Weekend batch
Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.
Free eBook: Top Programming Languages For A Data Scientist
Normality Test in Minitab: Minitab with Statistics
Machine Learning Career Guide: A Playbook to Becoming a Machine Learning Engineer
The bottom line.
Hypothesis testing, sometimes called significance testing, is an act in statistics whereby an analyst tests an assumption regarding a population parameter. The methodology employed by the analyst depends on the nature of the data used and the reason for the analysis.
Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data. Such data may come from a larger population or a data-generating process. The word "population" will be used for both of these cases in the following descriptions.
In hypothesis testing, an analyst tests a statistical sample, intending to provide evidence on the plausibility of the null hypothesis. Statistical analysts measure and examine a random sample of the population being analyzed. All analysts use a random population sample to test two different hypotheses: the null hypothesis and the alternative hypothesis.
The null hypothesis is usually a hypothesis of equality between population parameters; e.g., a null hypothesis may state that the population mean return is equal to zero. The alternative hypothesis is effectively the opposite of a null hypothesis. Thus, they are mutually exclusive , and only one can be true. However, one of the two hypotheses will always be true.
The null hypothesis is a statement about a population parameter, such as the population mean, that is assumed to be true.
If an individual wants to test that a penny has exactly a 50% chance of landing on heads, the null hypothesis would be that 50% is correct, and the alternative hypothesis would be that 50% is not correct. Mathematically, the null hypothesis is represented as Ho: P = 0.5. The alternative hypothesis is shown as "Ha" and is identical to the null hypothesis, except with the equal sign struck-through, meaning that it does not equal 50%.
A random sample of 100 coin flips is taken, and the null hypothesis is tested. If it is found that the 100 coin flips were distributed as 40 heads and 60 tails, the analyst would assume that a penny does not have a 50% chance of landing on heads and would reject the null hypothesis and accept the alternative hypothesis.
If there were 48 heads and 52 tails, then it is plausible that the coin could be fair and still produce such a result. In cases such as this where the null hypothesis is "accepted," the analyst states that the difference between the expected results (50 heads and 50 tails) and the observed results (48 heads and 52 tails) is "explainable by chance alone."
Some statisticians attribute the first hypothesis tests to satirical writer John Arbuthnot in 1710, who studied male and female births in England after observing that in nearly every year, male births exceeded female births by a slight proportion. Arbuthnot calculated that the probability of this happening by chance was small, and therefore it was due to “divine providence.”
Hypothesis testing helps assess the accuracy of new ideas or theories by testing them against data. This allows researchers to determine whether the evidence supports their hypothesis, helping to avoid false claims and conclusions. Hypothesis testing also provides a framework for decision-making based on data rather than personal opinions or biases. By relying on statistical analysis, hypothesis testing helps to reduce the effects of chance and confounding variables, providing a robust framework for making informed conclusions.
Hypothesis testing relies exclusively on data and doesn’t provide a comprehensive understanding of the subject being studied. Additionally, the accuracy of the results depends on the quality of the available data and the statistical methods used. Inaccurate data or inappropriate hypothesis formulation may lead to incorrect conclusions or failed tests. Hypothesis testing can also lead to errors, such as analysts either accepting or rejecting a null hypothesis when they shouldn’t have. These errors may result in false conclusions or missed opportunities to identify significant patterns or relationships in the data.
Hypothesis testing refers to a statistical process that helps researchers determine the reliability of a study. By using a well-formulated hypothesis and set of statistical tests, individuals or businesses can make inferences about the population that they are studying and draw conclusions based on the data presented. All hypothesis testing methods have the same four-step process, which includes stating the hypotheses, formulating an analysis plan, analyzing the sample data, and analyzing the result.
Sage. " Introduction to Hypothesis Testing ," Page 4.
Elder Research. " Who Invented the Null Hypothesis? "
Formplus. " Hypothesis Testing: Definition, Uses, Limitations and Examples ."
Hypothesis testing is the act of testing a hypothesis or a supposition in relation to a statistical parameter. Analysts implement hypothesis testing in order to test if a hypothesis is plausible or not.
In data science and statistics , hypothesis testing is an important step as it involves the verification of an assumption that could help develop a statistical parameter. For instance, a researcher establishes a hypothesis assuming that the average of all odd numbers is an even number.
In order to find the plausibility of this hypothesis, the researcher will have to test the hypothesis using hypothesis testing methods. Unlike a hypothesis that is ‘supposed’ to stand true on the basis of little or no evidence, hypothesis testing is required to have plausible evidence in order to establish that a statistical hypothesis is true.
Perhaps this is where statistics play an important role. A number of components are involved in this process. But before understanding the process involved in hypothesis testing in research methodology, we shall first understand the types of hypotheses that are involved in the process. Let us get started!
In data sampling, different types of hypothesis are involved in finding whether the tested samples test positive for a hypothesis or not. In this segment, we shall discover the different types of hypotheses and understand the role they play in hypothesis testing.
Alternative Hypothesis (H1) or the research hypothesis states that there is a relationship between two variables (where one variable affects the other). The alternative hypothesis is the main driving force for hypothesis testing.
It implies that the two variables are related to each other and the relationship that exists between them is not due to chance or coincidence.
When the process of hypothesis testing is carried out, the alternative hypothesis is the main subject of the testing process. The analyst intends to test the alternative hypothesis and verifies its plausibility.
The Null Hypothesis (H0) aims to nullify the alternative hypothesis by implying that there exists no relation between two variables in statistics. It states that the effect of one variable on the other is solely due to chance and no empirical cause lies behind it.
The null hypothesis is established alongside the alternative hypothesis and is recognized as important as the latter. In hypothesis testing, the null hypothesis has a major role to play as it influences the testing against the alternative hypothesis.
(Must read: What is ANOVA test? )
The Non-directional hypothesis states that the relation between two variables has no direction.
Simply put, it asserts that there exists a relation between two variables, but does not recognize the direction of effect, whether variable A affects variable B or vice versa.
The Directional hypothesis, on the other hand, asserts the direction of effect of the relationship that exists between two variables.
Herein, the hypothesis clearly states that variable A affects variable B, or vice versa.
A statistical hypothesis is a hypothesis that can be verified to be plausible on the basis of statistics.
By using data sampling and statistical knowledge, one can determine the plausibility of a statistical hypothesis and find out if it stands true or not.
(Related blog: z-test vs t-test )
Now that we have understood the types of hypotheses and the role they play in hypothesis testing, let us now move on to understand the process in a better manner.
In hypothesis testing, a researcher is first required to establish two hypotheses - alternative hypothesis and null hypothesis in order to begin with the procedure.
To establish these two hypotheses, one is required to study data samples, find a plausible pattern among the samples, and pen down a statistical hypothesis that they wish to test.
A random population of samples can be drawn, to begin with hypothesis testing. Among the two hypotheses, alternative and null, only one can be verified to be true. Perhaps the presence of both hypotheses is required to make the process successful.
At the end of the hypothesis testing procedure, either of the hypotheses will be rejected and the other one will be supported. Even though one of the two hypotheses turns out to be true, no hypothesis can ever be verified 100%.
(Read also: Types of data sampling techniques )
Therefore, a hypothesis can only be supported based on the statistical samples and verified data. Here is a step-by-step guide for hypothesis testing.
First things first, one is required to establish two hypotheses - alternative and null, that will set the foundation for hypothesis testing.
These hypotheses initiate the testing process that involves the researcher working on data samples in order to either support the alternative hypothesis or the null hypothesis.
Once the hypotheses have been formulated, it is now time to generate a testing plan. A testing plan or an analysis plan involves the accumulation of data samples, determining which statistic is to be considered and laying out the sample size.
All these factors are very important while one is working on hypothesis testing.
As soon as a testing plan is ready, it is time to move on to the analysis part. Analysis of data samples involves configuring statistical values of samples, drawing them together, and deriving a pattern out of these samples.
While analyzing the data samples, a researcher needs to determine a set of things -
Significance Level - The level of significance in hypothesis testing indicates if a statistical result could have significance if the null hypothesis stands to be true.
Testing Method - The testing method involves a type of sampling-distribution and a test statistic that leads to hypothesis testing. There are a number of testing methods that can assist in the analysis of data samples.
Test statistic - Test statistic is a numerical summary of a data set that can be used to perform hypothesis testing.
P-value - The P-value interpretation is the probability of finding a sample statistic to be as extreme as the test statistic, indicating the plausibility of the null hypothesis.
The analysis of data samples leads to the inference of results that establishes whether the alternative hypothesis stands true or not. When the P-value is less than the significance level, the null hypothesis is rejected and the alternative hypothesis turns out to be plausible.
As we have already looked into different aspects of hypothesis testing, we shall now look into the different methods of hypothesis testing. All in all, there are 2 most common types of hypothesis testing methods. They are as follows -
The frequentist hypothesis or the traditional approach to hypothesis testing is a hypothesis testing method that aims on making assumptions by considering current data.
The supposed truths and assumptions are based on the current data and a set of 2 hypotheses are formulated. A very popular subtype of the frequentist approach is the Null Hypothesis Significance Testing (NHST).
The NHST approach (involving the null and alternative hypothesis) has been one of the most sought-after methods of hypothesis testing in the field of statistics ever since its inception in the mid-1950s.
A much unconventional and modern method of hypothesis testing, the Bayesian Hypothesis Testing claims to test a particular hypothesis in accordance with the past data samples, known as prior probability, and current data that lead to the plausibility of a hypothesis.
The result obtained indicates the posterior probability of the hypothesis. In this method, the researcher relies on ‘prior probability and posterior probability’ to conduct hypothesis testing on hand.
On the basis of this prior probability, the Bayesian approach tests a hypothesis to be true or false. The Bayes factor, a major component of this method, indicates the likelihood ratio among the null hypothesis and the alternative hypothesis.
The Bayes factor is the indicator of the plausibility of either of the two hypotheses that are established for hypothesis testing.
(Also read - Introduction to Bayesian Statistics )
To conclude, hypothesis testing, a way to verify the plausibility of a supposed assumption can be done through different methods - the Bayesian approach or the Frequentist approach.
Although the Bayesian approach relies on the prior probability of data samples, the frequentist approach assumes without a probability. A number of elements involved in hypothesis testing are - significance level, p-level, test statistic, and method of hypothesis testing.
(Also read: Introduction to probability distributions )
A significant way to determine whether a hypothesis stands true or not is to verify the data samples and identify the plausible hypothesis among the null hypothesis and alternative hypothesis.
Be a part of our Instagram community
5 Factors Influencing Consumer Behavior
Elasticity of Demand and its Types
An Overview of Descriptive Analysis
What is PESTLE Analysis? Everything you need to know about it
What is Managerial Economics? Definition, Types, Nature, Principles, and Scope
5 Factors Affecting the Price Elasticity of Demand (PED)
6 Major Branches of Artificial Intelligence (AI)
Scope of Managerial Economics
Dijkstra’s Algorithm: The Shortest Path Algorithm
Different Types of Research Methods
Last updated
19 January 2023
Reviewed by
Miroslav Damyanov
Start with a broad subject matter that excites you, so your curiosity will motivate your work. Conduct a literature search to determine the range of questions already addressed and spot any holes in the existing research.
