biased responses, inaccurate data, and difficulty in data analysis. Researchers must carefully craft questions, ensure clarity, avoid leading or loaded questions, and consider the order and format of questions to obtain reliable and valid data.

III. Data Analysis Complexity

Quantitative research often involves complex data analysis techniques, especially when dealing with a large number of variables or intricate statistical models. Researchers may encounter challenges in selecting the appropriate statistical methods, interpreting results accurately, and handling missing or skewed data. Proper training and expertise in statistical analysis are essential to ensure accurate interpretations.

IV. Addressing Potential Biases

Despite the objective nature of quantitative research, biases can still influence the research process. Selection bias, response bias, and non-response bias are examples of biases that can distort results. Researchers need to implement strategies to minimize biases, such as random sampling, ensuring diverse participation, and analyzing non-response patterns.

V. Ethical Considerations and Data Privacy

Ethical considerations are paramount in quantitative research. Researchers must obtain informed consent from participants, protect their privacy, and adhere to ethical guidelines. Additionally, with increasing concerns about data privacy, ensuring that collected data is stored securely and used only for its intended purpose is essential to maintain trust and compliance with regulations.

Navigating these challenges and considerations is essential to conduct robust and credible quantitative research. Proper planning, careful methodology design, expert statistical analysis, and ethical awareness contribute to the reliability and validity of research outcomes. Integrating quantitative research with other research methods, such as qualitative research, can provide a more comprehensive understanding of complex phenomena and enhance the overall quality of insights.

Case Studies

A. real-world examples showcasing successful applications.

• Market Segmentation and Targeting: A retail company uses quantitative research to segment its customer base by demographics, buying behavior, and preferences. This allowed them to tailor their marketing strategies to different segments and enhance customer engagement.
• Product Development: An electronics manufacturer conducted quantitative research to understand consumer preferences for features in a new smartphone. By analyzing the data, they identified the most desired features and integrated them into the final product design.
• Advertising Effectiveness: An advertising agency utilized quantitative research to measure the impact of an advertising campaign on brand awareness and consumer attitudes. The insights gained helped the agency fine-tune future campaigns for better results.

B. Highlighting the Role of Quantitative Research in Decision Making

• Evidence-Based Decision Making: A pharmaceutical company used quantitative research to evaluate the effectiveness of a new medication. By conducting clinical trials and analyzing data, they were able to provide evidence to support the medication’s efficacy and safety, leading to informed regulatory decisions.
• Market Entry Strategy: An international food chain uses quantitative research to assess the potential success of entering a new market. They collected data on consumer preferences, competition, and economic indicators to make data-driven decisions on market entry timing and locations.
• Customer Satisfaction Enhancement: A hotel chain employed quantitative research to measure customer satisfaction levels and identify areas for improvement. By analyzing guest feedback and ratings, they implemented changes that led to increased guest satisfaction and loyalty.

Quantitative research plays a crucial role in providing actionable insights that inform strategic decisions across industries. By collecting and analyzing numerical data, organizations can make informed choices that lead to improved products, services, and customer experiences. It allows businesses to measure the impact of their actions and optimize strategies for better outcomes.

Future Trends and Innovations

1. integration of big data and machine learning.

The future of quantitative research is closely tied to the integration of big data and machine learning technologies. As data continues to grow exponentially, researchers are exploring ways to harness this wealth of information to gain deeper insights. Big data analytics allows researchers to process and analyze massive datasets, revealing patterns and trends that were previously difficult to uncover. Machine learning algorithms, on the other hand, can identify complex relationships within the data, making predictions and recommendations based on historical patterns.

Researchers will increasingly rely on big data and machine learning to:

• Identify subtle correlations and trends across vast datasets.
• Predict consumer behavior and preferences with higher accuracy.
• Personalize marketing strategies and product recommendations .
• Enhance decision-making by analyzing a wider range of variables.

2. Automation of Data Collection and Analysis

The automation of data collection and analysis is another significant trend in quantitative research. Advances in technology, such as online surveys, mobile apps, and IoT devices, enable researchers to collect data more efficiently and in real-time. Automated data analysis tools and software can quickly process and interpret data, reducing the time and effort required for manual analysis.

Key benefits of automation in quantitative research include:

• Faster data collection and analysis, leading to quicker insights.
• Reduced human error in data entry and analysis processes.
• Improved scalability for large-scale studies.
• Enhanced agility in adapting research strategies based on real-time data.

3. Cross-Disciplinary Collaboration

Quantitative research is becoming increasingly interdisciplinary, with researchers from various fields collaborating to tackle complex problems. Cross-disciplinary collaboration allows for a broader perspective on research questions and the application of diverse methodologies. For instance, economists, psychologists, and sociologists might work together to study consumer behavior and its economic impact.

Benefits of cross-disciplinary collaboration include:

• Incorporating insights from multiple disciplines to gain comprehensive insights.
• Leveraging complementary expertise to address multifaceted research questions.
• Fostering innovation by bringing together diverse perspectives and methodologies.

In conclusion, the future of quantitative research is marked by the integration of big data and machine learning, the automation of data collection and analysis, and increased cross-disciplinary collaboration. These trends will enhance the accuracy, efficiency, and scope of quantitative research, enabling researchers to extract deeper insights and make informed decisions across various domains. At Kadence, we are committed to staying at the forefront of these innovations to help you achieve your research objectives effectively.

Quantitative research stands as a vital pillar in the realm of market research. Its reliance on hard facts, numerical data, and statistical analysis empowers researchers to obtain an objective and comprehensive understanding of people’s opinions and behaviors. By employing structured instruments like surveys and experiments, quantitative research generates reliable insights into social phenomena and allows for predictions, comparisons, and generalizations based on concrete numerical data.

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Business research: definition, types & methods.

10 min read What is business research and why does it matter? Here are some of the ways business research can be helpful to your company, whichever method you choose to carry it out.

What is business research?

Business research helps companies make better business decisions by gathering information. The scope of the term business research is quite broad – it acts as an umbrella that covers every aspect of business, from finances to advertising creative. It can include research methods which help a company better understand its target market. It could focus on customer experience and assess customer satisfaction levels. Or it could involve sizing up the competition through competitor research.

Often when carrying out business research, companies are looking at their own data, sourced from their employees, their customers and their business records. However, business researchers can go beyond their own company in order to collect relevant information and understand patterns that may help leaders make informed decisions. For example, a business may carry out ethnographic research where the participants are studied in the context of their everyday lives, rather than just in their role as consumer, or look at secondary data sources such as open access public records and empirical research carried out in academic studies.

There is also a body of knowledge about business in general that can be mined for business research purposes. For example organizational theory and general studies on consumer behavior.

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Why is business research important?

We live in a time of high speed technological progress and hyper-connectedness. Customers have an entire market at their fingertips and can easily switch brands if a competitor is offering something better than you are. At the same time, the world of business has evolved to the point of near-saturation. It’s hard to think of a need that hasn’t been addressed by someone’s innovative product or service.

The combination of ease of switching, high consumer awareness and a super-evolved marketplace crowded with companies and their offerings means that businesses must do whatever they can to find and maintain an edge. Business research is one of the most useful weapons in the fight against business obscurity, since it allows companies to gain a deep understanding of buyer behavior and stay up to date at all times with detailed information on their market.

Thanks to the standard of modern business research tools and methods, it’s now possible for business analysts to track the intricate relationships between competitors, financial markets, social trends, geopolitical changes, world events, and more.

Find out how to conduct your own market research and make use of existing market research data with our Ultimate guide to market research

Types of business research

Business research methods vary widely, but they can be grouped into two broad categories – qualitative research and quantitative research .

Qualitative research methods

Qualitative business research deals with non-numerical data such as people’s thoughts, feelings and opinions. It relies heavily on the observations of researchers, who collect data from a relatively small number of participants – often through direct interactions.

Qualitative research interviews take place one-on-one between a researcher and participant. In a business context, the participant might be a customer, a supplier, an employee or other stakeholder. Using open-ended questions , the researcher conducts the interview in either a structured or unstructured format. Structured interviews stick closely to a question list and scripted phrases, while unstructured interviews are more conversational and exploratory. As well as listening to the participant’s responses, the interviewer will observe non-verbal information such as posture, tone of voice and facial expression.

Focus groups

Like the qualitative interview, a focus group is a form of business research that uses direct interaction between the researcher and participants to collect data. In focus groups , a small number of participants (usually around 10) take part in a group discussion led by a researcher who acts as moderator. The researcher asks questions and takes note of the responses, as in a qualitative research interview. Sampling for focus groups is usually purposive rather than random, so that the group members represent varied points of view.

Observational studies

In an observational study, the researcher may not directly interact with participants at all, but will pay attention to practical situations, such as a busy sales floor full of potential customers, or a conference for some relevant business activity. They will hear people speak and watch their interactions , then record relevant data such as behavior patterns that relate to the subject they are interested in. Observational studies can be classified as a type of ethnographic research. They can be used to gain insight about a company’s target audience in their everyday lives, or study employee behaviors in actual business situations.

Ethnographic Research

Ethnographic research is an immersive design of research where one observes peoples’ behavior in their natural environment. Ethnography was most commonly found in the anthropology field and is now practices across a wide range of social sciences.

Ehnography is used to support a designer’s deeper understanding of the design problem – including the relevant domain, audience(s), processes, goals and context(s) of use.

The ethnographic research process is a popular methodology used in the software development lifecycle. It helps create better UI/UX flow based on the real needs of the end-users.

If you truly want to understand your customers’ needs, wants, desires, pain-points “walking a mile” in their shoes enables this. Ethnographic research is this deeply rooted part of research where you truly learn your targe audiences’ problem to craft the perfect solution.

Case study research

A case study is a detailed piece of research that provides in depth knowledge about a specific person, place or organization. In the context of business research, case study research might focus on organizational dynamics or company culture in an actual business setting, and case studies have been used to develop new theories about how businesses operate. Proponents of case study research feel that it adds significant value in making theoretical and empirical advances. However its detractors point out that it can be time consuming and expensive, requiring highly skilled researchers to carry it out.

Quantitative research methods

Quantitative research focuses on countable data that is objective in nature. It relies on finding the patterns and relationships that emerge from mass data – for example by analyzing the material posted on social media platforms, or via surveys of the target audience. Data collected through quantitative methods is empirical in nature and can be analyzed using statistical techniques. Unlike qualitative approaches, a quantitative research method is usually reliant on finding the right sample size, as this will determine whether the results are representative. These are just a few methods – there are many more.

Surveys are one of the most effective ways to conduct business research. They use a highly structured questionnaire which is distributed to participants, typically online (although in the past, face to face and telephone surveys were widely used). The questions are predominantly closed-ended, limiting the range of responses so that they can be grouped and analyzed at scale using statistical tools. However surveys can also be used to get a better understanding of the pain points customers face by providing open field responses where they can express themselves in their own words. Both types of data can be captured on the same questionnaire, which offers efficiency of time and cost to the researcher.

Correlational research

Correlational research looks at the relationship between two entities, neither of which are manipulated by the researcher. For example, this might be the in-store sales of a certain product line and the proportion of female customers subscribed to a mailing list. Using statistical analysis methods, researchers can determine the strength of the correlation and even discover intricate relationships between the two variables. Compared with simple observation and intuition, correlation may identify further information about business activity and its impact, pointing the way towards potential improvements and more revenue.

Experimental research

It may sound like something that is strictly for scientists, but experimental research is used by both businesses and scholars alike. When conducted as part of the business intelligence process, experimental research is used to test different tactics to see which ones are most successful – for example one marketing approach versus another. In the simplest form of experimental research, the researcher identifies a dependent variable and an independent variable. The hypothesis is that the independent variable has no effect on the dependent variable, and the researcher will change the independent one to test this assumption. In a business context, the hypothesis might be that price has no relationship to customer satisfaction. The researcher manipulates the price and observes the C-Sat scores to see if there’s an effect.

The best tools for business research

You can make the business research process much quicker and more efficient by selecting the right tools. Business research methods like surveys and interviews demand tools and technologies that can store vast quantities of data while making them easy to access and navigate. If your system can also carry out statistical analysis, and provide predictive recommendations to help you with your business decisions, so much the better.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Methodology

• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Business Analytics: What It Is & Why It's Important

• 16 Jul 2019

Business analytics is a powerful tool in today’s marketplace that can be used to make decisions and craft business strategies. Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are data literate and know how to interpret and analyze that information.

According to a study by MicroStrategy , companies worldwide are using data to:

• Improve efficiency and productivity (64 percent)
• Achieve more effective decision-making (56 percent)
• Drive better financial performance (51 percent)

The research also shows that 65 percent of global enterprises plan to increase analytics spending.

In light of these market trends, gaining an in-depth understanding of business analytics can be a way to advance your career and make better decisions in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” said Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics , in a previous interview . “If you’re able to go into a meeting and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Before diving into the benefits of data analysis, it’s important to understand what the term “business analytics” means.

Check out our video on business analytics below, and subscribe to our YouTube channel for more explainer content!

What Is Business Analytics?

Business analytics is the process of using quantitative methods to derive meaning from data to make informed business decisions.

There are four primary methods of business analysis:

• Descriptive : The interpretation of historical data to identify trends and patterns
• Diagnostic : The interpretation of historical data to determine why something has happened
• Predictive : The use of statistics to forecast future outcomes
• Prescriptive : The application of testing and other techniques to determine which outcome will yield the best result in a given scenario

These four types of business analytics methods can be used individually or in tandem to analyze past efforts and improve future business performance.

Business Analytics vs. Data Science

To understand what business analytics is, it’s also important to distinguish it from data science. While both processes analyze data to solve business problems, the difference between business analytics and data science lies in how data is used.

Business analytics is concerned with extracting meaningful insights from and visualizing data to facilitate the decision-making process , whereas data science is focused on making sense of raw data using algorithms, statistical models, and computer programming. Despite their differences, both business analytics and data science glean insights from data to inform business decisions.

To better understand how data insights can drive organizational performance, here are some of the ways firms have benefitted from using business analytics.

The Benefits of Business Analytics

1. more informed decision-making.

Business analytics can be a valuable resource when approaching an important strategic decision.

When ride-hailing company Uber upgraded its Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve speed and accuracy when responding to support tickets—it used prescriptive analytics to examine whether the product’s new iteration would be more effective than its initial version.

Through A/B testing —a method of comparing the outcomes of two different choices—the company determined that the updated product led to faster service, more accurate resolution recommendations, and higher customer satisfaction scores. These insights not only streamlined Uber’s ticket resolution process, but saved the company millions of dollars.

2. Greater Revenue

Companies that embrace data and analytics initiatives can experience significant financial returns.

Research by McKinsey shows organizations that invest in big data yield a six percent average increase in profits, which jumps to nine percent for investments spanning five years.

Echoing this trend, a recent study by BARC found that businesses able to quantify their gains from analyzing data report an average eight percent increase in revenues and a 10 percent reduction in costs.

These findings illustrate the clear financial payoff that can come from a robust business analysis strategy—one that many firms can stand to benefit from as the big data and analytics market grows.

Related: 5 Business Analytics Skills for Professionals

3. Improved Operational Efficiency

Beyond financial gains, analytics can be used to fine-tune business processes and operations.

In a recent KPMG report on emerging trends in infrastructure, it was found that many firms now use predictive analytics to anticipate maintenance and operational issues before they become larger problems.

A mobile network operator surveyed noted that it leverages data to foresee outages seven days before they occur. Armed with this information, the firm can prevent outages by more effectively timing maintenance, enabling it to not only save on operational costs, but ensure it keeps assets at optimal performance levels.

Why Study Business Analytics?

Taking a data-driven approach to business can come with tremendous upside, but many companies report that the number of skilled employees in analytics roles are in short supply .

LinkedIn lists business analysis as one of the skills companies need most in 2020 , and the Bureau of Labor Statistics projects operations research analyst jobs to grow by 23 percent through 2031—a rate much faster than the average for all occupations.

“A lot of people can crunch numbers, but I think they’ll be in very limited positions unless they can help interpret those analyses in the context in which the business is competing,” said Hammond in a previous interview .

Skills Business Analysts Need

Success as a business analyst goes beyond knowing how to crunch numbers. In addition to collecting data and using statistics to analyze it, it’s crucial to have critical thinking skills to interpret the results. Strong communication skills are also necessary for effectively relaying insights to those who aren’t familiar with advanced analytics. An effective data analyst has both the technical and soft skills to ensure an organization is making the best use of its data.

Improving Your Business Analytics Skills

If you’re interested in capitalizing on the need for data-minded professionals, taking an online business analytics course is one way to broaden your analytical skill set and take your career to the next level

Through learning how to recognize trends, test hypotheses, and draw conclusions from population samples, you can build an analytical framework that can be applied in your everyday decision-making and help your organization thrive.

“If you don’t use the data, you’re going to fall behind,” Hammond said . “People that have those capabilities—as well as an understanding of business contexts—are going to be the ones that will add the most value and have the greatest impact.”

Do you want to leverage the power of data within your organization? Explore our eight-week online course Business Analytics to learn how to use data analysis to solve business problems.

This post was updated on November 14, 2022. It was originally published on July 16, 2019.

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Home • Knowledge hub • What is quantitative research?

What is quantitative research?

Quantitative research is an important part of market research that relies on hard facts and numerical data to gain as objective a picture of people’s opinions as possible.

It’s different from qualitative research in a number of important ways and is a highly useful tool for researchers.

Quantitative research is a systematic empirical approach used in the social sciences and various other fields to gather, analyze, and interpret numerical data. It focuses on obtaining measurable data and applying statistical methods to generalize findings to a larger population.

Researchers use structured instruments such as surveys, questionnaires, or experiments to collect data from a representative sample in quantitative research. The data collected is typically numerical values or categorical responses that can be analyzed using statistical techniques. These statistical analyses help researchers identify patterns, relationships, trends, or associations among variables.

Quantitative research aims to generate objective and reliable information about a particular phenomenon, population, or group. It aims to better understand the subject under investigation by employing statistical measures such as means, percentages, correlations, or regression analyses.

Quantitative research provides:

• A quantitative understanding of social phenomena.
• Allowing researchers to make generalizations.
• Predictions.
• Comparisons based on numerical data.

It is widely used in psychology, sociology, economics, marketing, and many other disciplines to explore and gain insights into various research questions.

In this article, we’ll take a deep dive into quantitative research, why it’s important, and how to use it effectively.

How is quantitative research different from qualitative research?

Although they’re both extremely useful, there are a number of key differences between quantitative and qualitative market research strategies. A solid market research strategy will make use of both qualitative and quantitative research.

• Quantitative research relies on gathering numerical data points. Qualitative research on the other hand, as the name suggests, seeks to gather qualitative data by speaking to people in individual or group settings. 
• Quantitative research normally uses closed questions, while qualitative research uses open questions more frequently.
• Quantitative research is great for establishing trends and patterns of behavior, whereas qualitative methods are great for explaining the “why” behind them.

Why is quantitative research useful?

Quantitative research has a crucial role to play in any market research strategy for a range of reasons:

• It enables you to conduct research at scale
• When quantitative research is conducted in a representative way, it can reveal insights about broader groups of people or the population as a whole
• It enables us to easily compare different groups (e.g. by age, gender or market) to understand similarities or differences 
• It can help businesses understand the size of a new opportunity 
•  It can be helpful for reducing a complex problem or topic to a limited number of variables

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Quantitative Research Design

Quantitative research design refers to the overall plan and structure that guides the collection, analysis, and interpretation of numerical data in a quantitative research study. It outlines the specific steps, procedures, and techniques used to address research questions or test hypotheses systematically and rigorously. A well-designed quantitative research study ensures that the data collected is reliable, valid, and capable of answering the research objectives.

There are several key components involved in designing a quantitative research study:

• Research Questions or Hypotheses: The research design begins with clearly defined research questions or hypotheses articulating the study’s objectives. These questions guide the selection of variables and the development of research instruments.
• Sampling: A critical aspect of quantitative research design is selecting a representative sample from the target population. The sample should be carefully chosen to ensure it adequately represents the population of interest, allowing for the generalizability of the findings.
• Variables and Operationalization: Quantitative research involves the measurement of variables. In the research design phase, researchers identify the variables they will study and determine how to operationalize them into measurable and observable forms. This includes defining the indicators or measures used to assess each variable.
• Data Collection Methods: Quantitative research typically involves collecting data through structured instruments, such as surveys, questionnaires, or tests. The research design specifies the data collection methods, including the procedures for administering the instruments, the timing of data collection, and the strategies for maximizing response rates.
• Data Analysis: Quantitative research design includes decisions about the statistical techniques and analyses applied to the collected data. This may involve descriptive statistics (e.g., means, percentages) and inferential statistics (e.g., t-tests, regression analyses) to examine variables’ relationships, differences, or associations.
• Validity and Reliability: Ensuring the validity and reliability of the data is a crucial consideration in quantitative research design. Validity refers to the extent to which a measurement instrument or procedure accurately measures what it intends to measure. Reliability refers to the consistency and stability of the measurement over time and across different conditions. Researchers employ pilot testing, validity checks, and statistical measures to enhance validity and reliability.
• Ethical Considerations: Quantitative research design also includes ethical considerations, such as obtaining informed consent from participants, protecting their privacy and confidentiality, and ensuring the study adheres to ethical guidelines and regulations.

By carefully designing a quantitative research study, researchers can ensure their investigations are methodologically sound, reliable, and valid. 

Well-designed research provides a solid foundation for collecting and analyzing numerical data, allowing researchers to draw meaningful conclusions and contribute to the body of knowledge in their respective fields.

Quantitative research data collection methods

When collecting and analyzing the data you need for quantitative research, you have a number of possibilities available to you. Each has its own pros and cons, and it might be best to use a mix. Here are some of the main research methods:

Survey research

This involves sending out surveys to your target audience to collect information before statistically analyzing the results to draw conclusions and insights. It’s a great way to better understand your target customers or explore a new market and can be turned around quickly. 

There are a number of different ways of conducting surveys, such as:

• Email — this is a quick way of reaching a large number of people and can be more affordable than the other methods described below.
• Phone — not everyone has access to the internet so if you’re looking to reach a particular demographic that may struggle to engage in this way (e.g. older consumers) telephone can be a better approach. That said, it can be expensive and time-consuming.
• Post or Mail — as with the phone, you can reach a wide segment of the population, but it’s expensive and takes a long time. As organizations look to identify and react to changes in consumer behavior at speed, postal surveys have become somewhat outdated. 
• In-person — in some instances it makes sense to conduct quantitative research in person. Examples of this include intercepts where you need to collect quantitative data about the customer experience in the moment or taste tests or central location tests , where you need consumers to physically interact with a product to provide useful feedback. Conducting research in this way can be expensive and logistically challenging to organize and carry out.

Survey questions for quantitative research usually include closed-ended questions rather than the open-ended questions used in qualitative research. For example, instead of asking

“How do you feel about our delivery policy?”

You might ask…

“How satisfied are you with our delivery policy? “Very satisfied / Satisfied / Don’t Know / Dissatisfied / Very Dissatisfied” 

This way, you’ll gain data that can be categorized and analyzed in a quantitative, numbers-based way.

Correlational Research

Correlational research is a specific type of quantitative research that examines the relationship between two or more variables. It focuses on determining whether there is a statistical association or correlation between variables without establishing causality. In other words, correlational research helps to understand how changes in one variable correspond to changes in another.

One of the critical features of correlational research is that it allows researchers to analyze data from existing sources or collect data through surveys or questionnaires. By measuring the variables of interest, researchers can calculate a correlation coefficient, such as Pearson’s, to quantify the strength and direction of the relationship. The correlation coefficient ranges from -1 to +1, where a positive value indicates a positive relationship, a negative value indicates a negative relationship and a value close to zero suggests no significant relationship. Correlational research is valuable in various fields, such as psychology, sociology, and economics, as it helps researchers explore connections between variables that may not be feasible to manipulate in an experimental setting. For example, a psychologist might use correlational research to investigate the relationship between sleep duration and student academic performance. By collecting data on these variables, they can determine whether there is a correlation between the two factors and to what extent they are related. It is important to note that correlational research does not imply causation. While a correlation suggests an association between variables, it does not provide evidence for a cause-and-effect relationship. Other factors, known as confounding variables, may be influencing the observed relationship. Therefore, researchers must exercise caution in interpreting correlational findings and consider additional research methods, such as experimental studies, to establish causality. Correlational research is vital in quantitative research and analysis by investigating relationships between variables. It provides valuable insights into the strength and direction of associations and helps researchers generate hypotheses for further investigation. By understanding the limitations of correlational research, researchers can use this method effectively to explore connections between variables in various disciplines.

Experimental Research

Experimental research is a fundamental approach within quantitative research that aims to establish cause-and-effect relationships between variables. It involves the manipulation of an independent variable and measuring its effects on a dependent variable while controlling for potential confounding variables. Experimental research is highly regarded for its ability to provide rigorous evidence and draw conclusions about causal relationships. The hallmark of experimental research is the presence of at least two groups: the experimental and control groups. The experimental group receives the manipulated variable, the independent variable, while the control group does not. By comparing the outcomes or responses of the two groups, researchers can attribute any differences observed to the effects of the independent variable. Several key components are employed to ensure the reliability and validity of experimental research. Random assignment is a crucial step that involves assigning participants to either the experimental or control group in a random and unbiased manner. This minimizes the potential for pre-existing differences between groups and strengthens the study’s internal validity. Another essential feature of experimental research is the ability to control extraneous variables. By carefully designing the study environment and procedures, researchers can minimize the influence of factors other than the independent variable on the dependent variable. This control enhances the ability to isolate the manipulated variable’s effects and increases the study’s internal validity. Quantitative data is typically collected in experimental research through objective and standardized measurements. Researchers use instruments such as surveys, tests, observations, or physiological measurements to gather numerical data that can be analyzed statistically. This allows for applying various statistical techniques, such as t-tests or analysis of variance (ANOVA), to determine the significance of the observed effects and draw conclusions about the relationship between variables. Experimental research is widely used across psychology, medicine, education, and the natural sciences. It enables researchers to test hypotheses, evaluate interventions or treatments, and provide evidence-based recommendations. Experimental research offers valuable insights into the effectiveness or impact of specific variables, interventions, or strategies by establishing cause-and-effect relationships. Despite its strengths, experimental research also has limitations. The artificial nature of laboratory settings and the need for control may reduce the generalizability of findings to real-world contexts. Ethical considerations also play a crucial role in experimental research, as researchers must ensure participants’ well-being and informed consent. Experimental research is a powerful tool in the quantitative research arsenal. It enables researchers to establish cause-and-effect relationships, control extraneous variables, and gather objective numerical data. Experimental research contributes to evidence-based decision-making and advances knowledge in various fields by employing rigorous methods.

Analyzing results

Once you have your results, the next step — and one of the most important overall — is to categorize and analyze them.

There are many ways to do this. One powerful method is cross-tabulation, where you separate your results into categories based on demographic subgroups. For example, of the people who answered ‘yes’ to a question, how many of them were business leaders and how many were entry-level employees?

You’ll also need to take time to clean the data (for example removing people who sped through the survey, selecting the same answer) to make sure you can confidently draw conclusions. This can all be taken care of by the right team of experts.

The importance of quantitative research

Quantitative research is a powerful tool for anyone looking to learn more about their market and customers. It allows you to gain reliable, objective insights from data and clearly understand trends and patterns.

Where quantitative research falls short is in explaining the ‘why’. This is where you need to turn to other methods, like qualitative research, where you’ll actually talk to your audience and delve into the more subjective factors driving their decision-making.

At Kadence, it’s our job to help you with every aspect of your research strategy. We’ve done this with countless businesses, and we’d love to do it with you. To find out more, get in touch with us .

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Kadence helped us not only conduct a thorough and insightful piece of research, its interpretation of the data provided many useful and unexpected good-news stories that we were able to use in our communications and interactions with government bodies. General Manager PR -Internal Communications & Government Affairs Mitsubishi

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Business Ethics and Quantification: Towards an Ethics of Numbers

Grenoble Ecole de Management and IREGE, 12 Rue Pierre Semard, 38000 Grenoble, France

Social practices of quantification, or the production and communication of numbers, have been recognized as important foundations of organizational knowledge, as well as sources of power. With the advent of increasingly sophisticated digital tools to capture and extract numerical data from social life, however, there is a pressing need to understand the ethical stakes of quantification. The current study examines quantification from an ethical lens, to frame and promote a research agenda around the ethics of quantification. After a brief overview of quantification research and its uses in state and market organization, I discuss quantification in terms of three core subprocesses—capture, specification, and appropriation, illustrating and identifying ethical concerns around each process. Linking these processes to the performative effects of measures, I present a working model of quantification from which the discussion builds ideas for developing a research agenda around quantification.

Recent interest has surged around the social and organizational implications of quantification, defined as “the production and communication of numbers” (Espeland and Stevens 2008 , p. 401). Much of this interest is around the ethical stakes of numbers, questioning the roles of quantification in worker control (Wilson et al. 2020 ; Mazmanian and Beckman 2018 ), ideological obfuscation (Chelli and Gendron 2013 ), or the “datafication” of life (e.g., Sadowski 2019 ). These critiques mobilize diverse ethical conceptions to examine quantification, from consequentialist concerns around the consequences of numbers in social life (e.g., Baud et al. 2019 ) to deontological issues around the objectification of subjectivity (e.g., Urueña 2015 ). Some recent work extends such concerns to the ethicality of quantification in research (Zyphur and Perides 2017 ), arguing that quantification may divert recognition of the relational aspects of organizing.