Narrow the topics that interest you and determine your research question. Rather than focusing on a hole in the research, you might choose to challenge an existing assumption, a process called problematization. You may also find yourself with a short list of questions or related topics.
Use the FINER method to determine the single problem you'll address with your research. FINER stands for:
I nteresting
You need a feasible research question, meaning that there is a way to address the question. You should find it interesting, but so should a larger audience. Rather than repeating research that others have already conducted, your research hypothesis should test something novel or unique.
The research must fall into accepted ethical parameters as defined by the government of your country and your university or college if you're an academic. You'll also need to come up with a relevant question since your research should provide a contribution to the existing research area.
This process typically narrows your shortlist down to a single problem you'd like to study and the variable you want to test. You're ready to write your hypothesis statements.
Dovetail streamlines research to help you uncover and share actionable insights
It is important to narrow your topic down to one idea before trying to write your research hypothesis. You'll only test one problem at a time. To do this, you'll write two hypotheses – a null hypothesis (H0) and an alternative hypothesis (Ha).
You'll come across many terms related to developing a research hypothesis or referring to a specific type of hypothesis. Let's take a quick look at these terms.
The term null hypothesis refers to a research hypothesis type that assumes no statistically significant relationship exists within a set of observations or data. It represents a claim that assumes that any observed relationship is due to chance. Represented as H0, the null represents the conjecture of the research.
The alternative hypothesis accompanies the null hypothesis. It states that the situation presented in the null hypothesis is false or untrue, and claims an observed effect in your test. This is typically denoted by Ha or H(n), where “n” stands for the number of alternative hypotheses. You can have more than one alternative hypothesis.
The term simple hypothesis refers to a hypothesis or theory that predicts the relationship between two variables - the independent (predictor) and the dependent (predicted).
The term complex hypothesis refers to a model – either quantitative (mathematical) or qualitative . A complex hypothesis states the surmised relationship between two or more potentially related variables.
When creating a statistical hypothesis, the directional hypothesis (the null hypothesis) states an assumption regarding one parameter of a population. Some academics call this the “one-sided” hypothesis. The alternative hypothesis indicates whether the researcher tests for a positive or negative effect by including either the greater than (">") or less than ("<") sign.
We refer to the alternative hypothesis in a statistical research question as a non-directional hypothesis. It includes the not equal ("≠") sign to show that the research tests whether or not an effect exists without specifying the effect's direction (positive or negative).
The term associative hypothesis assumes a link between two variables but stops short of stating that one variable impacts the other. Academic statistical literature asserts in this sense that correlation does not imply causation. So, although the hypothesis notes the correlation between two variables – the independent and dependent - it does not predict how the two interact.
Typically used in philosophy rather than science, researchers can't test a logical hypothesis because the technology or data set doesn't yet exist. A logical hypothesis uses logic as the basis of its assumptions.
In some cases, a logical hypothesis can become an empirical hypothesis once technology provides an opportunity for testing. Until that time, the question remains too expensive or complex to address. Note that a logical hypothesis is not a statistical hypothesis.
When we consider the opposite of a logical hypothesis, we call this an empirical or working hypothesis. This type of hypothesis considers a scientifically measurable question. A researcher can consider and test an empirical hypothesis through replicable tests, observations, and measurements.
The term statistical hypothesis refers to a test of a theory that uses representative statistical models to test relationships between variables to draw conclusions regarding a large population. This requires an existing large data set, commonly referred to as big data, or implementing a survey to obtain original statistical information to form a data set for the study.
Testing this type of hypothesis requires the use of random samples. Note that the null and alternative hypotheses are used in statistical hypothesis testing.
The term causal hypothesis refers to a research hypothesis that tests a cause-and-effect relationship. A causal hypothesis is utilized when conducting experimental or quasi-experimental research.
The term descriptive hypothesis refers to a research hypothesis used in non-experimental research, specifying an influence in the relationship between two variables.
An effective research hypothesis offers a clearly defined, specific statement, using simple wording that contains no assumptions or generalizations, and that you can test. A well-written hypothesis should predict the tested relationship and its outcome. It contains zero ambiguity and offers results you can observe and test.
The research hypothesis should address a question relevant to a research area. Overall, your research hypothesis needs the following essentials:
Hypothesis Essential #1: Specificity & Clarity
Hypothesis Essential #2: Testability (Provability)
In developing your hypothesis statements, you must pre-plan some of your statistical analysis. Once you decide on your problem to examine, determine three aspects:
the parameter you'll test
the test's direction (left-tailed, right-tailed, or non-directional)
the hypothesized parameter value
Any quantitative research includes a hypothesized parameter value of a mean, a proportion, or the difference between two proportions. Here's how to note each parameter:
Single mean (μ)
Paired means (μd)
Single proportion (p)
Difference between two independent means (μ1−μ2)
Difference between two proportions (p1−p2)
Simple linear regression slope (β)
Correlation (ρ)
Defining these parameters and determining whether you want to test the mean, proportion, or differences helps you determine the statistical tests you'll conduct to analyze your data. When writing your hypothesis, you only need to decide which parameter to test and in what overarching way.
The null research hypothesis must include everyday language, in a single sentence, stating the problem you want to solve. Write it as an if-then statement with defined variables. Write an alternative research hypothesis that states the opposite.
The following example shows the proper format and textual content of a hypothesis. It follows commonly accepted academic standards.
Null hypothesis (H0): High school students who participate in varsity sports as opposed to those who do not, fail to score higher on leadership tests than students who do not participate.
Alternative hypothesis (H1): High school students who play a varsity sport as opposed to those who do not participate in team athletics will score higher on leadership tests than students who do not participate in athletics.
The research question tests the correlation between varsity sports participation and leadership qualities expressed as a score on leadership tests. It compares the population of athletes to non-athletes.
Once you decide on the specific problem or question you want to address, you can write your research hypothesis. Use this five-step system to hone your null hypothesis and generate your alternative hypothesis.
Step 1 : Create your research question. This topic should interest and excite you; answering it provides relevant information to an industry or academic area.
Step 2 : Conduct a literature review to gather essential existing research.
Step 3 : Write a clear, strong, simply worded sentence that explains your test parameter, test direction, and hypothesized parameter.
Step 4 : Read it a few times. Have others read it and ask them what they think it means. Refine your statement accordingly until it becomes understandable to everyone. While not everyone can or will comprehend every research study conducted, any person from the general population should be able to read your hypothesis and alternative hypothesis and understand the essential question you want to answer.
Step 5 : Re-write your null hypothesis until it reads simply and understandably. Write your alternative hypothesis.
Some hypotheses are well-known, such as the Red Queen hypothesis. Choose your wording carefully, since you could become like the famed scientist Dr. Leigh Van Valen. In 1973, Dr. Van Valen proposed the Red Queen hypothesis to describe coevolutionary activity, specifically reciprocal evolutionary effects between species to explain extinction rates in the fossil record.
Essentially, Van Valen theorized that to survive, each species remains in a constant state of adaptation, evolution, and proliferation, and constantly competes for survival alongside other species doing the same. Only by doing this can a species avoid extinction. Van Valen took the hypothesis title from the Lewis Carroll book, "Through the Looking Glass," which contains a key character named the Red Queen who explains to Alice that for all of her running, she's merely running in place.
In conclusion, once you write your null hypothesis (H0) and an alternative hypothesis (Ha), you’ve essentially authored the elevator pitch of your research. These two one-sentence statements describe your topic in simple, understandable terms that both professionals and laymen can understand. They provide the starting point of your research project.
Do you want to discover previous research faster?
Do you share your research findings with others?
Do you analyze research data?
Start for free today, add your research, and get to key insights faster
Last updated: 18 April 2023
Last updated: 27 February 2023
Last updated: 6 February 2023
Last updated: 6 October 2023
Last updated: 5 February 2023
Last updated: 16 April 2023
Last updated: 9 March 2023
Last updated: 12 December 2023
Last updated: 11 March 2024
Last updated: 4 July 2024
Last updated: 6 March 2024
Last updated: 5 March 2024
Last updated: 13 May 2024
Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next.
Users report unexpectedly high data usage, especially during streaming sessions.
Users find it hard to navigate from the home page to relevant playlists in the app.
It would be great to have a sleep timer feature, especially for bedtime listening.
I need better filters to find the songs or artists I’m looking for.
Get started for free
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Humanities and Social Sciences Communications volume 11 , Article number: 960 ( 2024 ) Cite this article
167 Accesses
1 Altmetric
Metrics details
The study of happiness in economics has started to gain considerable momentum. Social policy factors are currently being recognized as determinants of national competitiveness, while innovation is an important factor to ensure economic growth and societal well-being. In order to shed light on the complex relationship between innovation performance and societal happiness, an examination was conducted in 130 countries that covered observations from 2011 to 2022. The analysis aims to uncover the degree to which these two dimensions are interconnected and to discern whether one may be identified as the causal factor of the other. The results derived from the SGMM regressions reveal that spaces characterized by elevated levels of innovation also tend to exhibit correspondingly higher indicators of resident happiness. Notably, this relationship is particularly pronounced in countries with observed real income per capita. Consequently, this study supports the hypothesis that innovation fosters improvements in resident well-being, despite ongoing debates. In light of these results, understanding the positive association between innovation and happiness has significant policy implications for fostering economic growth and enhancing quality of life on a national scale.
Introduction.
The significance of happiness within the larger framework of economic and social advancement has recently attracted substantial attention. Through an economic lens, happiness transcends conventional economic indicators by focusing on individuals’ subjective experiences and well-being. Economists have progressively acknowledged that conventional economic metrics, such as the gross domestic product (GDP), fail to adequately represent the complex range of individual’s experiences and quality of life. Consequently, their focus has shifted toward investigating happiness as an important measure of societal progress and individual well-being. On the contrary, innovation remains a deeply embedded concept within the academic discourse of economics. It has undergone a comprehensive examination as a pivotal catalyst for economic growth, development, and competitiveness. Scholars have explored various dimensions of innovation, including its determinants, effects, and implications for businesses, industries, and entire economies.
In recent years, there has been a growing interest in policies that consider human happiness and innovation performance as important factors in assessing and improving national competitiveness. Some countries, such as the United Arab Emirates, have appointed ministers of happiness or well-being to focus on improving citizens’ quality of life. These positions often work on policies related to education, healthcare, community development, and work-life balance. Happy citizens are more productive (Cabanas and Illouz 2019 ), and increases in productivity improve national competitiveness (O’Mahony and Van Ark 2003 ).
However, the relationship between happiness and innovation has recently attracted increased research attention among economists. To date, investigations into causality and effects have yielded inconsistent findings, necessitating further empirical exploration and theoretical development. Prior scholarly inquiry has predominantly examined either the influence of innovation performance on happiness (e.g., Huntsinger and Raoul 2019 ; Su and Muhammad 2023 ) or the reciprocal influence of happiness levels on innovation (e.g., Ji and Wang 2022 ; Li and Shen 2022 ; Wang et al. 2017 ; Bani-Melhem et al. 2018 ). In particular, these studies have been conducted in a single national context. Cross-national analyses investigating the correlation between happiness and innovation on a national scale remain conspicuously absent within the existing scholarly literature.