In the meantime, issues of quantification have become increasingly pressing across diverse areas of social life. Particularly in the wake of the “big data” phenomenon (e.g., Mayer-Schoenberger and Cukier 2013 ) and the increasing use of digital platforms to collect quantitative data en masse (Sadowski 2019 ), understanding the ethics of quantification is a pressing social concern (Pink and Lanzeni 2018 ). Increasingly, quantitative data are not only used to inform managerial decisions but may constitute a form of management itself (Bruno et al. 2014a , b ; van Dijk 2014 ), as critiques of “algorithmic management” explore the control possibilities of highly datafied workplaces (e.g., Beverungen et al. 2019 , 2015 ). More recently, the implications of quantification and measurement has taken new life in the COVID-19 pandemic, where how, where and upon whom quantification takes place can be a matter of life and death (Taylor 2020 ). While not limited to these new contexts, seen in their light, quantification takes on a qualitatively distinct meaning, and old questions around numbers’ representative and epistemological status are remade as questions about how social life is transformed in a digital age (Bruno et al. 2016 ; van Dijk 2014 ; Desrosières 1993 ).

Despite the growing interest and pressing need to understand the ethics of quantification, little work in business ethics currently exists although some have suggested preliminary steps toward establishing a research agenda around quantification (Pink and Lanzeni 2018 ; Espeland and Stevens 2007 ), particularly elaborating Foucauldian perspectives on measurement and governmentality (Wilson et al. 2020 ; Chelli and Genrop 2013 ). Drawing on but broadening from these perspectives, the current paper takes up this agenda in the context of business ethics to build theory in this area. Bringing together diverse quantification perspectives around an ethical focus, I organize these into a working model that can ground future empirical research. My hope is to move beyond critiques of quantification as such (e.g., Power 1997 ) to consider the complexities of quantification in its different phases, and thus to promote theory and practice around more ethical ways of dealing with the powerful technologies of quantification.

Because this study involves a broad survey of the possibilities of ethical theorizing around quantification, I do not adopt a single ethical standpoint to consider quantification in all its aspects. Rather, the goal of the paper is to show how distinct issues arise and different moments of quantification, from the choice to quantify, to the construction and deployment of metrics, to the use of numerical data. These different aspects bring up a wide array of ethical issues that can be understood according to diverse normative models. Thus, keeping a pluralistic orientation at this early point in the discussion aims to allow a space for emerging research across the gamut of ethical theorizing.

The rest of this paper will continue as follows: First, I provide an overview of quantification as it has been addressed in the social sciences. Next, I propose a three-fold schematization of ethical issues around the capture, specification, and appropriation of quantitative data, based on the overarching questions of what should be quantified, how quantification creates social objects, and how these objects are socially distributed . Elaborating on the specific ethical implications of each of these foundational questions, I build an agenda for research around the ethical study of processes of quantification. Finally, the discussion weighs the important social functions of quantification against its dangers. I do not argue for the rejection of quantification as such but rather for its modest use within a pluralistic epistemic toolbox that is tailored to the requirements of specific action situations. Indeed, in the discussion section, I describe how quantification can be essential to ethics when conducted reflexively as part of that toolbox.

Quantification as a Multi-Faceted Ethical Concern

Concerns around the sociology and ethics of quantification have appeared periodically across the social and human sciences, although these dispersed discussions have rarely been coordinated in a systematic way (Berman and Hirschman 2018 ; Espeland and Stevens 2008 ). Among these diverse areas are the history and philosophy of science (Desrosières 1993 ; Hacking 1990 ), sociology (Mau 2019 ; Espeland and Stevens 2008 ), accounting (Power 1997 ), and more recently digital and data studies communities (Pink and Lanzeni 2018 ; Dourish and Cruz 2018 ). From these diverse areas, some dialogue with the areas of business ethics and organization studies has been present (e.g., Baud et al. 2019 ; Zyphur and Perides 2019 ; Beverungen et al. 2015 ), although the diverse provenance of these ideas from different core literatures has rendered a coherent dialogue difficult. Running across the organizational adoptions, however, has been a concern with the ethics of quantitative representations (Zyphur and Perides 2017 ), with the datafication of workplace interactions (Stein et al. 2019 ; Mazmanian and Beckman 2018 ), with the social uses of numbers by organizations (Wilson et al. 2020 ; Boje, Gardner and Smith 2006 ), and with the exploitation possibilities of data-driven technologies (Beverungen et al. 2015 ).

Despite the broad sweep of influences feeding into quantification discussions, the ethical stakes discussed across these areas show some convergence, which could be characterized on two broad set of concerns. First, they involve epistemic/scientific concerns around numbers and their relation to social reality, representation, and the consequences of articulating complex qualitative experience as quantitative data. Such concerns involve the tension between the representative functions of numbers—i.e., their ability to model social phenomena—and their “performative” function—i.e., their ability to shape or constitute social phenomena (Mennicken and Espeland 2019 ; Desrosières 1993 ). Second, and relatedly, critical discussions have related quantification to social control, both by state and corporate actors who use numbers as technologies of governing (e.g., Thévenot 2019 ; Miller and O’Leary 1987 ), or by market actors who can capitalize on numbers by extracting economic value from quantitative data (e.g., Beverungen et al. 2015 ; Dean 2010 ).

Ethical issues of quantification related to representing and constructing social life and issues related to social control are deeply related, but can be discussed as analytically distinct to give a first pass at theorizing this broad array of literature. This first analytical separation will clarify some of the distinct ethical issues at stake, which will be teased apart then theorized in subsequent sections.

Numbers Represent and Construct Social Life

Quantification involves the articulation of aspects of people’s individual and collective lives as numerical quantities, an operation carrying complex problems and ambiguities (Mau 2019 ; Espeland and Stevens 2008 ). Numbers are often used to represent dimensions of objects in the world, but they can also be used to group together phenomena under a common metric to construct new social objects (Desrosières 1993 ). For example, Hacking ( 1990 ) notes that concepts such as unemployment or disease rates become comprehensible through the construction of metrics, which involve “strenuous efforts to make and enforce definitions” (Porter 1995a , b ). Furthermore, when quantification deals with aspects of intimate personal or social life, such as emotions, well-being, or social relationships, questions arise as to whether such phenomena can or should be quantified at all (Humphreys 2018 ). While related, these different aspects represent somewhat distinct discussions within academic literature.

Numbers as Representation and Constitution

Regarding the first broad set of concerns, scholars have recognized that numbers do not only represent social reality, but also influence that reality (Bruno et al. 2014a , b ; Espeland and Stevens 2008 ; Desrosières 1993 ; Power 1997 ). Yet these two functions are often at odds (cf., Esposito and Stark 2019 ).

The representative function of numbers is based around a measurement paradigm that claims authority for numbers on the basis of “validity” (Alexandrova and Haybron 2016 ). Quantification, in this view, is valid to the extent that values correspond to their objects and represent them in reliable ways (Alexandrova and Haybron 2016 ). By contrast, numbers can “make” social reality by constructing descriptive or statistical categories by which they postulate “things that hold” (Desrosières 1993 ), in other words, statistical categories that form stable objects around which people act. The constitutive aspect of quantification, rather than depending on a supposed underlying “reality,” establishes categories which are pragmatically useful and support social action.

Numbers and “Lived” Experience

Related to the point about the representation and constitution of social realities, quantification has often raised concerns over preserving a value-infused notion of what could be called “lived” experience (e.g., Humphreys 2018 ). The use of quotes here signals a recognition of the elusive nature of this concept (Toraldo, Islam and Mangia 2018 ), which is precisely the point of the problem; many have viewed quantification, particularly within a “digitally saturated environment” (Markham 2019 , p. 2) as inappropriately fixing and objectifying experience in ways that denature human forms of living (e.g., Humphreys 2018 ).

To illustrate, Hornstein ( 1988 ) notes that quantification as a model for psychological knowledge has been controversial throughout the history of psychology, given its placement at the interface of subjective experience and numerical accounting. The discomfort with quantifying subjective experience has become more acute as the digitalization of social interactions translates lived experience into publicly accessible, statistically analyzable forms (Turkle 2011 ). People’s everyday experiences are transformed by the datafication of memories and ongoing activities, rendering the private public (Espeland and Stevens 2019 ; Humphreys 2018 ). Some have argued that such technologies, by codifying and publicly displaying the ongoing flow of life, exteriorize inner experience (Sibilia 2008 ) and convert it into what Thévenot ( 2019 ) has called “intimate spectacles.” In this context, scholarly interest has intensified around the limits of numbers in the “quantification of our lived experience” (e.g., Johns and Alexandrova 2018 ).

Numbers and Control: Between States and Markets

If statistics exert power through “objectivation” and the creation of knowledge (Bourdieu 1985 ), then the epistemic ambiguities of representing and constructing social experience also contain a power dimension. Indeed, the history of “scientific management” (Taylor 1911 ) has been described as a linking of so-called scientific objectivity with “administrative and political values” (Power 1994 , p. 355). Consequently, concerns over quantification are often combined with political critiques of governing through numbers (e.g., Thévenot 2019 ).

At the same time, beyond its applications in scientific management and workplace control, quantification for social control has a long history of use by the state (Desrosières 1993 ) and the market (Dean 2006 ), and I examine each of these briefly.

Governing by Numbers

Quantification and the State . Historians of statistics have noted the central role of the emerging modern state in the construction of quantitative databases and the statistical tools needed to analyze them (e.g., Hacking 1990 ). Early state formation relied on constructing equivalent measures across diverse regions, as well as inventing inferential techniques to estimate population parameters that were not readily observable (Desrosières 1993 ). The ability to infer population values from samples required conceptualizing diverse communities as “in the same urn” of probabilities, leading to conceptions of the “average man” as a citizen within the nation, and distinct form other nationalities (Desrosières 1993 ). The resulting forms of “seeing like a state” (Scott 1998 ) constituted a core process of nation building, as it allowed the mental construction of a unified territorial space and the “people” as an imagined community (Anderson 1983 ).

Debates over the status of numerical objects, although rooted in medieval arguments over nominalism and realism (Desrosières 1993 ), became central to statistical thinking in the early nineteenth century, with the consolidation of a social scientific enterprise of category building, linked to an emerging republican nation-state. For example, Desrosières ( 1993 ) notes how, in France, post-revolutionary administrations formulated measures of income and socioeconomic status to replace earlier divisions of society into incommensurable “estates” (nobility, clergy, merchants). In this way, philosophical debates about the nature of numbers became grafted onto the emerging field of social statistics, pulled in-between the epistemological task of representing reality and the political task of administering and governing a territory (Desrosières 1993 ).

Beyond state formation, gathering and tracking statistics has been instrumental to state functioning, from account-keeping to public health to economic growth (Bruno et al. 2016 ). Policing, for instance, has been deeply transformed by statistical methods, while the question of how numbers should be used and by whom has been an area of intense contestation (Didier 2018 ). Quantified surveillance has made it possible to centralize power and govern at a distance (Espeland and Stevens 2019 ). From early ideals of a mathematically perfect rational state to more recent cost–benefit forms of governance (Supiot 2015 ), quantification has been key to the formation, development, and functioning of the modern state.

Markets for Everything: Quantification and the Market

While historical literature has tended to link increased quantification to state power, research on present day quantification tends to focus on its role in capitalist market organizations, or on the role of capitalism within state processes (Mau 2019 ; Sadowski 2019 ). Although cognizant of historical milestones such as the emergence of book-keeping or the joint-stock company (e.g., Porter 1995a , b ), quantification literature has also paid great attention to the question of commodification , that is, the extent to which non-economic aspects of social life can or should be brought into modes of economic calculation (e.g., Charitsis 2016 ; Zelizer 2005 ). Drawing from early critical discussions of the “quantification of life,” quantification is seen as a commodification that leads to a “dull, uniformization of life” based on utility calculation (Löwy 1987 , p. 892).

More recently, however, such views have been complicated by the recognition that commodification can, in some circumstances, impart social recognition or value, conferring status on persons or relationships through valuation (e.g., Zelizer 1994 ). In organizational contexts, some have argued that the ability to quantify value is fundamental for socially responsible goals such as social and environmental goals (Kroeger and Weber 2020 ). On the other hand, quantifying value, by establishing commensurability though reducing diversity to a common metric (Espeland and Stevens 2019), undermines the singularity and diversity of social life (Zyphur and Perides 2019 ). Particularly when applied to areas of human life such as well-being (Singh and Alexandrova 2020 ; Karjalainen et al. 2019 ) or social relationships (Gill and Pratt 2008 ), quantification has been seen as compromising the integrity of that which it measures by linking it to commodification.

Discussion of quantification in the context of market commodification has taken new life recently, however, with a surge of interest around “big data” and the mass quantification of unprecedented proportions of social existence (e.g., Humphreys 2018 ; Beverungen et al 2019 ; Dean 2010 ). Because of the ability of digital systems to extract and capitalize on small bits of information from seemingly innocuous online interaction, the conversion of daily life into “free labor” (Beverungen et al. 2015 ) has given rise to a wave of critical scholarship. Such scholarship has been concerned both with the intrinsic effects of so-called communicative capitalism (Dean 2006 ) on transforming social relations, and on the use of the resulting data by companies for surveillance, targeted advertising and encroaching control over consumer choices (Sadowski 2019 ).

Thus, quantification has been central to both processes of “seeing like a state” (Scott 1998 ) and to the marketization of social life (Gill and Pratt 2008 ). Recent scholarship around neoliberalism has noted that, in fact, quantification may lie at the nexus of state and market control (van Dijk 2014 ). Neoliberal governance by objectives, specifically, is a case in which quantitative indicators play a central role (Bruno et al. 2014a , b ; Thévenot 2011 ). Particularly around basic social institutions like health care (Ruckstein and Schüll 2017 ), transport and traffic control (Shapiro 2018 ) or trade (Davis et al. 2012 ), state and market actors may converge around quantitative techniques that mix political and economic objectives (e.g., Mennicken and Espeland 2019 ). Some warn that such techniques give rise to technologies of surveillance, valuation and ranking (van Dijk 2014 ) that combine the most draconian parts of states and markets in a hybrid of quantified governance.

In sum, this review, although brief, provides a background against which the ethical stakes of quantification should be understood as essential in the context of contemporary social organization. First, quantification is a multidimensional phenomenon, involving not one but several interrelated processes than can be teased apart for analysis. Second, the discussion around if and how numbers can be used to represent social reality is analytically distinct from, while providing a basis for, the issues of governance and control over numbers. That distinction should not be taken to be absolute, and the interlinking of the epistemic and political aspects is deep (cf., Bruno et al. 2016 ); yet each brings unique conceptual issues that will allow a further theorization.

Ethics Across the Quantification Process

From the above, it should be evident that quantification involves different dimensions with related ethical questions, from the question of how and whether to assign numerical values to experience, to how such values are considered with regards to social reality, to how the resulting numbers are used for governance or profit. This differentiated aspect of quantification has been noted in the social science literature; for instance, Eyraud ( 2012 ) differentiates between the different aspects of defining what “counts,” quantitative embedding philosophies in metrics, and using numerical values in action. Similarly, Espeland and Stevens ( 2008 ) differentiate between marking objects, establishing commensuration and shaping objects in the environment. Not focused on ethics specifically, these discussions nevertheless acknowledge the differentiated work of quantification (Espeland and Stevens 2008 ). Below, I abstract from these specific discussions to present a three-part conceptualization of quantification—involving capture, specification, and appropriation—each with unique implications for ethics.