To address this research gap, this study investigates the causal relationship between happiness and innovation in 130 countries over the period 2011–2022. Our examination involves the assessment of national-level happiness levels according to the World Happiness Report (United Nations) (Helliwell et al. 2023 ) and the evaluation of innovation performance using the innovation score from the Global Innovation Index.
Our findings show that the most supported hypothesis regarding causality is that innovation serves as a driver of happiness. Countries exhibiting greater innovation tendencies also tend to exhibit correspondingly higher values on the happiness indicator. Although innovation processes inherently have the potential to benefit all nations and economies that, in subsequent years, adopt the outcomes derived from innovation, there exists a significant statistical correlation at play here, which warrants careful consideration.
The paper unfolds as follows. Section Theoretical background provides the conceptual groundwork and offers a synthesis of the outcomes from pivotal research studies pertinent to our pursuit. Section Methodology describes the dataset and delineates the methodology employed. Subsequently, Results and Discussion showcase the empirical findings and engage in discourse vis-à-vis earlier studies. The paper ends with concluding remarks and policy recommendations.
Over the past decades, human happiness has emerged as a prominent subject in the literature (Kahneman and Krueger 2006 ; Clark et al. 2008 ). Social scientists have sought to understand the determinants and associations of happiness, as well as its implications for a country’s economic and social realms. The substantial expansion in both scope and rigor of this literature has contributed to the establishment of an emerging field known as the ‘economics of happiness’ (Easterlin 2004 ). Within the economics literature, happiness is equated with subjective well-being (Engelbrecht 2015 ).
Recognizing the complexity and significance of happiness for humanity, Layard ( 2005 ) emphasized the imperative for an ‘academic revolution,’ urging all social researchers to strive for a deeper understanding of the determinants of happiness. Broadly speaking, happiness has been linked to economic growth, economic freedom, institutional quality, cultural factors, social support, a healthy living environment, and physical well-being (Voukelatou et al. 2021 ; Veenhoven 2012 ).
Intuitively, economic growth has been posited to positively impact happiness (Aldieri et al. 2019 ). Empirical evidence supporting the notion that economic growth enhances happiness has also been presented (Hagerty and Veenhoven 2003 ; Inglehart 2017 ). Recent rigorous studies have demonstrated that happiness co-varies with several macroeconomic variables, including GDP, GDP growth, income, and unemployment (Aldieri et al. 2021 ; Di Tella et al. 2003 ; Helliwell 2003 ; Alesina et al. 2004 ). However, the positive effect of economic growth on happiness appears to plateau beyond a certain threshold. Furthermore, the Easterlin paradox (Easterlin 1974 , 1995 ) has established that, at the national level, average happiness remains relatively constant over time despite substantial increases in GNP per capita. Similarly, Dwyer ( 2020 ) documented the disconnect between GDP and self-reported life satisfaction levels.
The economic development of a nation has been unequivocally intertwined with numerous psychological shifts that impact human happiness. Economic growth triggers a shift in societal priorities, transitioning from the pursuit of maximum wealth to the pursuit of maximum well-being (Inglehart 2017 ; Pugno 2019 ). Happiness is related to pro-social values and behaviors, fostering trust and exhibiting a robust correlation with social participation and social capital (Layous et al. 2017 ; Guven 2011 ).
Within the scholarly discourse, there exists a consensus that economic development finds its base in innovation (Reznakova and Stefankova 2022 ; Fuertes-Callen and Cuellar-Fernandez 2019 ; Mourao and Popescu 2023 ; Popescu et al. 2023 ). Innovation has claimed a pivotal role in economic growth models, notably through the seminal contributions of Schumpeter ( 1934 ), whose ideas laid the groundwork for subsequent economic growth paradigms. Innovation performance acts as a catalyst for enhanced productivity growth, while simultaneously fostering improvements across various macroeconomic dimensions (Huong et al. 2021 ).
The nexus between innovation and happiness remains shrouded in ambiguity with respect to its effects, direction, and magnitude. As posited by Dolan and Metcalfe ( 2012 ), the available evidence on the link between happiness and innovation is exceedingly scarce. While the prevailing body of research acknowledges a positive correlation between innovation and happiness (e.g. Derclaye 2014 ), a cohort of scholars have acknowledged that innovation-driven growth could also be implicated in unemployment and inequalities, which could potentially impinge on happiness and well-being (Vivarelli 2014 ; Brynjolfsson and McAfee 2014 ).
Additionally, the direction of the relationship between innovation and happiness remains a subject of ongoing debate. Previous research has examined the impact of innovation performance on human happiness (e.g., Huntsinger and Raoul 2019 ; Su and Muhammad 2023 ) or the influence of happiness levels on innovation performance (e.g., Ji and Wang 2022 ; Li and Shen 2022 ; Wang et al. 2017 ; Bani-Melhem et al. 2018 ).
On the one hand, earlier studies have verified that innovation performance can indeed impact human happiness , although it is only one of several factors influencing overall happiness levels within a country. It is imperative to acknowledge, from the outset, that innovation embodies a dual nature, as underscored by Schubert ( 2012 ). Concerns about the ramifications of technological innovation (e.g., automation, digitization) on work are pervasive within economic literature and society. A spectrum of effects, both directly positive (e.g., creation of higher-quality jobs, enhanced worker skills, increased job satisfaction) and negative (e.g., heightened uncertainty, anxiety, devaluation of human capital, displacement, status erosion), have been proposed. In exploring these trade-offs, several scholars have emphasized that innovation can lead to imbalanced wealth distribution, potentially reducing employment, and increasing inequality, two factors intrinsically linked to determining happiness (Vivarelli 2014 ; Brynjolfsson and McAfee 2014 ).
However, given innovation’s pivotal role in economic development and societal advancement, the indirect positive effects of innovation on societal-level happiness have garnered substantial recognition (Castellacci 2022 ). For example, Engelbrecht ( 2015 ) formulated a comprehensive model outlining the nexus between innovation and happiness (subjective well-being). This model encompasses multiple dimensions, including the workplace and labor market, the product market, the material standard of living, objective well-being indicators (e.g., health, education, social indicators), and the natural environment. Spaces characterized by robust innovation performance tend to experience heightened economic growth rates and increased employment opportunities (Nelson 1996 ). This, in turn, leads to better living standards, better access to essential necessities, and increased disposable income (Aldieri et al. 2019 ), all of which can positively impact overall happiness. In the context of product markets, innovation drives the development of new and enhanced products and services, such as advanced healthcare technologies, improved infrastructure, and more efficient transportation systems (Barrett et al. 2015 ). These advancements in consumption can improve the quality of life and well-being, contributing to higher levels of happiness (Veenhoven et al. 2021 ). On the contrary, the purchase of financial products does not necessarily have a positive effect on happiness.
Dolan et al. ( 2008 ) contend that innovation catalyzes social and cultural advancement. Innovations spanning education, art, and culture can enrich societies, fostering a more vibrant and fulfilling way of life. Such steps have the potential to reinforce a sense of community and belonging, thus contributing to overall happiness levels. Innovation can precipitate technological breakthroughs that make everyday life more convenient and pleasant. For instance, advancements in communication technology have facilitated easier connections among individuals, cultivating social bonds and, consequently, augmenting happiness. In the realm of environmental sustainability, innovative solutions have the promise of addressing environmental predicaments, thereby nurturing a healthier and cleaner environment. This, in turn, can produce improved health outcomes and an increased sense of well-being for both individuals and communities (Aldieri et al. 2019 ). Even at the individual level, creative pursuits have been correlated with elevated personal competence and individual growth, fundamental components of a profound sense of well-being (Ryan et al. 2008 ). Elevated innovation performance can engender augmented personal competence, thus constituting a foundation for happiness (McManus and Carvalho 2022 ).
In an exploration of the effects of innovation on happiness and social welfare, Castellacci ( 2022 ) identifies four spheres of life significantly impacted by innovation: (i) the realm of work, (ii) consumption, (iii) leisure and personal life, and (iv) capabilities and functionings. Specifically: (i) Regarding the sphere of work, innovation influences individual happiness through various avenues: wage level (absolute income), relative income and social comparisons, and the quality of the workplace environment. (ii) Consumption implies increased utility and satisfaction for individuals. Innovation increases happiness by affecting consumption dynamics, ensuring preference fulfillment, time savings, and information provision. (iii) The domains of leisure and personal life experience innovation’s effects through aspects like social interaction and communication, physical environment enhancements (e.g., cleaner production), and the socio-institutional milieu. (iv) Finally, innovation impacts happiness by improving capabilities and functionings, achieved through improved education and health systems.
Recent empirical studies have produced conflicting results. Su and Muhammad ( 2023 ) documented a positive impact of innovation on the level of human happiness within Chinese society. They devised the metric ‘Innovation and development’ and estimated its influence on the dependent variable ‘Life satisfaction and happiness’. Using patents as an estimator of innovation, Derclaye ( 2014 ) found a robust correlation between innovation and happiness in developed countries, although without measuring specific effects. On the contrary, Aldieri et al. ( 2021 ) noted that innovation had a negative impact on subjective well-being in eight Western European countries during the period 1980–2014. This adverse effect was primarily ascribed to inequality, as a positive correlation between income and happiness was observed in the investigated nations.
The relationship between innovation and happiness at the country level has also recently been explored in conjunction with several other variables. Among these variables, one can discern: GDP, unemployment, inequality gauged by the Gini index (Aldieri et al. 2021 ), GDP per capita, consumer price index (CPI), taxes as a proportion of GDP, unemployment rate (Derclaye 2014 ), governmental control, and the degree of behavioral social constraints (Chua et al. 2019 ), freedom to make life choices, GDP growth, social contribution, employment rate, social support, life expectancy, coverage of social safety nets and high qualification (Su and Muhammad 2023 ).
On the other hand, an alternate body of research has found evidence suggesting that happiness contributes to increased innovation performance , implying that happier environments are more conducive to innovation. Happiness nurtures innovation capital and entrepreneurial initiative (Usai et al. 2020 ), while contented individuals are more likely to display traits of creativity, innovation, and productivity (Isen 2008 ; Diener 2012 ), as well as increased self-control, optimism, trust, and sociability (Huntsinger and Raoul 2019 ).
In the context of industrial England, Huntsinger and Raoul ( 2019 ) posited that higher living standards led to increased happiness, which in turn spurred a surge in innovation and economic growth. At the city level, elevated happiness stimulates investment behavior and research and development (R&D) efforts of local enterprises, particularly those of younger firms and R&D investments (Chuluun and Graham 2016 ; Kamguia et al. 2023 ). Furthermore, the role of local happiness is amplified in regions where happiness is more evenly distributed. Given its link to increased trust and social capital (Layous et al. 2017 ; Guven 2011 ), happiness stimulates social networking undertakings that facilitate the acquisition for innovation. At the regional level, happiness was also identified to play an important role in corporate green innovation (Li and Shen 2022 ). Using the quality of the local environment as a proxy for happiness, Ji and Wang ( 2022 ) provided evidence that happiness contributes positively and significantly to the efficiency of high-tech industries.