Capture: Definition and Illustrations

By “capture,” I refer to how lived experiences and everyday interactions in social life are cast into quantified or quantifiable forms (Dean 2010 ). Considered prior to the economic exploitation of quantified life, capture is about the process of objectifying social phenomenon so as to express it as a numerical quantity. As Zuboff ( 2015 , p. 76), describes it, capture transforms social life in that “nearly every aspect of the world is rendered in a new symbolic dimension as events, objects, processes, and people become visible, knowable, and shareable.” Although some scholars have linked capture more narrowly to the extraction of free labor in a digital setting for economic profit (Beverungen et al. 2015 ), I discuss this aspect under “appropriation” below.

Practices of capture relate to the objects of quantification in various ways, with different implications for the phenomena which are quantified (Pink and Lanzeni 2018 ). In some cases, lived phenomena may seem to naturally afford quantification, for example, quantities of goods that are easily measured or discrete objects whose countability does not require extraction from entangled webs of other objects (cf., Shapiro 2018 ). Other aspects of social life may be made amenable to quantification only after high levels of processing, manipulation, or abstraction, such as the case with psychological variables like well-being (Alexandrova 2012 ) or sociological concepts such as class (Desrosières 1993 ).

To illustrate, Martin’s ( 2007 ) ethnography of bipolar patients describes how these patients are encouraged to engage in the quantification of affect through mood charts which assigned daily quantities to their affect, allowing tracking and “performance measurement.” Through such capture, these sensibilities could be mapped onto medical treatments to increase behavioral control over emotions. Arguing that these quantification practices constitute technologies of control, Martin notes that codifying affect in numerical forms creates what Williams ( 1977 ) called “structures of feeling,” that is, vague sensibilities or affects that underlie popular culture but are difficult to pin down. Through quantifying structures of feeling, the patients were taught to objectify themselves to allow self-improvement.

While quantification in Martin ( 2007 ) involved psychological measurement, Scott ( 1998 ) examines the codification and enumeration of social productive processes by the state, and the resistance to quantification that it triggers. In this historical example, Scott describes how early states preferred grain crops such as rice, which could be easily quantified and measured and thus provided a basis of taxation. Such crops were visible because they grow above ground, are easy to transport, and have an even-timing of harvest, and thus were preferred stores of value and taxation for emerging state systems. Root crops, however, such as manioc and potatoes, were difficult to homogenize, less visible, and generally less “countable” than rice. Rebel groups adopted such crops, which resisted in their very physical composition the commensurability conferred by quantification. In this example, we can see the relation between the material properties of an environment, its quantification possibilities, and the political-economic ramifications for governance and resistance possibilities.

In both of these examples, quantification involves capturing a real but diffuse aspect of social life. In the case of Scott ( 1998 ), the material properties of crops and their amenity to quantification led to their selective uses for political ends, while in the case of Martin ( 2007 ), quantitative capture of diffuse feelings constituted therapeutic practices related to governance of the self (see also Humphreys 2018 ; Sibilia 2008 ).

Ethical Stakes of Quantitative Capture

Quantification as capture involves ethical questions pertaining to the transformation of lived relations by framing those relations in quantitative terms (Mazmanian and Beckman 2018 ). Three interrelated concerns are of particular interest, focusing on the effects of quantification on experience and its potentially deleterious effects on the phenomenological embeddedness of subjects in their worlds.

First, quantification may have the paradoxical effect of dismissing the primacy of lived experience in the very moment such experience is valued numerically (cf, Elden 2006 ). From a phenomenological perspective, the circumscribing of lived existence into discrete and determinable quantities already mis-specifies the nature of human being (Elden 2006 ). While quantified empirical data derive their validity from their basis in observation, the expectation that only through quantification can experience become “scientific” risks dismissing experience as a source of knowledge in itself (Jay 2005 ). Because lived experience involves embeddedness in a “lifeworld” of interconnected meanings, quantification always requires a reduction of experience to one of its facets (Mau 2019 ; Elden 2006 ). Establishing quantification as an epistemic value risks valuing this reduced form over the holistic matrix from which it was extracted and substituting the part for the whole.

This dismissal of “raw” experience can have a second and related consequence of obscuring the multiple possible interpretations of experience. Because experience is open-ended and is not exhausted in its forms of codification, encoded knowledge can be revisited in the light of lived experience and reframed on the basis of evolving ideas. To this extent, quantifications of experience should be considered as provisional and not definitive (cf., Boltanski 2016 ). Even considered as such, however, the quantification process necessarily decontextualizes one facet of a lived whole, as noted above, drawing attention to the object of codification and away from the complex of background experiences.

One consequence of this displacement between experience and context is to obscure the active and practical nature of experience as a form of ongoing experience (Espeland and Stevens 1998 ). The open-endedness of lived experience means that a constant process of adjustment and calibration characterizes action, as actors build knowledge through their management of the flow of experience. The resulting “objects” of knowledge may be of relative stability, able to be measured or quantified. However, taking such measures as equivalent of their grounding experiences may obscure the active process of knowing, individual and collective, by which those objects are built and maintained.

Finally, obscuring the active nature of experience can lead to an alienation of experience, which comes to be seen as separate from the subjects of experience (cf., Jay 2005 ). By institutionalizing a process by which the products of knowing are recognized as epistemically valuable, while the labor of knowing is neglected, quantification as capture rests on the paradoxical situation of a knowledge that is both empirical (hence based on experience) and objective (hence independent from experience). This alienation of experience from itself becomes relevant in the economic process of data extraction and free labor, as we will see below, but for now, as capture, quantification involves an alienation of experience from its subjects, facing them with the objects of their own cognition in an alien form.

Specification: Definition and illustrations

Closely related to the question of what may be quantified and whether it should be, I term “specification” the process by which choices are made as to how something should be quantified. Namely, construct definition and validation are modes of framing reality (Alexandrova and Haybron 2016 ), during which choices are made around how phenomena should be grouped, compared, and defined. As Espeland and Stevens ( 1998 , p. 314) notes, quantification is based on commensuration, that is, “transforming different qualities into a common metric,” and this definitional process has effects on the world.

In a previously mentioned example, Desrosières ( 1993 ) describes how French administrators replaced traditional social “estate” distinctions with income-based quantitative measurements. The result was both to put the citizens of the new republic on a commensurate measure (income), while at the same time constructing the concept of economic inequality. The resulting population was conceptualized a uniform body of citizens with unequally distributed revenue, rather than an incomparable set of differentiated “estates,” each with its own group identity.

A more recent example, the case of higher education measurement suggests how struggles over specification can reflect underlying tensions between logics of governance (Cussó 2016 ). Cussó explains how international organizations, from the 1980s, increasingly began to measure educational outcomes in terms of attainment, as well as return on investment. UNESCO, however, retained earlier measurement of educational outcomes in terms of the right to education and public expenditure, rather than cost–benefit type measures. This difference in the construction of measures reflected resistance to a move to more market-based education management, and an attempt to maintain the link between education and basic rights of citizenship (Cussó 2016 ).

In both of these examples, the question is not whether an aspect of social life (demographic information, educational outcomes) can be measured (i.e., capture), but in what form they should be measured (i.e., specification). In the example given by Desrosières ( 1993 ), the specification of persons on a single scale along the dimension of revenue established a new view of the political subject as equivalent to others in type, while framing them as unequal economically. Such techniques of subject-making are related Foucault’s (e.g., 1988 ) descriptions of the birth of the state through the construction of new kinds of subjects. In Cussó’s ( 2016 ) example, the aspect of education taken to be an object of measurement encodes an underlying assumption about the goal of education, as a basic right or as an economic investment. In both cases, measurement reconfigures social objects in ways that make certain policies possible while blocking alternative ways of organizing.

Ethical Stakes of Specification

Quantitative specification involves ethical questions involving the stakes of commensuration, which produces sameness out of difference (Espeland and Stevens 1998 ). Espeland and Stevens ( 1998 ) give the example of salary categories, where equal or fair outcomes depend on how categories are built. They note the inclusion, within university rankings, of faculty salaries, but not the salaries of administrative staff. The resulting human resource policies at universities tended to generously reward full-time faculty but not staff, who remained woefully underpaid.

In this example, what is tacitly assumed is that faculty are more definitive of the university community than staff—hence, their exclusive inclusion in the rankings metric. This example illustrates how processes of commensuration are built on judgments about inclusion and exclusion that then become obscured as the metric is consolidated. Such metrics become “black boxes” (Mennicken and Espeland 2019 ) whose inner diversity is obscured by the subsumption under a numeric value. While all categorization has this quality of connecting disparate elements, quantification takes this to the extreme, because the numerical value literally erases the qualitative traces in the category. While a qualitative category can be “unpacked” by examining the elements that compose it, once quantitative databases are constructed, these constituent elements are easily lost. This is ethically fraught because the traces of exclusions and possibilities for change around a given category are rendered opaque in this process.

Moreover, the commensuration processes involved in specifying metrics are deeply political and depend on the interests of the parties involved. Boje ( 2006 ), for instance, examines how the rhetorical aspects of financial performance metrics supported Enron’s ability to deceive the public about its financial robustness. By including mark-to-market figures in its annual revenue figure, Enron was able to claim future profits in the present, framing commensurability between the present and future to give a false impression about its financial stability. The fact that such metrics are constructed in closed settings and without public debate gives some actors powerful tools of representation and social reality construction without social accountability.

By contrast, when statistics become public, they can have galvanizing effects, as they become mobilizing objects for social groups and justice demands. DeSantos ( 2009 ) uses the example of the publication of country-risk statistics in Argentina to show how “public numbers” become everyday talking points that fix attention, hold public servants accountable, and concentrate public opinion. Similarly, recent work on what has been called “statactivism” (e.g., Bruno et al. 2014a , b ) has shown how reframing or constructing alternative statistics by community groups or activist academics has enabled the dislodging of taken-for-granted social facts. Examples of such dislodging include alternative wealth indicators to substitute GDP (cf., Ottaviani 2015 ) and community-based well-being measures to substitute standard psychometric “happiness” measures (Alexandrova 2017 ).

Implicit in the above examples is that the ethics of specification become evident when paying attention to who or what is excluded from the commensuration process. Specification is thus an inherently political process, and who is included in this process, and which access to what cognitive and conceptual resources, are ethically relevant questions. The opacity and apparent stability of numeric values bring particular urgency to these questions, because whomever succeeds in making their respective numbers “count” is legitimized in ways that may be difficult to undo or deconstruct, as compared to other techniques of constructing social reality.

Appropriation: Capitalizing on Numbers

While capture and specification are inherent processes that could be considered internal to quantification as such, what happens to numbers once they are assigned to phenomena is also a source of ethical concern (Sadowski 2019 ). Especially in the digital age, the question of the ownership, valuation, and use of numerical data is increasingly scrutinized by social scientists (e.g., Ruckstein and Schüll 2017; Neff 2013 ). I use the term “appropriation” to describe the processes by which numbers become the property or capital of specific actors. While all aspects of quantification are “political” in the sense that they are related to social power relations, capture and specification exert power through their epistemic qualities, i.e., that of knowing and defining the world, while appropriation draws economic and governance power through its ability to exert exclusive rights to the mobilization and deployment of numbers.

In this context, quantification has been increasingly described as a form of “extraction” (e.g., Sadowski 2019 ; Charitsis 2016 ), whereby “gathering and extracting maximal value” (Boyd and Crawford 2012 , p. 14) from numerical data is the goal of what has been called “life mining” (van Dijk 2014 , p.200). This language of extraction is echoed in what Dean ( 2006 , 2010 , 2016 ) calls “communicative capitalism,” in which the extraction of numerical data through social media interactions means that “our basic communicative activities are enclosed in circuits as raw materials for capital accumulation.” (Dean 2016 , p. 16).

While much has been said about the automated processes of numerical data extraction in algorithmic platforms (Sadowski 2019 ; Beverungen et al. 2015 ), it is also important to remember that more mundane, physical forms of labor are involved in quantification, and that appropriation can also involve the expropriation of the labor of making numbers. As Beverungen et al ( 2015 ) point out, the wage labor of maintaining equipment, coding, and selling advertisements is part of the datafication process, not to mention outsourced labor from the global periphery in monitoring and managing data. Similarly, Loveman ( 2005 ) notes the difficult work of collecting and compiling state statistics, which form a “primitive accumulation” that establishes state power.

One of the most insidious aspects of the appropriation of quantitative data is that the opacity of a numerical value tends to obscure and render invisible such labor (Beverungen et al. 2015 ). The ownership and control of numerical data as property is often unrelated to the work exerted in the production of numbers, and in the case of digital media, production may not even be perceived as economic value production or labor by users (e.g., Dean 2010 ), Users of digital technologies such as social media may experience their labor as entertainment or social interaction, and may pay for the opportunity to produce value for platforms and their owners. Whether the positive user experiences and social value from such platforms adequately compensates the abdication of control over the capitalized data thus produced is a question requiring ethical analysis.

To illustrate the ethical stakes of quantitative appropriation, Ruckenstein and Schüll ( 2017 ) focus on the health sector to examine how the datafication of health changes relationships between patients, healthcare providers and for-profit companies. Noting the tension between the openness of data and its private ownership, they argue that datafication leverages morally tinged “concepts such as ‘sharing’ and ‘the public good’ to promote voluntary giving up of data, which are then appropriated by technology companies seeking free access to their users’ data” (Ruckenstein and Schüll 2017 , p. 272). In response, they note how, faced with the private appropriation of health care data, “data activists” try to use medical statistics to promote justice in health outcomes, point out quality of life disparities for public attention, and promote user-centered solutions that shift power relations between patients and medical providers.

On a more personal level, Humphreys ( 2018 ) examines the role of property relations in the context of intimate relational aspects of the self that have been converted into online artifacts. For example, family artifacts such as wedding or child albums, once digitalized and posted on online platforms, become objects with ambiguous and complex property claims; those who made them, and to whom they hold personal value, may (consciously or not) agree to hand over economic rights to such datafied artifacts, which will be used for marketing, surveillance, or other purposes far different from their original social purposes.

In both of these examples, some form of “life mining” converts an intimate or personal aspect of life (health, intimate relationships, identity representations) into a commodifiable unit whose quantification renders it impersonal and economically exchangeable. In this sense, appropriation has some relation to capture; in both cases, quantification renders commensurable very different things (e.g., baby pictures, consumer advertisements, stock prices) and by doing so risks denaturing and rendering impersonal aspects of social life (e.g., Thévenot 2019 ; Sibilia 2008 ). However, while capture refers specially to the ontological/epistemological aspect of framing life as a knowable and quantifiable object, appropriation involves the question of distribution of such objects and their economic alienation. Capture highlights the denaturing of social experience, while appropriation highlights the fair allocation of social value.

Ethical Stakes of Appropriation

Stated in ethical terms, while capture questions the ethicality of describing life through quantities, appropriation questions the ethicality of how such quantities are distributed within an economy. The ethical questions raised by appropriation center around the justice of personal ownership and use of numerical data, as well concerns around the control aspects of data use. The latter concerns often involve questions of surveillance (and the value of privacy), as well as the propagandistic use of personal data (linked to the value of decisional autonomy).

First, as noted above, the denaturing critique of capture runs in parallel to the critique of appropriation as capitalization; both are concerned that essential features of social life are destroyed through commensuration, but that appropriation adds a further layer of economic commodification. As implied by the name, appropriation allows numerical quantities to become objects that can be distributed unequally, controlled by some but not others, and deployed in ways that surpass their mere epistemic functions as representations of the world.

The question of who retains rights over the storage and use of numerical data has often been discussed in the context of surveillance (e.g., Zuboff 2019 ; van Dijk 2014 ). Whether surveillance involves state actors, bringing up questions of civil liberties (van Dijk 2014 ), or market actors, bringing up questions of privacy and corporate overreach (Fuchs 2012 ), it is the concentration of masses of data in the hands of large actors that enables numbers to become generators of unequal power. The appropriation of numbers creates diverse power-related asymmetries. Asymmetries of representation occur where some actors, such as states or large market actors, wield large amounts of data to build knowledge that is inaccessible to smaller actors (Ruckstein and Schüll 2017). Asymmetries of prediction occur where predictive capacity, such as those of large investment banks or insurance agencies, allow unequal access to market opportunities or risk avoidance (Boje et al. 2006 ). Asymmetries of legitimacy occur where, regardless of the “correctness” of data itself, the fact that some actors retain access to huge stores of information gives them a presumption of knowledge or credibility that allow them to act unobstructed or without debate (Thévenot 2019 ). In all of these cases, knowledge production is decoupled from social debate and an active public sphere and is privatized and leveraged for monetary gain.

The result of such processes is that social decision-making is distorted, and power concentrated, in non-transparent ways. Adding to this concentration of big data, the automatized and algorithmic features of data analysis raise ethical issues around the autonomy of decision-making. Earlier critiques of the massification of media and their propagandistic effects (e.g., Habermas 1989 ) are increasingly replaced by a concern with the manipulative aspects of targeted media based on personal data (e.g., Ingram and Bar-Tura 2014 ). Even targeted advertisements, however, leave a shell of choice at the moment of consumption, while more advanced algorithmic systems may deploy big data to engage in decisions which are largely opaque to those affected by them (Greenfield 2017 ). As Greenfield ( 2017 , p. 217) notes about automatic data-driven systems, they render alien many of the aspects of life where personal autonomy would be considered fundamental to a well-lived life: “ We’ll be offered jobs, or not; loans, or not; loves, or not; cures, or not. And the worst of it is that until the day we die, we’ll never know which action or inaction of our own led to any of these outcomes.”

In sum, appropriation raises ethical questions around property, justice, and personal autonomy, many of which are made particularly salient by the increasing monetization and use possibilities of numerical data.

The Performativity of Quantification

As suggested by the processes of quantitative capture, specification, and appropriation, quantification goes beyond representing aspects of the world and itself constitutes a force of change and action (Ustek-Spilda 2019 ; Mingers and Willmott 2013 ). One way of stating this point is to say that quantification is “performative” (Ottaviani 2015 ; Mingers and Willmott 2013 ); that is, it can produce the realities it purports to describe. 1 Combined with a Foucauldian concern for the performativity of techniques (cf., Raffnsøe et al. 2019 ), quantification scholarship has focused on how numbers constitute active forces that establishes practices and norms (Mennicken and Espeland 2019 ).

Specifically, quantification shapes social reality by introducing metrics that retroactively define a reality that is already presumed to exist (Appadurai 2016 ), which I term “retro-performativity.” Quantification also shapes social reality by establishing “targets” towards which actors aspire to establish new realities (Esposito and Stark 2019 ; Greenfield 2017 ), which I term “telic performativity.” In other words, quantification is performative both in its effects on framing the present, and in its setting guidelines and incentives for future action (Esposito and Stark 2019 ; Bruno et al. 2016 ; Desrosières 1993 ).

Because retro-performativity involves changing how current or past objects are understood by defining them numerically, it retroactively constitutes social knowledge. Telic performativity, by projecting numbers in the future, shapes ongoing and future action. Moreover, these two forms may be opposed, as in Goodhart’s Law, which states that “when a measure becomes a target, it ceases to be useful as a measure.” (Greenfield 2017 , p. 205). However, they may be mutually reinforcing, where an instituted target becomes progressively aligned with social reality through a kind of feedback loop (Esposito and Stark 2019 ).

Retro-Performativity: Defining Backwards

I borrow the term “retro-performativity” from Appadurai ( 2016 , p. 149), who defines it as an effect where signs “produce their own conditions of possibility by acting as if they already existed.” Applied to quantification, retro-performativity describes how the process of attributing a numerical value (capture) to something based on a particular scalar dimension (specification) produces the impression that that thing always existed, waiting to be measured. Particularly in complex or abstract objects (e.g., unemployment, emotional intelligence, creditworthiness), the fact of having a numerical quantity confers solidity onto an otherwise ephemeral object, which may not even have been considered an “object” at all previously to the measurement. In this sense, quantification “makes things that hold” (Desrosières 1993 ), by marking them numerically and thus establishing—backwards—that which they measure.

To give an example, Martin’s ( 2007 ) anthropological study of mental illness, cited earlier, noted how measuring moods with scales is constitutive of domains of mental illness, whether done by professionals for diagnostic purposes or by patients as a daily therapeutic practice. Measurement is used to establish categories that are often contested (in the case of professionals), and to establish self-understandings of who one “really” is (in the case of patients) through diligent and exact measurement. While the ostensive purpose of measurement practices is to track an underlying and pre-existing condition, the ongoing forms of personal and social definition that are achieved through these practices shape understandings of what was, and thereby performatively construct the past and present.

As suggested above, retro-performativity is most intuitively connected to the processes of capture and specification, because it is through these practices that numbers come to consider their experience as an object (capture) and give it a specific form (specification). What is done with those numbers, however—i.e., their distribution, allocation, or use as guides for action—are a separate performative domain.

Telic Performativity: Defining Forwards

What I term “telic” performativity involves positioning a metric as a telos or goal; action is motivated to “make the numbers.” As Campbell ( 1979 ) argues, quantitative social indicators, when used for policy making, exert pressure on the underlying social processes they were made to monitor, shaping those processes in the future. Jany-Catrice ( 2016 ) elaborates on this process, where quantification shapes realities in neoliberal governance by establishing targets which can then be attached to economic incentive systems. She argues, moreover, that such incentive systems encroach into ever expanding circles of activity, including environmental, health, and personal statistics that convert an initial focus on measurement into a lever for governance. Summarized by Bruno et al. ( 2016 , p.28), “indicators retroactively influence the behavior of agents, as actors undergoing quantification. This idea supplements the notion of performativity that Michel Callon deploys in order to account for the changes to reality brought about by scientific theory.”

To illustrate, Desrosières ( 2016 ) examines ongoing critiques of the slippage of uses of gross domestic product (GDP) as an economic measure. Originally created as a national accounting measure to be used internally (Vanoli 2005 ), this usage slipped over the next half-century to become a catch-all metric to describe the economic well-being of a nation (Desrosières 2016 ). Such uses underwrote attempts to target GDP growth as a goal of government and to direct public policy, in a way that would not have been conceivable in its original formulation.

In an example closer to the direct experience of business scholars, Mingers and Wilmott ( 2013 ) discuss the performative effects of journal rankings lists on business schools, where the adoption of common metrics establish incentive systems, which in turn shape the production of business knowledge. They argue that turning an ostensive measurement of quality into a target for academic attainment exerts a homogenizing effect on research, as well as replacing substantive academic contributions with the technical mastery to craft research to be compatible with specific journal norms. The article further reinforces the idea of a slippage between the valid measurement of a variable and the shaping of the social world so as to retrospectively validate the variable.

In sum, retro-performativity shapes social reality through providing measures that retrospectively reframe events and variables, while telic performativity projects quantitative targets that shape actions and incentives. In both aspects, numerical quantities are not merely more-or-less valid measurements, but actively shape social reality, and are thus to be evaluated not only on their methodological validity but also on the ethicality of their effects, in terms of consequences, principles or other ethical criteria.

Table ​ Table1 1 summarizes the subprocesses of quantification along with their ethical stakes and their relation to retro- and telic performativity. Below, synthesizing the different aspects of quantification, I elaborate on these ethical considerations and build an agenda around which the ethics of quantification can proceed.

Quantification as capture, specification, and appropriation

A Working Model of Ethical Considerations Around Quantification

Based on the ideas of capture, specification, and appropriation, and their effects on social and organizational reality through distinct forms of performativity, we are in a position to build an initial theoretical model of quantification that can stimulate research around the its social and ethical impacts. I visually illustrate this model in Fig.  1 below.

Conceptual model of quantitative capture, specification, and appropriation

In this conception, I begin with the idea of an imperfectly articulated social reality that susceptible to quantitative capture through technologies of measurement and quantification. I visualize this as a series of increasingly “concrete” circles that represent the progressive objectification of a phenomenon. The formulation and validation, and then the eventual acceptance and institutionalization of a given metric, would constitute progressive consolidation of the new social object. Capture and specification increasingly fix and define ongoing social experience to frame it in terms of “objects,” definitions which act retroactively to modulate how actors understand their own experience, and thus frame that experience according to measurement categories.

Beyond the measurement-feedback process by which social reality is quantitatively objectified, the definitions used in specification also become projected into potential future values of the object, or “targets.” It is at this point that, in Greenfield’s ( 2017 , p. 205) words, “a measure becomes a target.” The process of turning a measurement dimension into a future target characterizes telic performativity. Telic performativity works, first, by reducing a measured phenomenon to a number, it confers a sense of objectivity and stability to the phenomenon. Second, the resulting data may be used as a source of value, supporting efforts for planning, control, creating incentives to maintain the objectified measure regardless of its relation to any social reality. In this way, even if the specification of a measure was discovered to be misguided or inaccurate, when enough political or economic stakes have been piled onto the target measure, and enough institution building has used it as an anchor point, its epistemic value may cease to be of interest to those who have built an edifice around a measure.

From this initial model, we can see several places in which ethics research around quantification could direct its questions. Notably, the effects of capture, specification, and appropriation have distinct but interrelated qualities that may raise different ethical concerns. Put broadly, one set of concerns could be thought of as the “epistemic” concerns around how quantification shapes, enables, or forecloses on knowledge, as social reality is shaped and concretized in certain ways. These concerns center on the retro-performativity of knowledge tools on the realities they aim to know and are most visible in the processes of capture and specification. At the same time, the deployment and use of quantitative data have impacts on social reality as well, involving the telic aspect of performativity as actors lean on measures to achieve their goals.

To note, because social action shapes the ground of experience as such, the epistemic and practical aspects of quantification are deeply intertwined in any empirical situation (and thus retro- and telic performativity are also deeply linked in practice). However, as an analytic distinction, separating these elements helps ethics scholars separate out the different kinds of ethical issues that quantification raises. Data extraction, profiteering, and surveillance cause social ills that are distinct from those caused by misjudging reality by overly clinging onto a measurement scale. In fact, the two may contradict each other—for instance, surveillance may presume valid measurement, and imposing self-serving measures may reduce the economic and practical utility of the measures. Convincing people to adopt a measure for expedience reasons is different than arguing for the scientific validity of the measure. Yet, in practice, these different ethically relevant aspects of quantitative measurement are likely to co-exist in complex ways, requiring emerging ideas to theorize and study their interrelations.

The complex combination of processes described above may be illustrated by a case recently described by Aitken ( 2017 ). A recent attempt in New York City to expand ID cards to undocumented populations was rejected by financial institutions such as Bank of America, JP Morgan Chase and Citibank, who refused accept the new IDs to open new accounts, claiming that the initiative would increase the riskiness of client identification (cf., Corkery and Silver-Greenberg 2015 ).

This example could be read along different dimensions in the model in Fig.  1 , with different ethical implications. At the level of capture, a public logic of capture as inclusion was contested by a private logic of exclusion of high-risk individuals, leading to contested “regimes of visibility” (Aitkin 2017, p. 275). As retro-performativity, the inclusion (or not) of these individuals would establish a social criteria for who “counts,” retroactively recognizing or denying a person’s status and raising deontological ethical issues about the duty of recognition of humans in society.

On the other hand, the extension of credit scores to high-risk groups as a basis for predatory lending would have allowed capture but configured specification so as to re-define “the unbanked” as “the high-risk” (Aitkin 2017). As telic performativity, “risk” would establish a set of behavioral targets (to raise credit scores) that subjects could leverage to “improve” their standing, constructing themselves into different future selves. In this situation, the ethics of inclusion could come into tension with the ethics of manipulation, a tension invoking both utilitarian (the consequences of credit access) versus deontological (the principle of autonomy) issues. Finally, inclusion via credit scores would initiate a process of data appropriation, where complex educational, consumer, and other data composing the credit score would be commoditized and used by financial institutions, raising the ethical question of who should profit from the data traces left behind by everyday life.

While cursory, this brief example illustrates how a single case can raise multiple issues related to capture, specification, and appropriation, invoking retro-performativity to define reality and telic performativity to shape reality. Each of these issues raises ethical consequences but also foundational principles, such as who has the right to name the world and its inhabitants, and on what basis.

Discussion: Toward a Research Agenda around the Ethics of Quantification

The current study has examined the phenomenon of quantification from an ethical lens, to unpack the different ethically relevant issues emerging along the process of quantification. Doing so required first decomposing quantification, a complex concept, into the specific components of capture, specification, and appropriation, and discussing the ethical implications of each of these. This involved stressing the active aspect of quantification as a force in the world, above and beyond its epistemic functions of representing, which was discussed in terms of performativity, understood in two distinct ways. From this conceptual layout, the final step was to reconnect these parts into an overall model of quantification from which an agenda of future research around the ethics of quantification can be constructed below.

Taken as a whole, this study contributes to understanding the ethical ramification of quantification that connect to recent organizational concerns. While the perspectives showcased here exist in current literature, they have been dispersed across fields and topics, making it difficult to think about the ethics of quantification in an integrated way. Organizational research has shown increasing interest in the social implications of numbers. These have ranged from concerns around “audit society” and the encroachment of metrics across organizational life (Mingers and Wilmott 2013 ; Powers 1997 ) to the datafication of everyday worklife in organizations (Stein et al. 2019 ; Mazmanian and Beckman 2018 ), to considerations of the social foundations of quantitative data and relation to power (e.g., Perides and Zyphur 2019 ; Gephart 2006 ). Despite their breadth, these perspectives have in common a recognition that numeric values are born out of social process and have social impact.

The current study begins from this broad literature, distilling out of the various aspects of quantification the specific ethical stakes of each. Beginning by arguing for the “social life of numbers,” my goal was to position numbers as a form of social action closely linked to governance by state and market actors (cf. Bruno et al. 2016 ; Desrosières 1993 ) and thus to establish the need to examine numbers beyond the question of scientific representation. Then, I analyzed quantification not as a single process, but as three intertwined processes of capture, specification, and appropriation, linking these to the actions of reframing social categories (retro-performativity) and anchoring future actions through incentives and targets (telic performativity).