Exploring the potential pathways through which happiness might enhance innovation performance, we have identified prior research that underscores happiness’s influence on individuals’ motivation, creativity, health, well-being, collaboration, social cohesion, education, learning, and entrepreneurial initiative (e.g., Usai et al. 2020 ), as follows. Happy individuals are more prone to being motivated, productive, and inventive, as has been previously suggested (e.g., Ceci and Kumar 2016 ; Armenta et al. 2020 ; Rego et al. 2009 ). When individuals possess a sense of life satisfaction and an optimistic perspective, they are more inclined to engage in innovative thinking and problem-solving, producing fresh ideas, inventions, and advancements in diverse industries. Furthermore, happiness is closely related to physical and mental well-being (Trabelsi 2023 ). When people enjoy good health and well-being, they are more likely to actively participate in both the workforce and society. Strong social bonds and a sense of community enhance the propensity for collaboration, knowledge sharing, and collective involvement in innovative projects. Individuals who are both happy and healthy are better positioned to contribute to innovative and research-oriented behaviors, while a positive and unified social environment can foster an innovative culture (Mutonyi et al. 2020 ; Espasandín-Bustelo et al. 2021 ). In addition, happy and fulfilled individuals are more predisposed to value education and continuous learning. Education plays an essential role in nurturing innovation, as it equips individuals with the knowledge and skills requisite to contribute to scientific, technological, and cultural progress. Happiness can exert an influence on individuals’ inclination to explore entrepreneurial ventures (Bao and Dou 2021 ; Sweida and Sherman 2020 ). Consequently, individuals who experience happiness may exhibit greater openness to exploring novel opportunities and ideas, thereby driving innovation across business and technology domains.
Overall, the theoretical framework presented offers a comprehensive overview of the literature at the intersection of happiness and innovation. Three main theoretical strands regarding the relationship between happiness and innovation are integrated into this study’s conceptual framework. First, happiness as subjective well-being has been acknowledged within the economics literature. The perspective of subjective well-being centers on individuals’ subjective perceptions and assessments of their own lives. Previous studies within this domain have explored dimensions such as life satisfaction, happiness, and overall well-being, confirming the reliability and validity of these subjective measures as reliable indicators (Diener et al. 2002 ; Frey and Stutzer 2009 ; Veenhoven 2012 ). Second, innovation has been seen as a catalyst for economic growth, echoing the seminal contributions of Schumpeter ( 1934 ). It emphasizes how innovation acts as a catalyst for enhanced productivity growth and improvements in various macroeconomic dimensions. This perspective underscores the importance of innovation in fostering economic progress and lays the groundwork for exploring its implications for happiness. Third, the economic growth theory explores the factors and mechanisms that drive sustained growth with prioritization of well-being. Along these lines, this study considers happiness as a crucial social goal alongside economic prosperity.
On examination of recent pertinent research, we found contradictory findings in empirical studies exploring the relationship between happiness and innovation. The disparity in the findings arises from the application of varying measurements, analytical scales, and national contexts across previous studies. Furthermore, happiness is frequently assessed as a variable with subjective attributes, and emotional states tend to exhibit transience. This temporal aspect can lead to inconsistencies in happiness assessment over time, even when utilizing a consistent methodology. Common metrics employed to measure happiness include the Cantril ladder derived from the World Happiness Index (Ionescu-Feleagă et al. 2022 ; Carlsen 2018 ; Araújo et al. 2022 ), happiness indicators sourced from alternative references (e.g., European Social Survey, European Value Survey, Chinese General Social Survey (CGSS), and China Family Panel Studies (CFPS) surveys) (Plepytė-Davidavičienė 2020 ; Wang et al. 2022a ; Derclaye 2014 ), and even author-constructed happiness indices for both happiness and subjective well-being (Ross et al. 2005 ; Li and Shen 2022 ), along with life satisfaction measurements (Diener and Diener 1995 ). Less frequently employed happiness metrics encompass affect balance (Tsurumi et al. 2018 ; Yoon et al. 2022 ), quality of the living environment (Ji and Wang 2022 ), people’s livelihood, happiness, and shared development (Wang et al. 2022b ). In contrast, diverse metrics have been used for innovation, with the most commonplace including research and development (R&D) expenditure, patent counts, and innovation indices computed by international organizations (Popescu 2020 ).
Consequently, earlier studies addressing the direction of the relationship between happiness and innovation remain inconclusive, highlighting the idea that the connection between human happiness and a country’s innovation performance is complex and multifaceted, thus requiring further exploration. As recently highlighted by Aldieri et al. ( 2021 , p. 1300), a research gap persists in the study of the relationship between innovation and happiness.
This study aims to bridge this gap by undertaking a series of estimations encompassing both directions of potential dependency, as derived from previous academic research. The primary objective is to determine the directional interdependence of the relationship between happiness and innovation performance on a country level, along with its associated effects. Therefore, this study aims to address the following research question: ‘ Do happier environments breed greater innovation or do more innovative environments tend to be happier?’
For happiness assessment, we have chosen to utilize the World Happiness Index (WHI), sourced from the World Happiness Reports (United Nations) (Helliwell et al. 2023 ), covering all available years (2011–2022). The World Happiness Report (WHR) constitutes an openly accessible online publication under the aegis of the United Nations’ global initiative, the Sustainable Development Solutions Network, predominantly drawing data from the Gallup World Poll. Currently, the WHR includes 156 countries and is progressively receiving acknowledgment from international entities and governments as a framework for shaping policies by employing happiness indicators (Stiglitz et al. 2018 ; Jaswal et al. 2020 ). The WHI measures the degree of happiness and contentment among a nation’s populace through the application of the Cantril ladder, a scale ranging from 0 (representing the lowest value) to 10 (indicating the highest value), with respondents utilizing it to assess their contentment across diverse facets of life. Several studies have agreed on the reliability of WHI, further confirming its suitability in previous research studies (Ionescu-Feleagă et al. 2022 ; Carlsen 2018 ; Araújo et al. 2022 ). The principal benefits inherent in employing this WHI encompass the canvassing of nationally representative samples, the use of weighting mechanisms to render estimates representative of the population, and the cross-national comparability of the data. To quantify innovation performance on a country level, we will opt, in this work, to exhibit the outcomes considering the Global Innovation Index, as the variable related to innovation. The rationale is that this variable encompasses other indicators for innovation (namely, Research & Development as a percentage of GDP, information technology exports as a percentage of total goods exports, and High-tech exports as percentage of manufactured exports, among others). Data for GDP per capita, in current US dollars, were collected from The World Bank (World Bank 2024 ).
Our database comprises a maximum of 1560 observations conducted across 130 countries spanning the period from 2011 to 2022. Descriptive statistics for our dataset are outlined in Table 1 .
The discussion surrounding the concept of causality, as well as the empirical tools to scrutinize hypotheses such as ‘cause X precedes effect Y ,’ is a longstanding and complex discourse within economics and other social sciences.
We will begin by offering a concise overview of the different models under examination, followed by a summary of empirical investigations based on the inherent nature of the data.
The term ‘cause’ involves one dimension preceding another dimension, called ‘effect.’ However, it is crucial for there to exist a substantiated rationale within the literature supporting this directional linkage. The mere observation that alterations in variable X , for instance, appear to precede alterations in variable Y is inadequate to establish a causal relationship, as this observation could be deemed ’spurious.’ In addition to this, temporal consistency must be maintained. The relationship established between X (the cause) and Y (the effect) should not depend on the specific time frame being considered. If the causal link between X and Y is only valid during certain periods, it is plausible that seasonal influences or longer-term cycles could contribute to this phenomenon. Beyond temporal consistency, the examination of individual consistency is essential. This involves assessing whether the influence on Y holds true for most of the observed individuals. It should be noted that, as we explore further, particularly during our discussion of cointegrated panel data, several tests analyze the adequacy of X being the cause of Y for a minimal number of individuals, positing it as a hypothesis of causality.
Finally, X is indeed the cause of Y when the impact of X persists over Y in the same direction within an observed trajectory. If the elevation leads to a corresponding increase in Y , this incremental effect tends to last for a certain duration. If, however, the consequence of the elevation on Y is inconsistent, such as an increment in one period followed by a decrement in the immediate subsequent period, then the observed periodicity becomes a critical determinant. In this scenario, we might be confronted with either a spurious relationship or cyclical causality effects, albeit with a periodicity different from the one being studied.
The discussion of causality can also be categorized based on the type of data under observation. Primarily, within time-series analysis, the concept of ‘Granger’ causality constitutes a pivotal element in the current scientific discourse. There is a substantial body of literature on this topic (Maziarz 2015 ; Radu 2013 ). It is noteworthy to acknowledge that the advent of ‘Granger’ causality triggered a profound revolution within the realm of time-series econometrics (Zaman 2008 ). In the context of ‘Granger’ causality tests for time-series data, we recommend referring to Grosche ( 2014 ).
Subsequently, this concept of causality was extended to dynamic panel data. Works such as those authored by Lopez and Weber ( 2017 ), Lu et al. ( 2017 ), or Juodis and Karavias ( 2019 ) contribute to this discourse. The fundamental premise lies in the notion that lagged observations of X (such as X t-1 , X t-2 , …, X t-n ) elucidate current observations of Y t . In addition, this causality framework has been expanded to include a broader array of individuals under observation. While time-series observations often focus on a single entity (e.g., a country) across years, panel data facilitate observations of multiple entities over diverse periods. For testing the causality of Y over X in panel data, techniques such as joint significance tests of the estimated coefficients for time lags of Y within the estimated regressions are employed, among other methodologies.
When the temporal horizon is extensive and a multitude of individuals are observed, the concept of cointegrated panel data arises (Herzer and Strulik 2017 ; Jalles 2015 ; Law et al. 2014 ). This approach seeks confirmation not only of the existence of a long-term relationship but also of estimated significant relationships between the dependent variable (effect variable) and the independent variables (cause variables) in the short term. Tests such as Kao ( 1999 ), Pedroni ( 1999 , 2004 ), and Westerlund ( 2005 ) are employed in this context.
The Kao ( 1999 ) test presupposes a cointegration vector uniform across all panels, estimating means for each panel (as fixed effects) and precludes the inclusion of a time trend. The alternative hypothesis in this test posits that the series exhibits cointegration across all panels with the same cointegration vector. In contrast, the Pedroni test (1999, 2004) diverges from the Kao test in two aspects: It assumes distinct cointegration vectors and specific autocorrelation terms for each panel. Consequently, the alternative hypothesis posits cointegration across all panels with panel-specific cointegration vectors. The Westerlund test (Westerlund 2005 ) encompasses a statistical examination of the variance ratio, obtained through testing for the existence of a unit root within the estimated residuals of the Dickey-Fuller regression. This test assumes uniform autocorrelation terms for all panels. The alternative hypothesis in this case stipulates cointegration across all panels.
At this juncture, it is vital to acknowledge that both dimensions X and Y in our study, happiness, and innovation, might be influenced by a shared ‘hidden/latent’ dimension, each deriving positive influences from this latent dimension. For example, we can posit that robust societal institutions (e.g., efficient courts, low crime rates, and equitable income distribution) foster happy residents and a conducive environment for innovation. However, even in such a scenario, one of the dimensions under scrutiny (innovation or happiness) could preclude the absorption of certain positive influences from this shared source, channeling these forces toward the other dimension. This generates causal processes between the initially affected and the resultant effects observed in the receiving dimension.
In empirical terms, due to the lack of consensus in the literature on whether ‘happier’ spaces are more innovative or if more innovative spaces are ‘happier’, we conducted a series of estimates. The study takes two main approaches to explore this relationship. First, using dynamic panel data analysis, it investigates how changes in happiness levels, measured by the Happiness indicator from the World Happiness Report, influence innovation outcomes. The Global Innovation Index serves as the dependent variable in this analysis. Second, in response to criticisms about the consistency of results when happiness is treated as an independent variable, the study reverses the analysis. It examines how levels of innovation, as measured by the Global Innovation Index, affect happiness levels, using the same Happiness indicator as the dependent variable. This dual approach aims to provide a complete understanding of how happiness and innovation interact, addressing methodological challenges such as omitted variable bias through robust panel data techniques.
As we built a dynamic panel data, we chose to highlight throughout this section the results of the estimations carried out using the System-GMM (Roodman 2009 ), without prejudice to being able to show the estimations made with fixed effects, random effects or Pooled-OLS that turned out to be of lower statistical quality. Thus, Table 2 shows the results of the estimation that considered the Innovation indicator as the dependent variable and the Happiness indicator as the main independent variable. We also emphasize that we used several control variables whose estimated coefficients and respective significance values will be shown if requested. Finally, even regarding the issue of omitted variables, we based our rationale on Bond ( 2002 ), who clearly stated that dynamic panel data models are better able to address the omitted variable problems and endogeneity issues than static models.
Table 2 also shows the p -values of the Arellano-Bond tests for autocorrelation in first differences as well as the Sargan and Hansen tests for overidentified restrictions. Difference-in-Hansen tests of exogeneity values are available for consultation upon request. However, the various tests did not identify problems that would prevent an inference from the results in Table 2 .
The results in Table 2 show that there is a long persistence of the values observed for Innovation. The estimated coefficient for the Innovation lag (0.709) reveals that countries with higher values in a given year tend to maintain high values in the following year (Costa and Tashakori 2023 ; Bianchini and Pellegrino 2019 ). A country having a one-point higher innovation score will tend to have for the next year an expected increase of 0.709 in the same variable. The effect of happiness is positive for the year under observation – higher values of happiness in a given year in a country are associated with higher values of the Innovation indicator in that country and in that year (estimated coefficient of 4.242, statistically significant at 5%). Once again, we interpret the estimated coefficient as follows: A country happier than another in 1 point will tend to have an expected score of innovation increased by 4.242 points. However, the effect arising from the happiness first lag on Innovation is negative (statistically significant at 10%). Therefore, innovation increases in ‘less happy’ countries in the previous year and in ‘happier’ countries in the same year.
This type of result should be seen in the face of criticism in Baraldi et al. ( 2013 ) or Musick and Meier ( 2012 ). Cycles of influence between two variables (related in non-spurious terms) must be consistent. This implies that if the variable X is the cause of the variable Y, which generates a contemporary positive effect on Y, then the same positive effect should be noticed in the estimated coefficients for the first lags observed in X in the regression that has Y as the dependent variable.
In view of this criticism of the inconsistency observed in the estimated results of the first lags of happiness over Innovation, we estimate the reverse equation. Thus, we also estimated Innovation as an explanatory variable for happiness (now the dependent variable). These results are in Table 3 .
The results in Table 3 show another level of consistency. In fact, we observe in Table 3 the following: (i) There is persistence in the observations related to happiness. The effect arising from the variable observed in the previous year is positive and statistically significant; (ii) There is consistency in the (positive) effect arising from Innovation on Happiness. Countries with higher values in the Innovation indicator are countries with higher values on the Happiness indicator. This effect is contemporary but also comes from the previous period.
Therefore, we favor the hypothesis that “Innovation causes happiness’ in our study. Therefore, more innovative countries tend to be associated with higher values for the Happiness indicator as well. Although innovation processes are processes that, ultimately, will favor all countries and all economies that, in the following years, adopt the objects resulting from innovation, there is a statistical association here that cannot be neglected.
We aimed to further investigate this empirical evidence. To achieve this, we used a partitioning approach within our shared sample. Specifically, we divided the sample into two segments based on whether the observations fell below or above the median value. This was applied to both the GDP per capita variable and the Gini coefficient variable. This strategy follows empirical studies like Plepytė-Davidavičienė ( 2020 ) or Schumpeter ( 1934 ). The rationale is that economies with higher levels of real GDP per capita tend to exhibit particular patterns of happiness metrics and innovation scores. Related to the Gini indicator, a popular construction for debating income inequality, it is expected that populations with higher levels of economic inequality will tend to express particular responses in terms of assumed happiness. Table 4 presents the results corresponding to observations below and above the median values for GDP per capita.
The results in Table 4 show the following: (i) the persistence of the variable related to Happiness is greater in countries and periods with higher real GDP per capita; (ii) in a way that raises several challenges to further investigation, we found that the effects arising from Innovation are not associated with estimated coefficients that are statistically significant in countries with lower real GDP per capita; (iii) On the other hand, in countries and periods with higher values of real GDP per capita, the positive effect of Innovation tends to only be noticed contemporaneously.
Thus, when synthetically interpreting the potential implications of Table 3 , we discern that Innovation primarily impacts Happiness in instances characterized by higher real GDP per capita. This observation introduces additional challenges for further investigation, prompting exploration of the mechanisms that operate within contexts of elevated real GDP per capita, as well as those that may not be as streamlined in scenarios falling below the median of real GDP per capita.
Finally, Table 5 allows for additional interpretations, which generally converge with the interpretations in Table 3 . The separation of our database considering the pattern of income inequality, measured by the Gini coefficient, shows that Innovation values have a positive contemporary effect on Happiness. However, by performing a coefficient equality test, we do not reject the null hypothesis that the estimated coefficient for Innovation in countries with less inequality is equivalent to the estimated coefficient for Innovation in cases of greater inequality. This evidence can be further supported by the argument that the variability observed in the database in the Gini coefficient variable is relatively small (for example, in comparison).
The results derived from our analysis on both directions of dependence within the relationship between happiness and innovation offer valuable insights for discussion. As our review of the literature affirms, there remains a lack of consensus regarding the correlation between innovation and happiness (Dolan and Metcalfe 2012 ). The dimensions identified in the literature as contributors to community or country-level happiness are multifaceted. Parameters such as per capita income, low unemployment, minimized income inequality or access to quality healthcare have been recognized as pertinent aspects. However, the quality of innovation tied to a particular region or space should not be disregarded (Mourao and Popescu 2023 ). On the one hand, regions marked by higher innovation levels tend to reap the benefits of early exposure to externalities generated by innovation. These regions may enjoy improved healthcare access due to increased innovation in pharmaceuticals, for example, or they may access more efficient production methods by incorporating innovative inputs.
Working with panel data (consisting of 1560 observations across 130 countries for the period 2011–2022), our approach to causality analysis diverged from that employed for time-series data - as seen in works like Lopez and Weber ( 2017 ), Lu et al. ( 2017 ), or Juodis and Karavias ( 2019 ). Our methodology necessitated not only assessing the statistical significance of contemporaneous estimates of the primary independent variable, but also evaluating the significance of estimates from lagged observations of the same variable. Furthermore, to establish the causality direction, we examined the persistence of the causal effect, scrutinizing whether estimates for the independent variable consistently aligned in the same direction across different time points.
Previous research shows that performance on global competitiveness pillars of innovation and institutions is positively relate to performances on happiness and life satisfaction (Canatay et al. 2023 ). Based on our observations, we draw the conclusion that the hypothesis most substantiated in terms of causality is that innovation acts as a catalyst for the well-being of surrounding communities (in line with previous results obtained by Su and Muhammad 2023 ). We have systematically examined this hypothesis by scrutinizing subsamples. These subsamples include evaluations of real GDP per capita and the Gini index. Utilizing these subsamples has allowed us to deduce that this relationship particularly comes to the forefront in observations marked by higher real income per capita. This finding highlights the existence of persistent barriers within regions with lower real per capita income, which hinder the full extension of innovation’s positive impacts on the presumed level of happiness. Addressing the effectiveness of these barriers poses a significant challenge, important for enhancing the potential of innovation to improve the well-being of the communities involved.
The findings of this study are in line with those of Dolan et al. ( 2008 ), who argued that innovation acts as a catalyst for social and cultural progress. Innovations have the potential to enrich societies and promote a more dynamic and fulfilling quality of life. While research findings support the hypothesis that innovation causes happiness, it is important to acknowledge the divergent perspectives highlighted in previous studies and explore possible reasons for this divergence. For example, Aldieri et al. ( 2021 ) noted a negative impact of innovation on subjective well-being in certain Western European countries, primarily attributed to issues of inequality. In the context of industrial England, Huntsinger and Raoul ( 2019 ) suggested that higher living standards are associated with greater happiness, thereby fostering a surge in innovation and economic growth. It is essential to consider the contextual nuances that may underlie these divergent findings. For example, the socioeconomic conditions, cultural norms, and policy environments of the studied countries could influence the observed relationship between innovation and happiness. Furthermore, variations in the level of analysis and the research methodologies, such as sample selection criteria and measurement approaches, can contribute to differing conclusions between studies. The opposing views suggest that happiness nurtures innovation capital, fosters creativity and collaboration, and enhances individuals’ motivation and entrepreneurial initiative (Usai et al. 2020 ; Rego et al. 2009 ; Ceci and Kumar 2016 ; Armenta et al. 2020 ). This discrepancy highlights the complexity of the relationship between innovation and happiness, suggesting that contextual factors and social dynamics may influence the observed effects.
This research study analyzed the causality relationship between happiness and innovation with data collected at national level. Our findings support for the hypothesis that innovation causes happiness within our dataset for 130 countries.
Empirical evidence highlights a compelling degree of consistency. The analysis reveals the following key patterns: (i) A noticeable persistence effect within happiness observations, with a positive and statistically significant influence from the previous year; (ii) Consistency in the positive impact of innovation on happiness, showcasing a contemporary effect alongside the influence from the previous period. Consequently, countries that exhibit higher levels of innovation are closely associated with elevated levels of the Happiness indicator. Although innovation processes inherently benefit all nations and economies that adopt innovation-driven outcomes in subsequent years, the observed statistical correlation underpins the significance of this association. With the methodological resources available, we favor this hypothesis that more innovative spaces become happier spaces. Further avenues for research can use other methods such as PLS-SEM (Partial Least Squares Structural Equations Modelling) to confirm or complement this analysis, including the possibility of studying latent dimensions that intervene in the constructs measured by innovation or happiness.
We further partitioned our sample into two segments based on GDP per capita and Gini coefficient variables. Our findings show: (i) Enhanced persistence within the happiness variable across countries and periods characterized by higher real GDP per capita; (ii) a nuanced scenario where Innovation’s effects are not statistically significant in countries with lower real GDP per capita; (iii) Conversely, the positive impact of Innovation is primarily concurrent in countries and periods with elevated real GDP per capita. This interpretation underscores that Innovation’s influence on happiness predominantly resonates in observations marked by higher real GDP per capita. This observation requires further inquiries into the channels at play within these contexts, contrasting them with cases below the median of real GDP per capita. The division of our dataset based on the pattern of income inequality, measured by the Gini coefficient, shows that innovation has a positive immediate impact on happiness.