Laying out the aspects of quantification in this way clarifies its ethics because the stakes involved in each of these components is distinct. Questions of what should or not be counted (capture) are distinct from how they should be counted, or by whose criteria (specification). Further, the question of who owns, or uses, these numbers, and how they may be stored, sold, analyzed, or destroyed, raised separated ethical questions. My hope is that an ethics of quantification would draw upon the schematic framework to build an agenda around each of these sets of issues. Below, I give some initial ideas for starting such an agenda.

Future Research Directions

Regarding how quantification can be developed as a theme in business ethics, one can immediately noting the wide array of immediate research questions that appear from the above discussion. For example, the ethics of ownership and privacy of personal data, consent in collecting data, and permission for use are topics at the forefront of contemporary controversies (Sadowski 2019 ). Similarly, how organizations use—carefully or less so—psychometric scales, performance metrics, targets, and other numbers produce ethical questions both around the sometimes-questionable validity of such measures (Alexandrova and Haybron 2016 ) and around their possibly harmful social effects (Espeland and Sauder 2007 ). Moreover, issues around the critical sociology of numbers have recently entered organizational discussions around the ethics of quantitative methods, and how to understand quantitative methods as technologies of power and thus build reflexivity in management research (Zyphur and Perides 2017; Zyphur and Perides 2019). Such topics about the “effects” of numbers are important areas for future research. I would like to highlight three related issues, however, which dig deeper into the core conceptual themes, that is, what is the nature of numbers in their relation to social and organizational practices.

First, it is valuable to examine the complex relationships between beliefs in the objectivity of numbers and what critical scholars have referred to as the “objectivation” of power relations (cf., Mau 2019 ; Cussó 2016 ; Bourdieu 1985 ). According to Cussó ( 2016 ), objectification involves “hardening” social reality into taken-for-granted forms, which are then taken to be “objective.” Bourdieu ( 1985 ) uses this notion to describe how powerful actors confer a sense of reality and legitimacy on their vested interests and thereby reinforce social power relations and obscure injustices to groups without such objectifying power. Ethical examinations of quantification should focus on the use of quantification as an objectification strategy to understand whose interests are upheld, and whose interests are obscured, behind a given number or metric.

Based on this idea, a second set of research questions involves the ethics of struggle around who measures or how a social phenomenon is measured. Already, an emerging literature on “statactivism” is beginning to take seriously the idea of counter-statistics or inclusion of less-represented groups in the quantification process (e.g., Didier 2018 ; Bruno et al. 2014a , b ). Some work has begun to try to map out who are or are not represented by scale development (e.g., Ottaviani 2015 ) and how scales relate to domination (e.g., Wilson et al. 2020 ) as well as examining alternative quantifications of constructs such as well-being (Bache 2019 ; Alexandrova and Haybron 2016 ). Yet, a vast array of quantification processes in organizations, as well as scales and constructs in the social scientific literature, remain off the radar of such critical work.

In this respect, ethical quantification should be reflexive about what aspects of social life are quantified, why, and by and for whom (Bruno et al. 2014a , b ). Scale construction, for example, could draw more actively on the participation of those who are measured, not only as objects of study but as subject experts of their own qualities. Such reflexive quantification would also acknowledge the capitalization possibilities of numbers, ensuring fair distributional arrangements when data are monetized, but also asking difficult questions about the shifting lines between the epistemic and the economic use of numbers.

A third, somewhat more speculative, research question around quantification would ask whether all numerical representations have the effects of closing or occluding their sources, a theme which runs throughout the critical discussion above. Are the “injustices” done through capturing and framing reality through numbers inherent in quantification, or is it possible to represent social reality through numbers while maintaining the richness of social life? An early but promising concept to address such a question is Boltanski’s ( 2016 ) concept of “reflexive numbers.” With this concept, Boltanski describes the challenge of using statistics to describe social reality when the production of statistics is itself part of that social reality. To establish a critical statistics, he argues, one must be able to both use statistical operations to support social critique (e.g., by statistically revealing social inequality, gender discrimination, or other quantifiable justice-related themes), and at the same time, subject statistical techniques themselves to critique. Doing this involves genealogical examinations of statistics (cf., Zyphur and Perides 2019), but also sociological analyses of where and how statistics are made as a form of organization (cf., Mazmanian and Beckman 2018 ).

Each of the three above suggestions is deeply critical of quantification, but none dismisses it or longs nostalgically for a world that cannot be measured (cf., Mingers and Willmott 2013 ). Rather, treating quantification as a social technology, it would develop a line of ethical analysis that both acknowledged the power and potential of organizing by numbers, while remaining aware of the politics that arises from this power.

Applied Ethical Research on Quantification in Specific Domains

Beyond the above broad questions around quantification as such, it is useful to point out some specific empirical domains in which this research agenda could be applied, although these are inevitably limited by the scope of this article.

First, quantification is increasingly central in and may soon become transformative of workplace dynamics (Moore and Robinson 2015 ), with deep ethical implications. The use of metrics as control and subjectivity-shaping technologies in the workplace has been acknowledged (Wilson et al. 2020 ), but as these become linked to online platform, credit, and other data sources, the totalizing effects of such technologies may take on qualitatively different character. Such technologies of worker measurement carry ethical ramifications in terms of their consequences for worker economic and psychological well-being, and also in terms of their ramifications the human dignity of workers. Thus, it is urgent that ethics research devotes more attention to the ethical aspects of the forms and uses of workplace measurement.

Second, and relatedly, the ubiquity of online interaction and the emergence of algorithmic (and increasingly, AI) interaction (Beverungen et al. 2019 ) suggests that increasing swaths of human life will be mediated through datafying systems. The data thereby produced are a fundamental part of technology business models (Beverungen et al. 2015 ), and bode both new forms of extraction of personal information (Dean 2010 ) and the retroactive shaping of uses actions and ideas though algorithmic feedback (Mau 2019 ).s The dynamics of such data flows can shape political communities, influence democratic elections, and build or destroy reputations, as well as individual and group self-concepts. Both as a deontological issue of one’s property over oneself and of the consequences of such developments, ethics research must begin to untie these complex webs of data politics in digital cultures.

Third, as recent public health and environmental crises have revealed, data play an important macro-level role in public management with deep ethical ramifications. In the context of the COVID-19 pandemic, contact-tracing technologies may pose a key public health response, while raising important concerns about surveillance and data privacy (Taylor 2020 ). Public management responses in similar macro-level phenomena, from global warming to the genetic modification of species populations, require complex data modeling outside of the hands of most citizens, raising questions of data control, institutional trust, and political accountability (Zuboff 2019 ). It may be that the ethical imperative of preserving democratic society will increasingly require struggles on the terrain of data management, and the earlier we can build applicable knowledge about the social life of data, the better.

In conclusion, the current paper has addressed the ethics of quantification as a social phenomenon, in which numbers both represent and shape social reality. They do so through capturing everyday life in easily manageable yet opaque units, which specify the flow of life into specified values. These operations confer commensurability onto the variety of social experience, making life manageable in ways that support the functioning of organizations, markets, and governments. Yet they also bring a host of worries, about the loss of experience, the exclusion of alternative views, and the exploitation of data by powerful actors. The metric society has been increasingly described in dystopian terms, yet the quantification processes making it possible are only recently beginning to come under sociological scrutiny. The current paper attempts to push such scrutiny further and to define it as an important field of ethics.

Acknowledgements

The author would like to acknowledge Greg Molecke and Fiona Ottaviani for their close readings and in-depth comments on previous versions of this manuscript, as well as for their helpful discussion of the ideas in this manuscript.

Compliance with Ethical Standards

The authors declare that they have no conflicts of interest.

1 For a description of the various (and sometimes opposing) conceptions of performativity, see Gond et al ( 2016 ).

Editors at the Journal of Business Ethics are recused from all decisions relating to submissions with which there is any identified potential conflict of interest. Submissions to the Journal of Business Ethics from editors of the journal are handled by a senior independent editor at the journal and subject to full double blind peer review processes.

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Home Market Research

Business Research: Methods, Types & Examples

Content Index

Business research: Definition

Quantitative research methods, qualitative research methods, advantages of business research, disadvantages of business research, importance of business research.

Business research is a process of acquiring detailed information on all the areas of business and using such information to maximize the sales and profit of the business. Such a study helps companies determine which product/service is most profitable or in demand. In simple words, it can be stated as the acquisition of information or knowledge for professional or commercial purposes to determine opportunities and goals for a business.

Business research can be done for anything and everything. In general, when people speak about business research design , it means asking research questions to know where the money can be spent to increase sales, profits, or market share. Such research is critical to make wise and informed decisions.

LEARN ABOUT: Research Process Steps

For example: A mobile company wants to launch a new model in the market. But they are not aware of what are the dimensions of a mobile that are in most demand. Hence, the company conducts business research using various methods to gather information, and the same is then evaluated, and conclusions are drawn as to what dimensions are most in demand.

This will enable the researcher to make wise decisions to position his phone at the right price in the market and hence acquire a larger market share.

LEARN ABOUT:  Test Market Demand

Business research: Types and methodologies

Business research is a part of the business intelligence process. It is usually conducted to determine whether a company can succeed in a new region, to understand its competitors, or simply select a marketing approach for a product. This research can be carried out using steps in qualitative research methods or quantitative research methods.

Quantitative research methods are research methods that deal with numbers. It is a systematic empirical investigation using statistical, mathematical, or computational techniques . Such methods usually start with data collection and then proceed to statistical analysis using various methods. The following are some of the research methods used to carry out business research.

LEARN ABOUT: Data Management Framework

Survey research

Survey research is one of the most widely used methods to gather data, especially for conducting business research. Surveys involve asking various survey questions to a set of audiences through various types like online polls, online surveys, questionnaires, etc. Nowadays, most of the major corporations use this method to gather data and use it to understand the market and make appropriate business decisions.

Various types of surveys, like cross-sectional studies , which need to collect data from a set of audiences at a given point of time, or longitudinal surveys which are needed to collect data from a set of audiences across various time durations in order to understand changes in the respondents’ behavior are used to conduct survey research. With the advancement in technology, surveys can now be sent online through email or social media .

For example: A company wants to know the NPS score for their website i.e. how satisfied are people who are visiting their website. An increase in traffic to their website or the audience spending more time on a website can result in higher rankings on search engines which will enable the company to get more leads as well as increase its visibility.

Hence, the company can ask people who visit their website a few questions through an online survey to understand their opinions or gain feedback and hence make appropriate changes to the website to increase satisfaction.

Learn More:  Business Survey Template

Correlational research

Correlational research is conducted to understand the relationship between two entities and what impact each one of them has on the other. Using mathematical analysis methods, correlational research enables the researcher to correlate two or more variables .

Such research can help understand patterns, relationships, trends, etc. Manipulation of one variable is possible to get the desired results as well. Generally, a conclusion cannot be drawn only on the basis of correlational research.

For example: Research can be conducted to understand the relationship between colors and gender-based audiences. Using such research and identifying the target audience, a company can choose the production of particular color products to be released in the market. This can enable the company to understand the supply and demand requirements of its products.

Causal-Comparative research

Causal-comparative research is a method based on the comparison. It is used to deduce the cause-effect relationship between variables. Sometimes also known as quasi-experimental research, it involves establishing an independent variable and analyzing the effects on the dependent variable.

In such research, data manipulation is not done; however, changes are observed in the variables or groups under the influence of the same changes. Drawing conclusions through such research is a little tricky as independent and dependent variables will always exist in a group. Hence all other parameters have to be taken into consideration before drawing any inferences from the research.

LEARN ABOUT: Causal Research

For example: Research can be conducted to analyze the effect of good educational facilities in rural areas. Such a study can be done to analyze the changes in the group of people from rural areas when they are provided with good educational facilities and before that.

Another example can be to analyze the effect of having dams and how it will affect the farmers or the production of crops in that area.

LEARN ABOUT: Market research trends

Experimental research

Experimental research is based on trying to prove a theory. Such research may be useful in business research as it can let the product company know some behavioral traits of its consumers, which can lead to more revenue. In this method, an experiment is carried out on a set of audiences to observe and later analyze their behavior when impacted by certain parameters.

LEARN ABOUT: Behavioral Targeting

For example: Experimental research was conducted recently to understand if particular colors have an effect on consumers’ hunger. A set of the audience was then exposed to those particular colors while they were eating, and the subjects were observed. It was seen that certain colors like red or yellow increase hunger.

Hence, such research was a boon to the hospitality industry. You can see many food chains like Mcdonalds, KFC, etc., using such colors in their interiors, brands, as well as packaging.

Another example of inferences drawn from experimental research, which is used widely by most bars/pubs across the world, is that loud music in the workplace or anywhere makes a person drink more in less time. This was proven through experimental research and was a key finding for many business owners across the globe.

Online research / Literature research

Literature research is one of the oldest methods available. It is very economical, and a lot of information can be gathered using such research. Online research or literature research involves gathering information from existing documents and studies, which can be available at Libraries, annual reports, etc.

Nowadays, with the advancement in technology, such research has become even more simple and accessible to everyone. An individual can directly research online for any information that is needed, which will give him in-depth information about the topic or the organization.

Such research is used mostly by marketing and salespeople in the business sector to understand the market or their customers. Such research is carried out using existing information that is available from various sources. However, care has to be taken to validate the sources from where the information is going to be collected.

For example , a salesperson has heard a particular firm is looking for some solution that their company provides. Hence, the salesperson will first search for a decision maker from the company, investigate what department he is from, and understand what the target company is looking for and what they are into.

Using this research, he can cater his solution to be spot on when he pitches it to this client. He can also reach out to the customer directly by finding a means to communicate with him by researching online.’

LEARN ABOUT: 12 Best Tools for Researchers

Qualitative research is a method that has a high importance in business research. Qualitative research involves obtaining data through open-ended conversational means of communication. Such research enables the researcher to not only understand what the audience thinks but also why he thinks it.

In such research, in-depth information can be gathered from the subjects depending on their responses. There are various types of qualitative research methods, such as interviews, focus groups, ethnographic research, content analysis, and case study research, that are widely used.

Such methods are of very high importance in business research as they enable the researcher to understand the consumer. What motivates the consumer to buy and what does not is what will lead to higher sales, and that is the prime objective for any business.

Following are a few methods that are widely used in today’s world by most businesses.

Interviews are somewhat similar to surveys, like sometimes they may have the same types of questions used. The difference is that the respondent can answer these open-ended questions at length, and the direction of the conversation or the questions being asked can be changed depending on the response of the subject.

Such a method usually gives the researcher detailed information about the perspective or opinions of its subject. Carrying out interviews with subject matter experts can also give important information critical to some businesses.

For example: An interview was conducted by a telecom manufacturer with a group of women to understand why they have less number of female customers. After interviewing them, the researcher understood that there were fewer feminine colors in some of the models, and females preferred not to purchase them.

Such information can be critical to a business such as a  telecom manufacturer and hence it can be used to increase its market share by targeting women customers by launching some feminine colors in the market.

Another example would be to interview a subject matter expert in social media marketing. Such an interview can enable a researcher to understand why certain types of social media advertising strategies work for a company and why some of them don’t.