Our findings support the idea that innovation acts as a catalyst for well-being. This outcome led us to recognize that innovation is just one of several factors contributing to overall happiness. These results suggest some implications for policies. First, when designing policies to improve happiness and well-being in a comprehensive way, it is important to recognize that several policies should be aligned to foster innovation to further spur happiness. The main policy goal is to create environments and societies where individuals have the opportunity to lead fulfilling lives that go beyond mere innovation and economic success. Additionally, policymakers must develop policies that foster innovation and promote happiness to create an environment where individuals are empowered to be creative, solve problems, and contribute positively to society, through, for instance, the development of educational systems that focus not only on academic achievement but also on nurturing creativity, critical thinking, emotional intelligence, and social skills; development of flexible employment policies that benefit from technological advancements and workplace innovations; provision of funding and incentives for research and innovation in various sectors (including technology, healthcare, and sustainability); development and support of entrepreneurial ecosystems, digital infrastructure, workplace innovation, community engagement and diversity, and inclusion initiatives.
Second, the positive impacts of innovation on well-being are more pronounced in regions with higher real income per capita. Policymakers should address regional disparities by implementing targeted initiatives to support innovation in areas with lower income levels. This could involve providing incentives for companies to establish operations in these regions or investing in education and infrastructure to boost innovation capacity. Third, income inequality may play a role in hindering the full extension of innovation’s positive impacts on happiness. National policies should therefore focus on promoting inclusive growth to ensure that the benefits of innovation are shared more equitably among different segments of the population. National policies should prioritize capacity-building initiatives to improve the ability of regions, especially those with lower income levels, to take advantage of innovation. This may involve targeted training programs, infrastructure development, and other forms of support.
Efforts to integrate happiness and innovation into national policies should be made. Countries that recognize the importance of these factors are more likely to implement policies that prioritize the well-being of their citizens and foster an environment conducive to innovation. As global perspectives on success and development continue to evolve, it is possible that more nations will consider these broader indicators in shaping their policies. Several countries have shown that it is possible to pursue policies that foster innovation and promote well-being simultaneously, recognizing the interdependence between economic growth, innovation, and the quality of life of their citizens. Finland is renowned for its comprehensive approach to education and innovation while prioritizing citizen well-being. It invests significantly in education and research to foster innovation and economic growth while also focusing on social welfare programs, healthcare, and environmental sustainability to ensure the well-being of its citizens (Prime Minister’s Office, Finland 2020 ). Similarly, Danish public policies aim to support innovation through initiatives such as investment in research and development, fostering entrepreneurship, and promoting sustainable practices. At the same time, Denmark places a strong emphasis on social equality, healthcare, education, and work-life balance, contributing to the overall well-being of its population (Denmark Ministry of Foreign Affairs 2021 ).
Future research could focus on elucidating the specific mechanisms through which innovation impacts societal happiness. This could involve exploring the role of factors such as job creation, income inequality, access to education and healthcare, and social capital in mediating the relationship between innovation and well-being. Understanding these mechanisms is important to designing targeted interventions and policies aimed at maximizing the positive effects of innovation on happiness. Future research could investigate further into categorizing countries based on their development status or Human Development Index (HDI). This approach would allow a more nuanced understanding of how the relationship between innovation and happiness varies between different levels of human development. Additionally, examining how socioeconomic factors interact with innovation to influence happiness levels within each category could provide valuable information for policymakers.
The data used are publicly available, and the data sources are mentioned in the Methodology section.
Aldieri L, Bruno B, Vinci CP (2019) Does environmental innovation make us happy? An empirical investigation. Socioecon Plann Sci 67:166–172. https://doi.org/10.1016/j.seps.2018.10.008
Article Google Scholar
Aldieri L, Bruno B, Vinci CP (2021) A multi-dimensional approach to happiness and innovation. Appl Econ 53(11):1300–1310. https://doi.org/10.1080/00036846.2020.1828807
Alesina A, Di Tella R, MacCulloch RJ (2004) Inequality and happiness: are Europeans and Americans different? J Public Econ 88(9–10):2009–2042. https://doi.org/10.1016/j.jpubeco.2003.07.006
Araújo RED, Silva HAD, Salvio GMM (2022) Statistical correlation between socioeconomic indicators and protected natural areas around the world. Rev Árvore 46:e4601. https://doi.org/10.1590/1806-908820220000001
Armenta CN, Fritz MM, Walsh LC, Lyubomirsky S (2020) Satisfied yet striving: gratitude fosters life satisfaction and improvement motivation in youth. Emotion 22(5):1004–1016. https://doi.org/10.1037/emo0000896
Article PubMed Google Scholar
Bani-Melhem S, Zeffane R, Albaity M (2018) Determinants of employees’ innovative behavior. Int J Contemp Hosp Manag 30(3):1601–1620. https://doi.org/10.1108/IJCHM-02-2017-0079
Bao J, Dou J (2021) The formation of subsequent entrepreneurial intention: happiness matters. Sustainability 13(21):12323. https://doi.org/10.3390/su132112323
Baraldi AL, Cantabene C, Perani G (2013) Reverse causality in the R&D–patents relationship: an interpretation of the innovation persistence. Econ Innov N. Technol 23(3):304–326. https://doi.org/10.1080/10438599.2013.848059
Barrett M, Davidson E, Prabhu J, Vargo SL (2015) Service innovation in the digital age. MIS Q 39(1):135–154. https://www.jstor.org/stable/26628344
Bianchini S, Pellegrino G (2019) Innovation persistence and employment dynamics. Res Pol 48(5):1171–1186. https://doi.org/10.1016/j.respol.2018.12.008
Brynjolfsson E, McAfee A (2014) The second machine age: work, progress, and prosperity in a time of brilliant technologies. Norton, New York
Google Scholar
Bond SR (2002) Dynamic panel data models: a guide to micro data methods and practice. Port Econ J 1:141–162. https://doi.org/10.1007/s10258-002-0009-9
Cabanas E, Illouz E (2019) Manufacturing happy citizens: how the science and industry of happiness control our lives. Polity Press, Cambridge, UK
Canatay A, Prieto L, Amin MR (2023) Integrating “Neoliberal-turn” and “Social-turn” constructs in examining sustainable development and happiness and life satisfaction: a global-, country cluster-, and country-level study. Sustainability 15:10010. https://doi.org/10.3390/su151310010
Carlsen L (2018) Happiness as a sustainability factor. The world happiness index. a posetic based data analysis. Sustain Sci 13:549–571. https://doi.org/10.1007/s11625-017-0482-9
Castellacci F (2022) Innovation and social welfare: a new research agenda. J Econ Surv 37:1156–1191. https://doi.org/10.1111/joes.12537
Ceci MW, Kumar VK (2016) A correlational study of creativity, happiness, motivation, and stress from creative pursuits. J Happiness Stud 17:609–626. https://doi.org/10.1007/s10902-015-9615-y
Chua RY, Huang KG, Jin M (2019) Mapping cultural tightness and its links to innovation, urbanization, and happiness across 31 provinces in China. Proc Natl Acad Sci USA 116(14):6720–6725. https://doi.org/10.1073/pnas.1815723116
Article ADS CAS PubMed PubMed Central Google Scholar
Chuluun T, Graham C (2016) Local happiness and firm behavior: do firms in happy places invest more? J Econ Behav Organ 125:41–56. https://doi.org/10.1016/j.jebo.2016.01.014
Clark AE, Frijters P, Shields MA (2008) Relative income, happiness, and utility: an explanation for the Easterlin paradox and other puzzles. J Econ Lit 46(1):95–144. https://doi.org/10.1257/jel.46.1.95
Costa J, Tashakori N (2023) Persistence in innovation. Do low-tech sectors differ much from the high-tech?. In: de Oliveira Matias JC, Oliveira Pimentel CM, Gonçalves dos Reis JC, Costa Martins das Dores JM, Santos G (eds) Quality innovation and sustainability. ICQUIS 2022. Springer proceedings in business and economics. Springer, Cham. https://doi.org/10.1007/978-3-031-12914-8_20
Derclaye E (2014) Do patents, trademarks and designs foster happiness in developed countries? An empirical analysis. Int J Happiness Dev 1(4):357–368. https://doi.org/10.1504/IJHD.2014.066121
Di Tella R, MacCulloch RJ, Oswald AJ (2003) The macroeconomics of happiness. Rev Econ Stat 85(4):809–827. https://doi.org/10.1162/003465303772815745
Diener E (2012) New findings and future directions for subjective well-being research. Am Psychol 67(8):590–597. https://doi.org/10.1037/a0029541
Diener E, Diener M (1995) Cross cultural correlates of life satisfaction and self-esteem. J Pers Soc Psychol 68:653–663. https://doi.org/10.1037/0022-3514.68.4.653
Article CAS PubMed Google Scholar
Diener E, Oishi S, & Lucas RE (2002) Subjective well-being: the science of happiness and life satisfaction. In The Oxford handbook of positive psychology. Oxford University Press, USA
Dolan P, Metcalfe R (2012) The relationship between innovation and subjective wellbeing. Res Pol 41(8):1489–1498. https://doi.org/10.1016/j.respol.2012.04.001
Dolan P, Peasgood T, White M (2008) Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being. J Econ Psychol 29(1):94–122. https://doi.org/10.1016/j.joep.2007.09.001
Dwyer L (2020) Tourism development and sustainable well-being: a Beyond GDP perspective. J Sustain Tour 31(10):2399–2416. https://doi.org/10.1080/09669582.2020.1825457
Easterlin RA (1974) Does economic growth improve the human lot? Some empirical evidence. In: David R, Reder M (eds) Nations and households in economic growth: essays in honor of Moses Abramovitz. Academic Press, New York, pp 89–125. https://doi.org/10.1016/B978-0-12-205050-3.50008-7
Easterlin RA (1995) Will raising the incomes of all increase the happiness of all? J Econ Behav Organ 27(1):35–47. https://doi.org/10.1016/0167-2681(95)00003-B
Easterlin RA (2004) The economics of happiness. Daedalus 133(2):26–33. https://doi.org/10.1162/001152604323049361
Engelbrecht HJ (2015) A general model of the innovation-subjective well-being nexus. In: Pyka A, Foster J (eds) The evolution of economic and innovation systems. Springer, Cham, Switzerland, pp 69–90
Espasandín-Bustelo F, Ganaza-Vargas J, Diaz-Carrion R (2021) Employee happiness and corporate social responsibility: the role of organizational culture. Empl Relat 43(3):609–629. https://doi.org/10.1108/ER-07-2020-0343
Frey BS, Stutzer A (2009) Should national happiness be maximized? In: Dutt AK, Radcliff B (eds) Happiness, economics and politics. Edward Elgar Publishing, Cheltenham, UK. https://doi.org/10.4337/9781849801973.00023
Fuertes-Callen Y, Cuellar-Fernandez B (2019) Inter-relationship between firm growth and profitability in a context of economic crisis. J Bus Econ Manag 20(1):86–106. https://doi.org/10.3846/jbem.2019.6928