LEARN ABOUT: Qualitative Interview

Focus groups

Focus groups are a set of individuals selected specifically to understand their opinions and behaviors. It is usually a small set of a group that is selected keeping in mind the parameters for their target market audience to discuss a particular product or service. Such a method enables a researcher with a larger sample than the interview or a case study while taking advantage of conversational communication.

Focus group is also one of the best examples of qualitative data in education . Nowadays, focus groups can be sent online surveys as well to collect data and answer why, what, and how questions. Such a method is very crucial to test new concepts or products before they are launched in the market.

For example: Research is conducted with a focus group to understand what dimension of screen size is preferred most by the current target market. Such a method can enable a researcher to dig deeper if the target market focuses more on the screen size, features, or colors of the phone. Using this data, a company can make wise decisions about its product line and secure a higher market share.

Ethnographic research

Ethnographic research is one of the most challenging research but can give extremely precise results. Such research is used quite rarely, as it is time-consuming and can be expensive as well. It involves the researcher adapting to the natural environment and observing its target audience to collect data. Such a method is generally used to understand cultures, challenges, or other things that can occur in that particular setting.

For example: The world-renowned show “Undercover Boss” would be an apt example of how ethnographic research can be used in businesses. In this show, the senior management of a large organization works in his own company as a regular employee to understand what improvements can be made, what is the culture in the organization, and to identify hard-working employees and reward them.

It can be seen that the researcher had to spend a good amount of time in the natural setting of the employees and adapt to their ways and processes. While observing in this setting, the researcher could find out the information he needed firsthand without losing any information or any bias and improve certain things that would impact his business.

LEARN ABOUT:   Workforce Planning Model

Case study research

Case study research is one of the most important in business research. It is also used as marketing collateral by most businesses to land up more clients. Case study research is conducted to assess customer satisfaction and document the challenges that were faced and the solutions that the firm gave them.

These inferences are made to point out the benefits that the customer enjoyed for choosing their specific firm. Such research is widely used in other fields like education, social sciences, and similar. Case studies are provided by businesses to new clients to showcase their capabilities, and hence such research plays a crucial role in the business sector.

For example: A services company has provided a testing solution to one of its clients. A case study research is conducted to find out what were the challenges faced during the project, what was the scope of their work, what objective was to be achieved, and what solutions were given to tackle the challenges.

The study can end with the benefits that the company provided through its solutions, like reduced time to test batches, easy implementation or integration of the system, or even cost reduction. Such a study showcases the capability of the company, and hence it can be stated as empirical evidence of the new prospect.

Website visitor profiling/research

Website intercept surveys or website visitor profiling/research is something new that has come up and is quite helpful in the business sector. It is an innovative approach to collect direct feedback from your website visitors using surveys. In recent times a lot of business generation happens online, and hence it is important to understand the visitors of your website as they are your potential customers.

Collecting feedback is critical to any business, as without understanding a customer, no business can be successful. A company has to keep its customers satisfied and try to make them loyal customers in order to stay on top.

A website intercept survey is an online survey that allows you to target visitors to understand their intent and collect feedback to evaluate the customers’ online experience. Information like visitor intention, behavior path, and satisfaction with the overall website can be collected using this.

Depending on what information a company is looking for, multiple forms of website intercept surveys can be used to gather responses. Some of the popular ones are Pop-ups, also called Modal boxes, and on-page surveys.

For example: A prospective customer is looking for a particular product that a company is selling. Once he is directed to the website, an intercept survey will start noting his intent and path. Once the transaction has been made, a pop-up or an on-page survey is provided to the customer to rate the website.

Such research enables the researcher to put this data to good use and hence understand the customers’ intent and path and improve any parts of the website depending on the responses, which in turn would lead to satisfied customers and hence, higher revenues and market share.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

• Business research helps to identify opportunities and threats.
• It helps identify research problems , and using this information, wise decisions can be made to tackle the issue appropriately.
• It helps to understand customers better and hence can be useful to communicate better with the customers or stakeholders.
• Risks and uncertainties can be minimized by conducting business research in advance.
• Financial outcomes and investments that will be needed can be planned effectively using business research.
• Such research can help track competition in the business sector.
• Business research can enable a company to make wise decisions as to where to spend and how much.
• Business research can enable a company to stay up-to-date with the market and its trends, and appropriate innovations can be made to stay ahead in the game.
• Business research helps to measure reputation management
• Business research can be a high-cost affair
• Most of the time, business research is based on assumptions
• Business research can be time-consuming
• Business research can sometimes give you inaccurate information because of a biased population or a small focus group.
• Business research results can quickly become obsolete because of the fast-changing markets

Business research is one of the most effective ways to understand customers, the market, and competitors. Such research helps companies to understand the demand and supply of the market. Using such research will help businesses reduce costs and create solutions or products that are targeted to the demand in the market and the correct audience.

In-house business research can enable senior management to build an effective team or train or mentor when needed. Business research enables the company to track its competitors and hence can give you the upper hand to stay ahead of them.

Failures can be avoided by conducting such research as it can give the researcher an idea if the time is right to launch its product/solution and also if the audience is right. It will help understand the brand value and measure customer satisfaction which is essential to continuously innovate and meet customer demands.

This will help the company grow its revenue and market share. Business research also helps recruit ideal candidates for various roles in the company. By conducting such research, a company can carry out a SWOT analysis , i.e. understand the strengths, weaknesses, opportunities, and threats. With the help of this information, wise decisions can be made to ensure business success.

LEARN ABOUT:  Market research industry

Business research is the first step that any business owner needs to set up his business to survive or to excel in the market. The main reason why such research is of utmost importance is that it helps businesses to grow in terms of revenue, market share, and brand value.

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Marketing91

Importance of Quantitative Research

June 12, 2023 | By Hitesh Bhasin | Filed Under: Marketing

Quantitative research is a systematic approach to collect information via sampling methods, for instance, questionnaires, online polls and online surveys. It is gathered from both potential and existing customers and clients and depicted in terms of numerical.

Quantitative research is generally used in fields like political science, gender studies, community health, marketing , sociology, economics, psychology, demography, and education . It’s objective is to employ mathematical theories in relation to phenomena. The process offers a connection between mathematical expression and empirical observation.

Quantitative research is a method to measure variables, analyze them and report relationships amongst the studied variables through a numerical system. Its objective is to understand, analyze, describe and make future predictions of a product or a service because after understanding the numbers, it becomes easier for people to make suitable changes. It deals in objective, logic, and numbers and puts its focus on convergent reasoning and detailed and unchanging data.

The data in quantitative research is collected through structured research, and the results are based on a larger size of samples that represents or reflects the population. An important fact about this kind of research is that it can be repeated and replicated. The questions about the research study are defined, and all its aspects are designed so that the data collected is reliable and accurate.

The quantitative researcher uses several tools to gather numerical data that is in the form of statistics and numbers and is arranged in non-textual forms like figures, charts, and tables.

Table of Contents

1. Establishes relationships between variables

Quantitative research is considered a traditional sort of scientific method that tries to establish causal relationships and test its strength as well as significance. It has put its onus on objective measurements and numerical, mathematical or statistical data analysis that has been collected over a while through sampling methods or via maneuvering the existing statistical data.

The research is all about making comparisons and determining the relationship between the independent and outcome variable and generalizing the collected data across numerous groups of people. The research design can be either experimental which establishes casualty or descriptive that establishes associations between variables depending upon the situation. The research templates are investigative, and the achieved result is unbiased, statistical and logical.

The importance of quantitative research is that it allows establishing a relationship between variables through a structured method on a sample that is the representation of the entire population.

2. Objective and reliable data collection

Quantitative research is a method used in generating reliable and accurate outcome data by analyzing and measuring them. It can explain why the data is collected and also about its statistical treatment. It also deals with results about relations in the research and reports about the events that were unanticipated during the collection of data.

The research especially explains the reason why both planned and actual analysis differs from each other. It gives a fair idea about handling the data that has gone missing, and why does it not undermine the validity of the research analysis.

The importance of quantitative research is that it helps in objective and reliable collection of data that is discussed in a logical, analytical and comprehensive manner and highlights key findings.

3. Identifying the research problem

The importance of quantitative research is that it is used to investigate research problems. It assists in combining key themes and making viable notes about studies that have been using the same type of analysis and inquiry.

The research also makes viable notes about key gaps that exist in the collected data and how the research study can fill the existing gaps and make suitable clarifications about existing knowledge. Quantitative research is conducted to offer an outline of the theory related to the study to describe the theoretical framework accurately.

It also gives important descriptions about complex or any term that looks unfamiliar, about relevant ideas and concepts and the necessary background information so that the research problem can be placed in an appropriate context. For instance, in a place like economic, cultural or historical.

4. Importance of quantitative research in methodology

Quantitative research offers details about the objectives of the study that is taking place and how will it be achieved so that an informed assessment of the methods to obtain results of the research problem can also be done.

The importance of quantitative research is that it offers tremendous help in studying samples and populations. It discusses in detail relevant questions, for instance, where did the data come from, where are the existing gaps in the data, how robust is it and what were the exclusions within the data research.  It is vital to note the process for their selection and describe the methods and tools that are being used by the researcher to collect the information.

The quantitative research identifies variables that are being measured, gives a detailed description of the applicable method that is used in obtaining relevant data, notes down important criteria about the fact that the data was already in existence or the researcher gathered himself.

Remember to mention details and describe what type of instrument was used in the data collection and why, if the researcher was collecting it. Mention any limitations or discrepancies in the methods used for data collection if any. The importance of quantitative research in methodology is that it helps to describe the process or method for both processing and analyzing of data in detail, specific instruments used for studying the research objective and the type of software used in the manipulation of existing data.

In the quantitative method, the findings of the research are written in a precise form that is entirely objective. The non-textual elements like charts, tables and graphs are there that they are used to add to the overall description of the available result. It also clarifies important points so that the reader can understand the data and information in a better manner.

5. Helps in Testing theories

Quantitative research is, in fact, about the cause and also the effect of social phenomena. It starts with an assumption and is used to test hypotheses via its deductive or investigative nature. It tries to minimize a complex problem and then restructure it into limited variables that can be tested easily.

Before starting on its design, it is imperative to decide whether to make the design experimental or descriptive because it will have a direct impact on how the researcher collects, analyzes and interprets the results of the collected data and tests theories. In a descriptive study, the subject of the study is a sample population that can be between hundreds and thousands.

These subjects are measured once, and the purpose of the research is to establish associations between the different variables. The quantitative research ensures that it provides an authenticated estimate of the relationships between variables in a generalized manner. The experimental design, on the other hand, is about a very small and specific sample population chosen with a particular purpose. The subjects are measured before and after the particular treatment to establish casualty between the variables.

The importance of quantitative research is that the researchers can use this research to test hypotheses during the experiment and undertake research on a large-scale basis on the general population. It easily carries out statistical analysis to gather data for accord and is presented in numerical form, for instance, percentages, statistics, etc. The researcher asks the participants through a survey, questionnaire , etc.

specific questions and the answers help him to collect samples. The researcher further analyses the data through the help of statistics.  This is done to get an unbiased result that can be used as a generalized version for a larger population.

6. Helps measure customer experience

The importance of quantitative research is that it is easy for it to measure data and show the results via objective data. It provides descriptive data that can be broken down to determine variances between particular groups, for example, age groups. The prediction based on this type of research is via numerical data and is difficult to deny or argue.

One of the significant benefits of opting for quantitative research is its ability to expand the data into predictions and measure customer experience . Commercial business relies heavily on its customers and has tried to put its onus on service-focused operations. For them, the development and measuring of customer service and performance are of utmost importance.

Quantitative research is an important element to obtain a true reflection of the customer experience so that it can make real alterations in performance. The importance of quantitative research in measuring customer experience is immense.

It takes high-volume suitable sample size and finds valid, accurate and trustworthy results in customer insight. It adopts survey-based research so that it can obtain feedback that is in direct relation to population opinions and ideas.

The quantitative research helps in addressing vital parts of the customer journey to discover what the customers think about their experience to make informed business decisions based on this feedback. Another importance of quantitative research is to identify the numerous blockages that are affecting sales performance and restricting service excellence . The research offers a chance to stay connected with customer trends through preferred survey formats which are scheduled regularly to receive instant feedback.

7. Statistical analysis

Quantitative research is a tool that is used to understand and measure the relationship between the independent and dependent variables and generalize a finding. It has an important role to play in product development because of the data collected from this type of research.

For instance, user preference, demographics and market size offers vital information that proves credible during the making of business decisions. Quantitative research aims to count the features, classify them accordingly and develop statistical models.

The research assists in explaining the techniques that are being used by researchers to clean the data set. It chooses a statistical procedure and specifies the computer program that applies to offer a rationale for its reference and use.

Moreover, it is important to offer details about the assumptions in every process and state the steps that ensure there were no violations during the said procedure. Statistical analysis helps to obtain important facts through qualitative research from research data, for instance, differences between demographics and groups and determining preference trends.

It records everything about the data and also about the changes if any that occurs. The importance of quantitative research is that it can provide sample sizes, confidence intervals and descriptive statistics for every variable and the value of the test statistic, its significance level, degrees of freedom and the direction it needs to take.

This will be a great help in avoiding inferring casualty and conveying global effects by integrating graphic representations of confidence intervals.

Liked this post? Check out the complete series on Market research

Related posts:

• Quantitative Market Research
• 11 Types Of Quantitative Research options that exist for Market Researchers
• Qualitative vs. Quantitative Research: What is the Difference?
• What are the Characteristics of Quantitative Research?
• Research Ethics – Importance and Principles of Ethics in Research
• What is Research Design? Type of Research Designs
• How to Write Research Proposal? Research Proposal Format
• 7 Key Differences between Research Method and Research Methodology
• Qualitative Research: Meaning, and Features of Qualitative Research
• What is the Importance of Qualitative Research to an Organization?

About Hitesh Bhasin

Hitesh Bhasin is the CEO of Marketing91 and has over a decade of experience in the marketing field. He is an accomplished author of thousands of insightful articles, including in-depth analyses of brands and companies. Holding an MBA in Marketing, Hitesh manages several offline ventures, where he applies all the concepts of Marketing that he writes about.

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Why Does Your Business Need Qualitative And Quantitative Research?

If you own a business, you understand the value of information and data. Almost every firm makes judgments based on data. Some businesses strongly emphasise quantitative research, while others devote their efforts to gathering and interpreting qualitative data analysis.

We can all concur that quantitative market research knowledge can potentially convert your company into a results-driven, strong franchise. Also, if you intend to make it big, you must do your homework before diving in. You can conduct qualitative or quantitative research or both. They each have distinct advantages. The best results are obtained by combining the two.

This post will discuss the qualitative research approach, why it is vital for your business, quantitative market research services, and its significance in business. Also, we will discuss why your business needs both qualitative and quantitative research. Then we’ll tell you about the platform that offers the best qualitative and quantitative market research services. Let’s get this started.

• Qualitative Research

The qualitative research approach focuses on how individuals feel, what they believe, and why they make specific decisions. For example, suppose you are considering altering your branding. In that case, you could conduct qualitative data analysis to understand people’s emotional reactions to your new identity and what they identify it with.