Grosche SC (2014) What does Granger causality prove? A critical examination of the interpretation of Granger causality results on price effects of index trading in agricultural commodity markets. J Agric Econ 65(2):279–302. https://doi.org/10.1111/1477-9552.12058
Guven C (2011) Are happier people better citizens? Kyklos 64(2):178–192. https://doi.org/10.1111/j.1467-6435.2011.00501.x
Hagerty MR, Veenhoven R (2003) Wealth and happiness revisited – growing national income does go with greater happiness. Soc Indic Res 64:1–27. https://doi.org/10.1023/A:1024790530822
Helliwell JF (2003) How’s life? Combining individual and national variables to explain subjective well-being. Econ Model 20(2):331–360. https://doi.org/10.1016/S0264-9993(02)00057-3
Helliwell JF, Layard R, Sachs JD, Aknin LB, De Neve JE, & Wang S (eds) (2023) World happiness report 2023 (11th ed.). Sustainable Development Solutions Network, New York, US
Herzer D, Strulik H (2017) Religiosity and income: a panel cointegration and causality analysis. Appl Econ 49(30):2922–2938. https://doi.org/10.1080/00036846.2016.1251562
Huntsinger JR, Raoul A (2019) The affective origins of the Industrial Revolution. Behav Brain Sci 42:e203. https://doi.org/10.1017/S0140525X19000141
Huong PT, Cherian J, Hien NT, Sial MS, Samad S, Tuan BA (2021) Environmental management, green innovation, and social–open innovation. J Open Innov Technol Mark Complex 7(89):1–14. https://doi.org/10.3390/joitmc7010089
Inglehart RF (2017) Evolutionary modernization theory: Why people’s motivations are changing. Changing Soc Personal. 1(2):136–151. https://doi.org/10.15826/csp.2017.1.2.010
Ionescu-Feleagă L, Ionescu BȘ, Stoica OC (2022) The impact of digitalization on happiness: a European perspective. Mathematics 10(15):2766. https://doi.org/10.3390/math10152766
Isen AM (2008) Some ways in which positive affect influences decision making and problem solving. In: Lewis M, Haviland-Jones JM, Feldman-Barrett L (eds) Handbook of emotions, 3rd edn. pp 548–573. Guilford Press, New York, US
Jalles JT (2015) Panel causality and cointegration between productivity and unemployment. Appl Econ Q 61(2):141–153. https://doi.org/10.3790/aeq.61.2.141
Jaswal V, Kishore K, Muniraju M, Jaswal N, Kapoor R (2020) Understanding the determinants of happiness through Gallup World Poll. J Fam Med Prim Care 9(9):4826
Ji Y, Wang Z (2022) Impact of local living environment on innovation efficiency of high-tech industries in China: a spatial analysis. Environ Sci Pollut Res 29(48):73563–73576. https://doi.org/10.1007/s11356-022-21038-8
Juodis A, Karavias Y (2019) Partially heterogeneous tests for Granger non-causality in panel data (59). Bank of Lithuania, Vilnius, Lithuania
Kahneman D, Krueger A (2006) Developments in the measurement of subjective well-being. J Econ Perspect 20(1):3–24. https://doi.org/10.1257/089533006776526030
Kamguia B, Keneck‐Massil J, Njangang H, Tadadjeu S (2023) Sophistication gap between countries: the effect of research and development expenditure. Econ Transit Instit Change 1–40. https://doi.org/10.1111/ecot.12400
Kao C (1999) Spurious regression and residual-based tests for cointegration in panel data. J Econ 90(1):1–44. https://doi.org/10.1016/S0304-4076(98)00023-2
Article MathSciNet Google Scholar
Law SH, Azman-Saini WNW, Tan HB (2014) Economic globalization and financial development in East Asia: a panel cointegration and causality analysis. Emerg Mark Financ Tr 50(1):210–225. https://doi.org/10.2753/REE1540-496X500112
Layard R (2005) Happiness: lessons from a new science. Allen Lane, London
Layous K, Nelson SK, Kurtz JL, Lyubomirsky S (2017) What triggers prosocial effort? A positive feedback loop between positive activities, kindness, and well-being. J Posit Psychol 12(4):385–398. https://doi.org/10.1080/17439760.2016.1198924
Li D, Shen W (2022) Regional happiness and corporate green innovation: a financing constraints perspective. Sustainability 14(4):2263. https://doi.org/10.3390/su14042263
Lopez L, Weber S (2017) Testing for Granger causality in panel data. Stata J 17(4):972–984. https://doi.org/10.1177/1536867X1801700412
Lu X, Su L, White H (2017) Granger causality and structural causality in cross-section and panel data. Econ Theory 33(2):263–291. https://doi.org/10.1017/S0266466616000086
Maziarz M (2015) A review of the Granger-causality fallacy. J Philos Econ 8(2):86–105. https://doi.org/10.46298/jpe.10676
McManus JF, Carvalho SW (2022) Consumers’ love for technological gadgets is linked to personal growth. Pers Individ Dif 194:111637. https://doi.org/10.1016/j.paid.2022.111637
Ministry of Foreign Affairs of Denmark (2021). The world we share. Denmark’s strategy for development cooperation. https://amg.um.dk/-/media/country-sites/amg-en/policies-and-strategies/strategy-for-denmarks-development-cooperation/denmarks-strategy-for-development-cooperation-the-world-we-share-1-.ashx
Mourao PR, Popescu IA (2023) Investment, growth and competitiveness: the multiplier-accelerator in the 21st century. J Competitiveness 15(3):60–78. https://doi.org/10.7441/joc.2023.03.04
Musick K, Meier A (2012) Assessing causality and persistence in associations between family dinners and adolescent well‐being. J Marriage Fam 74(3):476–493. https://doi.org/10.1111/j.1741-3737.2012.00973.x
Article PubMed PubMed Central Google Scholar
Mutonyi BR, Slåtten T, Lien G (2020) Empowering leadership, work group cohesiveness, individual learning orientation and individual innovative behaviour in the public sector: empirical evidence from Norway. Int J Public Leadersh 16(2):175–197. https://doi.org/10.1108/IJPL-07-2019-0045
Nelson RR (1996) National innovation systems: a retrospective on a study (381-409). In: Dosi G, Malerba F (eds) Organization and strategy in the evolution of the enterprise. Palgrave Macmillan, UK. https://doi.org/10.1007/978-1-349-13389-5_17
O’Mahony M, Van Ark B (2003) EU productivity and competitiveness: an industry perspective. Can Europe resume the catching-up process. Enterprise publications. Office for Official Publications of the European Communities, Luxembourg
Pedroni P (1999) Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxf Bull Econ Stat 61(S1):653–670. https://doi.org/10.1111/1468-0084.61.s1.14
Pedroni P (2004) Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econ Theory 20(3):597–625. https://doi.org/10.1017/S0266466604203073
Plepytė-Davidavičienė G (2020) How is Happiness Measured? Filos Sociol 31(2):107–116. https://doi.org/10.6001/fil-soc.v31i2.4229
Popescu AI (2020) Long-term city innovation trajectories and quality of urban life. Sustainability 12(24):10587. https://doi.org/10.3390/su122410587
Popescu IA, Reis Mourao P, Bilan Y (2023) Innovation, coopetition and spillover effects in European regions. J Bus Econ Manag 24(5):818–840. https://doi.org/10.3846/jbem.2023.19890
Prime Minister’s Office Finland (2020). Voluntary national review 2020. Report on the implementation of the 2030 agenda for sustainable development. https://sustainabledevelopment.un.org/content/documents/26265VNR_Report_Finland_2020.pdf
Pugno M (2019) Happiness, human development, and economic (de)growth. Ann Fond Luigi Einaudi LIII:151–172. https://doi.org/10.26331/1088
Radu I (2013) The methodology of testing the causality between the romanian mutual funds market and the economy’s dynamics. Netw Intell Stud 1(1):90–101
Rego A, Machado F, Leal S, Cunha MPE (2009) Are hopeful employees more creative? An empirical study. Creat Res J 21(2-3):223–231. https://doi.org/10.1080/10400410902858733
Reznakova M, Stefankova S (2022) New indicators of innovation activity in economic growth models. J Competitiveness 14(3):153–172. https://doi.org/10.7441/joc.2022.03.09
Roodman D (2009) How to do xtabond2: an introduction to difference and system GMM in Stata. Stata J 9(1):86–136. https://doi.org/10.1177/1536867X0900900106
Ross M, Eyman A, Kishchuck N (2005) Determinants of subjective well-being. In: Olson JM, Herman CP, Zanna MP (eds) Relative deprivation and social comparison. Psychology Press, Hove, UK, pp 79–92
Ryan R, Huta V, Deci EL (2008) Living well: a self-determination theory perspective on eudaimonia. J Happiness Stud 9:139–170. https://doi.org/10.1007/s10902-006-9023-4
Schubert C (2012) Is novelty always a good thing? Towards an evolutionary welfare economics. J Evol Econ 22(3):585–619. https://doi.org/10.1007/s00191-011-0257-x
Schumpeter J (1934) Theory of economic development/Theorie der wirtschaftlichen Entwicklung, Leipzig. Harvard University Press, Cambridge, MA
Stiglitz J, Fitoussi J, Durand M (2018) Beyond GDP: measuring what counts for economic and social performance. OECD Publishing, Paris. https://doi.org/10.1787/9789264307292-en
Su Y, Muhammad AS (2023) Role of economic, and social parameters affecting life satisfaction and happiness during pre and post Covid era: a study with Marx’s perspective. Econ Res-Ekon Istraz 36:1. https://doi.org/10.1080/1331677X.2023.2166970
Sweida G, Sherman CL (2020) Does happiness launch more businesses? Affect, gender, and entrepreneurial intention. Int J Environ Res Public Health 17(18):6908. https://doi.org/10.3390/ijerph17186908
Trabelsi MA (2023) What is the impact of social well-being factors on happiness? Eur J Manag Stud 28(1):37–47. https://doi.org/10.1108/EJMS-01-2022-0004
Tsurumi T, Imauji A, Managi S (2018) Greenery and subjective well-being: assessing the monetary value of greenery by type. Ecol Econ 148:152–169. https://doi.org/10.1016/j.ecolecon.2018.02.014
Usai A, Orlando B, Mazzoleni A (2020) Happiness as a driver of entrepreneurial initiative and innovation capital. J Intellect Cap 21(6):1229–1255. https://doi.org/10.1108/JIC-11-2019-0250
Veenhoven, R (2012). Happiness: also known as “life satisfaction” and “subjective well-being”. In: Land KC, Michalos AC, Sirgy MJ (eds) Handbook of social indicators and quality of life research. Springer, Netherlands, pp 63–77. https://doi.org/10.1007/978-94-007-2421-1_3
Veenhoven R (2012) Cross-national differences in happiness: cultural measurement bias or effect of culture? Int J Wellbeing 2(4):333–353. https://doi.org/10.5502/ijw.v2.i4.4
Veenhoven R, Chiperi F, Kang X, Burger M (2021) Happiness and consumption: a research synthesis using an online finding archive. Sage Open, 11(1). https://doi.org/10.1177/2158244020986239
Vivarelli M (2014) Innovation, employment and skills in advanced and developing countries: a survey of economic literature. J Econ Issues 48(1):123–154. https://doi.org/10.2753/JEI0021-3624480106
Voukelatou V, Gabrielli L, Miliou I, Cresci S, Sharma R, Tesconi M, Pappalardo L (2021) Measuring objective and subjective well-being: dimensions and data sources. Int J Data Sci Anal 11:279–309. https://doi.org/10.1007/s41060-020-00224-2
Wang J, Yang J, Xue Y (2017) Subjective well-being, knowledge sharing and individual innovation behavior: the moderating role of absorptive capacity. Leadersh Org Dev J 38(8):1110–1127. https://doi.org/10.1108/LODJ-10-2015-0235
Wang S, Duan L, Jiang S (2022a) Research on spatial differences and driving effects of ecological well-being performance in China. Int J Environ Res Public Health 19(15):9310. https://doi.org/10.3390/ijerph19159310
Wang X, Guan S, Chen X (2022b) Evaluation of China’s High-quality Economic Development Level-Empirical study based on 30 provinces and municipalities. In: Proceedings of the 4th international conference on management science and industrial engineering. pp 63–70. https://doi.org/10.1145/3535782.3535792
Westerlund J (2005) A panel CUSUM test of the null of cointegration. Oxf Bull Econ Stat 67(2):231–262. https://doi.org/10.1111/j.1468-0084.2004.00118.x
World Bank (2024) World Development Indicators: GDP per capita (current US dollars). https://data.worldbank.org/indicator/NY.GDP.PCAP.CD
Yoon DJ, Bono JE, Yang T, Lee K, Glomb TM, Duffy MK (2022) The balance between positive and negative affect in employee well‐being. J Organ Behav 43(4):763–782. https://doi.org/10.1002/job.2580
Zaman A (2008) Causal relations via econometrics. MPRA Paper 10128. University Library of Munich, Germany. https://doi.org/10.2139/ssrn.1374208
Download references
Authors and affiliations.