Do you know what qualitative research is? If your answer is a no, it’s a research strategy that produces semi-structured results, guaranteeing that the conversation – one-on-one or in a focus group – stays on topic and relevant to deliver the information you want. There are many qualitative research platforms, but your chosen one matters greatly.

Why Does Your Business Need Qualitative Research?

The benefit of qualitative research or qualitative data analysis is that it gives you a comprehensive image of your consumers. The following are some of the reasons why the qualitative research approach is essential for any business.

Qualitative Research Approach Brings Flexibility

Using qualitative project management or research in all facets of your business’s operation gives you flexibility and fluidity. The main implication of the qualitative research approach is that the qualitative data analysis must be relevant to the subject matter and should give the best possible path ahead.

Suppose the data collection source does not meet the requirements. In that case, the qualitative research company has the liberty and flexibility to alter the source or the data collection method to adhere to and respect the quality elements.

Qualitative Research is Based on Human Perceptions and Experiences

Since the best qualitative research platforms understand the value of qualitative research, the data collected and obtained is heavily dependent on human experiences and observations.

The human mind operates on two main functioning modes, one centred on more facts and numbers and the other on our subconsciousness. The qualitative content analysis considers a person’s gut reactions and instincts since they result from experiences and observations accumulated over time.

Qualitative Data Analysis Has a Predictive Character

The data produced and gathered has a predictive character, which is one of the benefits and merits of the qualitative research approach. The key reason for this is that the qualitative research company focuses on one specific group to gather data on the subject matter and that specific customers share the frequency and are like-minded.

All of this leads to data that is verified on quality parameters. It may be a disadvantage for the research aspect, but the data gathered is viable and useful for the organisation that requires it.

Qualitative Research Breeds Creativity

One of the primary reasons that industry experts and professionals recommend businesses to comprehend and implement a qualitative research approach is that it incorporates the value of creativity in its technique. Since it is based more on emotional responses, experiences, and perceptions, the mode of operation allows consumers to voice their opinions honestly, transparently, and authentically.

They are given the flexibility of creative expression, which aids the research team in producing genuine outputs and final products.

Qualitative Research is a Fluid Process

Another advantage of the qualitative research approach is that it is an open-ended, fluid procedure. The research is not time-limited because it must conform to and meet quality requirements.

It also has an emotional component because it is based on human experiences and perceptions you cannot get in a certain questionnaire or time frame. The replies acquired are mostly influenced by the person’s actions or impacts on their conduct in various life events. Therefore the study must be open-ended in nature.

Qualitative Research Gives Insights and Information About the Business

In today’s highly competitive and ever-changing market, it is critical for businesses to communicate with their clients on a psychological and emotional level. It provides a deeper grasp of the consumers and target audience’s actual wants and developing desires. It assists businesses in curating, designing, planning, and manufacturing product offers that meet the wants and expectations of their customers.

Furthermore, it promotes the creation of industry-related data and insights that benefit the whole industrial domain and its enterprises. As a result, a qualitative research approach must be used to capture and comprehend the prospective customer’s behavioural psychology.

Qualitative Research Saves Cost

One of the most significant benefits of hiring a good qualitative research company for your business is that it saves money without affecting the total marketing and sales budget. You can implement the qualitative research approach with smaller sample numbers compared to other research approaches.

Its result is swift and legitimate, giving the research team confidence to carry forward with the project as good and beneficial data has been supplied to the firm management.

Qualitative Research Provides Extra Material for Your Company’s Marketing and Creative Teams

As previously said, the qualitative research approach has an artistic element that works as an intrinsic component. And when more data is acquired based on real feelings, experiences, and observations while putting human consciousness and psychology at the forefront, the material becomes more authentic and fruitful.

It greatly assists the creative, advertising, and sales departments in planning, designing and implementing genuine and effective brand and promotional campaigns. To summarise, it is a win-win scenario for all parties concerned.

It greatly assists the firm’s management in attracting the target audience and market through unique and out-of-the-box promotional and marketing concepts and campaigns. Plus, all of it assists in boosting the general sales and earnings of the organization achieving all the short and long-term targets.

Example of How Qualitative Research is Conducted

One way the qualitative research approach is conducted is through one-on-one interviews. One-on-one interviews can be conducted over the phone or in person. It is a more personalised method that gives a greater knowledge of the clients without outside influence. Participants are generally more at ease with this method than with any other.

• Quantitative Research

Quantitative market research collects customer data on attitudes, behaviours, views, and other characteristics to support or refute a hypothesis. This is done by gathering numerical data, which is easily measurable to establish statistical significance.

Quantitative research or quantitative data analysis, as it may be called, gathers numerical data through closed-ended questions, such as Likert scales or questionnaire forms. Once you’ve distributed your survey to your intended audience, you can quickly quantify the answers for each answer choice.

The questions you pose must be impartial to gather and interpret respondent data. Numbers are fundamental to quantitative data collection services. It employs statistical analysis and data to spotlight critical information about your company and market.

This sort of data, obtained through multiple-choice surveys, can assist you in gauging interest in your firm and its offers. For example, quantitative research can help answer problems like:

• Is there a demand for your goods and services?
• How well-known is your product or service in the market?
• How many individuals are considering purchasing your goods or service?
• What kind of personalities are your most loyal customers?
• What are their purchasing patterns?
• How are your target market’s demands changing?
• How long are visitors remaining on your business website, and which website are they leaving to?

Most significantly, quantitative market research services are statistically legitimate since they are mathematically founded. This implies you use its results to forecast the future of your business.

Quantitative data analysis and quantitative data collection services may sound stressful, which is why some business owners seek the services of a good quantitative market research agency.

This gives them a chance to focus on the more important aspect of the business while the quantitative market research companies do their job.

Why Your Business Needs Quantitative Market Research Services

Below are some of the importance of quantitative market research services for your business.

Quantitative Data Analysis Services Establishes Connections Between Variables

Quantitative research is a conventional scientific procedure that attempts to establish causal correlations and measure their strength and relevance. It has emphasised quantifiable metrics, and numerical, mathematical, or statistical market research gathered over time using sampling methods or by manipulating existing statistical data.

Good quantitative market research companies emphasise comparing, finding the link between the uncontrolled and outcome variables, and generalising the acquired data over many consumer groups.

Depending on the scenario, the research design might be experimental, demonstrating causality, or descriptive, identifying connections between variables. The quantitative research methods are investigative, and the outcome is objective, statistical, and logical.

The importance of quantitative market research to your business is that it helps you establish a link between variables using a systematic manner on a sample representing the whole consumer base.

Quantitative Market Research Gives Data That is Objective and Dependable

Quantitative research is a way of collecting trustworthy and accurate business outcome data. This is accomplished through analysis and measurement. It can illustrate why the data is obtained as well as how it is statistically treated.

It also deals with study results concerning relationships and reports on unexpected incidents during data collection. The study specifically shows why planned, and real analysis differ from one another. It offers a good notion of how to deal with missing data.

The importance of quantitative data analysis to your organization is that it aids in the objective and dependable acquisition of data, which is then presented in a logical, analytical, and complete manner, highlighting crucial results.

Quantitative Market Research Helps in Measuring The Consumer Experience

The importance of quantitative market research is that it is simple to assess data and demonstrate outcomes using objective data. It gives descriptive data that a quantitative market researcher may split down to discover differences between certain groups, such as age groupings.

Quantitative data analysis forecast is based on numerical facts and is tough to refute or dispute. One of the major advantages of doing quantitative market research is the capacity to transform data into forecasts and quantify customer experience. Businesses rely largely on their consumers and have attempted to focus on service-oriented operations. Customer services and performance development and measurement are critical to them.

Quantitative research is essential for obtaining an accurate depiction of the consumer experience and making significant changes in performance. The significance of quantitative market research and quantitative data analysis in gauging customer experience cannot be overstated.

It uses a large enough sample size to obtain valid, accurate, and trustworthy outcomes in consumer insight. It uses survey-based research to collect input related to population attitudes and thoughts. Quantitative research for marketing assists in addressing critical sections of the customer journey to learn what consumers think about their experience so you can make educated business decisions based on this input.

Another function of quantitative market research is discovering the multiple impediments to sales effectiveness and service excellence. The study provides an opportunity to keep connected with client trends through selected survey forms that are scheduled regularly to obtain a fast response.

Example of How Quantitative Research is Conducted

The most effective quantitative research method is questionnaires and surveys. You can deliver questionnaires and surveys containing a list of acceptable responses to a wide sample group.

The options narrow the field of possible responses, resulting in a more thorough survey. The survey is measured by visually showing the proportion of respondents who picked each response. Quantitative data analysis has gotten much easier with online survey questionnaires.

The simplest answer to whether it is possible to combine qualitative and quantitative research is a resounding YES. Usability testing incorporating qualitative and quantitative data analysis for company improvement may be both complementary and productive.

Various data have different values depending on where you are in your business. The qualitative research approach is useful in the early phases of a firm since it allows you to evaluate choices, identify problems, and make modifications.

Although quantitative market research is beneficial throughout the business development process, it can have a higher impact later on when a product is ready to be published or utilised. You might want to track which customers appreciated it the most and which did not.

While each approach has benefits and drawbacks, you are not compelled to select one. Use both data to understand the “what” and “why” questions. Quantitative market research can only tell you whether the metrics you monitor are rising or dropping, not why. The “why” of a user study may be the most important part, and qualitative data analysis will assist in answering that query.

The ideal usability studies contain quantitative and qualitative data analysis to fully comprehend the client experience, efficiency, and the identification of any faults or business defects.

Where to Get The Best Qualitative And Quantitative Research For Your Business?

There are several quantitative market research companies and qualitative research platforms available. But only a few offer quantitative and qualitative research. One such company, and one we recommend, is Insights Opinion, whose simple platform gives users the statistics as well as the explanation behind the findings.

Their team is well-versed in how you intend to utilise the data and what you ought to know. They adhere to strict quality standards; consequently, their qualitative and quantitative market research methodologies produce reliable findings. Visit the Insights Opinion website now to get one of the best qualitative and quantitative data analysis services.

Customer research methodologies, both qualitative and quantitative, collaborate to completely grasp the usefulness of data and customer preferences and to assist in producing the greatest resources available. The more value you derive from your optimization process, the more you will improve it.

Using the best qualitative and quantitative market research agency is important to build a better business. If you’re ready to improve your business with qualitative and quantitative market research methods, visit Insights Opinion and get started immediately.

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Importance of Quantitative Research Across Fields

First of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people succeed in business; 4) it enables us to disprove lies and support truths; and 5) it serves as a means to find, gauge, and seize opportunities, as well as helps in finding solutions to social and health problems (in fact, the discovery of COVID-19 vaccines is a product of research).

Now, quantitative research, as a type of research that explains phenomena according to numerical data which are analyzed by means of mathematically based methods, especially statistics, is very important because it relies on hard facts and numerical data to gain as objective a picture of people’s opinion as possible or an objective understanding of reality. Hence, quantitative research enables us to map out and understand the world in which we live.

In addition, quantitative research is important because it enables us to conduct research on a large scale; it can reveal insights about broader groups of people or the population as a whole; it enables researchers to compare different groups to understand similarities and differences; and it helps businesses understand the size of a new opportunity. As we can see, quantitative research is important across fields and disciplines.

Let me now briefly discuss the importance of quantitative research across fields and disciplines. But for brevity’ sake, the discussion that follows will only focus on the importance of quantitative research in psychology, economics, education, environmental science and sustainability, and business.

First, on the importance of quantitative research in psychology .

We know for a fact that one of the major goals of psychology is to understand all the elements that propel human (as well as animal) behavior. Here, one of the most frequent tasks of psychologists is to represent a series of observations or measurements by a concise and suitable formula. Such a formula may either express a physical hypothesis, or on the other hand be merely empirical, that is, it may enable researchers in the field of psychology to represent by a few well selected constants a wide range of experimental or observational data. In the latter case it serves not only for purposes of interpolation, but frequently suggests new physical concepts or statistical constants. Indeed, quantitative research is very important for this purpose.

It is also important to note that in psychology research, researchers would normally discern cause-effect relationships, such as the study that determines the effect of drugs on teenagers. But cause-effect relationships cannot be elucidated without hard statistical data gathered through observations and empirical research. Hence, again, quantitative research is very important in the field of psychology because it allows researchers to accumulate facts and eventually create theories that allow researchers in psychology to understand human condition and perhaps diminish suffering and allow human race to flourish.

Second, on the importance of quantitative research in economics .

In general perspective, the economists have long used quantitative methods to provide us with theories and explanations on why certain things happen in the market. Through quantitative research too, economists were able to explain why a given economic system behaves the way it does. It is also important to note that the application of quantitative methods, models and the corresponding algorithms helps to make more accurate and efficient research of complex economic phenomena and issues, as well as their interdependence with the aim of making decisions and forecasting future trends of economic aspects and processes.

Third, on the importance of quantitative research in education .

Again, quantitative research deals with the collection of numerical data for some type of analysis. Whether a teacher is trying to assess the average scores on a classroom test, determine a teaching standard that was most commonly missed on the classroom assessment, or if a principal wants to assess the ways the attendance rates correlate with students’ performance on government assessments, quantitative research is more useful and appropriate.

In many cases too, school districts use quantitative data to evaluate teacher effectiveness from a number of measures, including stakeholder perception surveys, students’ performance and growth on standardized government assessments, and percentages on their levels of professionalism. Quantitative research is also good for informing instructional decisions, measuring the effectiveness of the school climate based on survey data issued to teachers and school personnel, and discovering students’ learning preferences.

Fourth, on the importance of quantitative research in Environmental Science and Sustainability.

Addressing environmental problems requires solid evidence to persuade decision makers of the necessity of change. This makes quantitative literacy essential for sustainability professionals to interpret scientific data and implement management procedures. Indeed, with our world facing increasingly complex environmental issues, quantitative techniques reduce the numerous uncertainties by providing a reliable representation of reality, enabling policy makers to proceed toward potential solutions with greater confidence. For this purpose, a wide range of statistical tools and approaches are now available for sustainability scientists to measure environmental indicators and inform responsible policymaking. As we can see, quantitative research is very important in environmental science and sustainability.

But how does quantitative research provide the context for environmental science and sustainability?

Environmental science brings a transdisciplinary systems approach to analyzing sustainability concerns. As the intrinsic concept of sustainability can be interpreted according to diverse values and definitions, quantitative methods based on rigorous scientific research are crucial for establishing an evidence-based consensus on pertinent issues that provide a foundation for meaningful policy implementation.

And fifth, on the importance of quantitative research in business .

As is well known, market research plays a key role in determining the factors that lead to business success. Whether one wants to estimate the size of a potential market or understand the competition for a particular product, it is very important to apply methods that will yield measurable results in conducting a  market research  assignment. Quantitative research can make this happen by employing data capture methods and statistical analysis. Quantitative market research is used for estimating consumer attitudes and behaviors, market sizing, segmentation and identifying drivers for brand recall and product purchase decisions.

Indeed, quantitative data open a lot of doors for businesses. Regression analysis, simulations, and hypothesis testing are examples of tools that might reveal trends that business leaders might not have noticed otherwise. Business leaders can use this data to identify areas where their company could improve its performance.