Department of International Business and Economics, Bucharest University of Economic Studies, Bucharest, Romania
Irina Alina Popescu
Department of Economics & The Research Center in Economic Policies and Business (NIPE), University of Minho, Braga, Portugal
Paulo Jorge Reis Mourão
You can also search for this author in PubMed Google Scholar
The authors confirm their contribution to the article as follows: Introduction: IAP; Theoretical background: IAP and PJRM; Methodology: IAP and PJRM; Results: IAP and PJRM; Discussion: PJRM; Conclusions: IAP and PJRM.
Correspondence to Irina Alina Popescu .
Competing interests.
The authors declare no competing interests.
Ethical approval was not required as the study did not involve human participants.
Informed consent was not required as the study did not involve human participants.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .
Reprints and permissions
Cite this article.
Popescu, I.A., Reis Mourão, P.J. Exploring the nexus between national innovation performance and happiness. Humanit Soc Sci Commun 11 , 960 (2024). https://doi.org/10.1057/s41599-024-03491-7
Download citation
Received : 22 February 2024
Accepted : 17 July 2024
Published : 26 July 2024
DOI : https://doi.org/10.1057/s41599-024-03491-7
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
Description.
Research Hypothesis The study hypothesizes that design students' perceptions of authorship in AI-assisted design vary depending on the degree of influence their original image prompts have on the final AI-generated outcomes. It aims to understand at what point students consider AI-generated images their own creation and how comfortable they are in collaborating with AI tools, giving credit to the AI where due. Data Collection &Analysis Data was gathered through a mixed-methods approach involving an AI-aided design workshop followed by a survey. Participants included second-year industrial design (ID) and interior architecture (IA) students. The workshop introduced the Vizcom AI tool, which students used to generate AI-assisted designs based on their studio project outcomes. The survey assessed students' familiarity with AI tools, perceptions of authorship, reasons for their perceptions, and comfort levels in co-designing with AI. The sample size was 30 students (12 ID, 18 IA). difference in familiarity level with AI tools (Q1), perception of authorship (Q2), reasons of perception of the puthorship (Q3), and comfort level of collaboration with ai (Q4) between ID and IA students. A Spearman's rank correlation coefficient can be calculated to assess the relationship between students' familiarity with AI tools (Q1) and their comfort levels in co-designing with AI (Q4). Textual analysis can be conducted to thematically analyze the responses from the open-ended question, Q5. Notable Findings T-Test: The T-test results indicate that there is no statistically significant difference between ID and IA students in terms of their familiarity with AI tools, perception of authorship, reasons for their perception of authorship, and comfort level with collaborating with AI. Perception of Authorship: 33.3% of students considered AI-generated images as their own creation up to a 50% image prompt influence level. Significant thresholds for authorship were identified between 40% and 70% image prompt influence, with most students feeling a sense of ownership within this range. Comfort with AI Collaboration: 46.4% of participants were comfortable or very comfortable collaborating with AI and might give credit to AI. A moderate positive correlation (rs=0.48) was found between familiarity with AI tools and comfort in co-designing with AI. Reasons for Perceived Authorship: The primary reason cited was that the design was still based on the students' original research or findings (60%). Implications for Future Research The study's findings suggest a need for further research with a larger and more diverse sample. Exploring perceptions of more experienced designers and different design fields could provide a broader understanding of authorship in AI-assisted design. Future studies could also delve deeper into the reasons behind neutral attitudes towards AI collaboration and explore the impact of storytelling in enhancing AI-human collaboration.
Study Design -The study used a mixed-methods approach, combining an AI-aided design workshop with a survey to collect both quantitative and qualitative data. Participants -The study involved 30 second-year students: 12 from Industrial Design (ID) and 18 from Interior Architecture (IA). Workshop Protocol -The 50-minute workshop introduced students to the Vizcom AI tool. -First 20 minutes: Introduction to the tool's features and use. -Next 20 minutes: Practice session where students used their project outcomes as prompts to generate AI-assisted designs. They created 11 images by adjusting the image prompt influence from 100% to 0% in 10% decrements. Suggested prompts were "modern design of a salt and pepper grinder" for ID and "a modern and sleek house exterior/interior" for IA. Survey Protocol -After the workshop, students completed a 10-minute survey. Survey Questions: -Familiarity with AI tools (Likert scale: 1-5). -Perception of authorship at different AI contribution levels (percentage-based scale: 0%-100%). -Reasons for authorship perception (categorical with open-ended option). -Comfort in co-designing with AI and willingness to give credit (Likert scale: 1-5). -Open-ended question on overall thoughts about using Vizcom AI. Instruments and Software -Vizcom AI Tool: Used in the workshop. -Survey Software: For administering and collecting responses. -Statistical Software: Excel/SPSS/R for t-tests and correlation analysis. -Qualitative Analysis Software: Qualtrics for thematic analysis. Data Analysis Workflow Quantitative Data Analysis -Descriptive statistics summarized frequencies and percentages. -Independent samples t-tests compared ID and IA student responses. -Spearman's rank correlation coefficient assessed the relationship between AI familiarity and comfort in co-designing. Qualitative Data Analysis -Thematic analysis of open-ended responses involved coding, categorizing, and interpreting themes. Reproducibility To reproduce the study: -Recruit second-year ID and IA students. -Conduct a 50-minute workshop introducing the Vizcom AI tool. -Administer a post-workshop survey assessing AI familiarity, authorship perceptions, comfort levels in co-designing with AI, and overall thoughts. -Use electronic survey platforms and ensure data anonymity. -Analyze data with statistical and qualitative software, following the outlined workflows. -By following these protocols, researchers can replicate the study to explore design students' perceptions of authorship in AI-assisted design.
IMAGES
VIDEO
COMMENTS
Step 5: Present your findings. The results of hypothesis testing will be presented in the results and discussion sections of your research paper, dissertation or thesis.. In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p-value).
The predicted outcome of the experiment or analysis; 5. Phrase your hypothesis in three ways. To identify the variables, you can write a simple prediction in ... Write a null hypothesis. If your research involves statistical hypothesis testing, you will also have to write a null hypothesis. The null hypothesis is the default position that there ...
Hypothesis testing is a crucial procedure to perform when you want to make inferences about a population using a random sample. These inferences include estimating population properties such as the mean, differences between means, proportions, and the relationships between variables. This post provides an overview of statistical hypothesis testing.
A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes - specificity, clarity and testability. Let's take a look at these more closely.
Introduction. Statistical analysis is necessary for any research project seeking to make quantitative conclusions. The following is a primer for research-based statistical analysis. It is intended to be a high-level overview of appropriate statistical testing, while not diving too deep into any specific methodology.
Hypothesis testing is a scientific method used for making a decision and drawing conclusions by using a statistical approach. It is used to suggest new ideas by testing theories to know whether or not the sample data supports research. A research hypothesis is a predictive statement that has to be tested using scientific methods that join an ...
A hypothesis test consists of five steps: 1. State the hypotheses. State the null and alternative hypotheses. These two hypotheses need to be mutually exclusive, so if one is true then the other must be false. 2. Determine a significance level to use for the hypothesis. Decide on a significance level.
3. Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4.
HYPOTHESIS TESTING. A clinical trial begins with an assumption or belief, and then proceeds to either prove or disprove this assumption. In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the "alternate" hypothesis, and the opposite ...
It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis. 7.
A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis.
A research question is what a study aims to answer after data analysis and interpretation. ... This statement is based on background research and current knowledge.8,9 The research hypothesis makes a specific prediction about a new phenomenon10 or a formal statement on the expected relationship between an independent variable and a dependent ...
A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
5 Hypothesis Testing in Quantitative Research . Mikaila Mariel Lemonik Arthur. Statistical reasoning is built on the assumption that data are normally distributed, meaning that they will be distributed in the shape of a bell curve as discussed in the chapter on Univariate Analysis.While real life often—perhaps even usually—does not resemble a bell curve, basic statistical analysis assumes ...
A research hypothesis helps test theories. A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior. It serves as a great platform for investigation activities.
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...
Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample data to draw conclusions about a population. It involves formulating two competing hypotheses, the null hypothesis (H0) and the alternative hypothesis (Ha), and then collecting data to assess the evidence.
Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter. The methodology employed by the analyst depends on the nature of the data used ...
Alternative Hypothesis (H1) or the research hypothesis states that there is a relationship between two variables (where one variable affects the other). ... Once the hypotheses have been formulated, it is now time to generate a testing plan. A testing plan or an analysis plan involves the accumulation of data samples, determining which ...
A research hypothesis defines the theory or problem your research intends to test. It is the "educated guess" of what the final results of your research or experiment will be. ... In developing your hypothesis statements, you must pre-plan some of your statistical analysis. Once you decide on your problem to examine, determine three aspects ...
A snapshot analysis of citation activity of hypothesis articles may reveal interest of the global scientific community towards their implications across various disciplines and countries. As a prime example, Strachan's hygiene hypothesis, published in 1989,10 is still attracting numerous citations on Scopus, the largest bibliographic database ...
Consequently, this study supports the hypothesis that innovation fosters improvements in resident well-being, despite ongoing debates. ... variations in the level of analysis and the research ...
Analysis of the EKC hypothesis on carbon emissions in Indonesia. After analyzing CO 2 emissions under four feasible scenarios, this study investigates the EKC hypothesis, which Kuznets put forward and defined by Panayotou (1993). According to the EKC hypothesis, pollution will rise as the economy expands at low-income levels and fall when ...
Research Hypothesis The study hypothesizes that design students' perceptions of authorship in AI-assisted design vary depending on the degree of influence their original image prompts have on the final AI-generated outcomes. It aims to understand at what point students consider AI-generated images their own creation and how comfortable they are in collaborating with AI tools, giving credit to ...