A Study on Behavioral Investment Approach Among Gig Workers and Self Entrepreneurs

 

Manjunath P K ¹, Satheeshkumar R ², Sachana C ³                      

 

¹ Student, MBA 2nd Year, Department of MBA & Research Centre, Surana College (Autonomous), Bangalore, India

² Professor, Department of MBA & Research Centre, Surana College, Kengeri Campus, Bengaluru, Karnataka, India

³ Research Scholar, Institute of Management Studies, Davangere University, Davangere, Karnataka, India

 

INTRODUCTION TO THE STUDY

The Financial Challenge of the Flexible Workforce

Unlike salaried employees who enjoy predictable paychecks and established financial safety nets, gig workers and the self-employed often operate month-to-month. Their financial decisions are frequently reactive, driven by immediate cash flow needs rather than a strategic, long-term investment mindset. This uncertainty creates a major barrier to conventional wealth-building and long-term financial security. They are particularly vulnerable to unexpected emergencies or economic downturns, as they lack the institutional safeguards like automatic payroll deductions for savings or employer-matched retirement contributions that benefit their salaried counterparts.

The Need for a Behavioral Investment Study

Despite their growing number and economic contribution, the financial behaviors of gig workers remain largely underexplored in academic research and underserved by the financial industry. Existing literature on behavioral finance and investment largely focuses on individuals with steady incomes, leaving a critical gap in our understanding of this demographic.

This study aims to bridge that gap by looking through the lens of behavioral finance. Classical economic theory assumes people are perfectly rational, but in reality, psychological biases such as the fear of loss (loss aversion) or following the crowd heavily influence financial choices. For workers under the constant pressure of financial instability, these biases are even more pronounced. Understanding how factors like financial literacy, demographics, and the use of digital financial tools shape the investment choices of gig workers and self-entrepreneurs is essential.

Practical Impact and a Novel Solution

The importance of this research extends beyond academia. The findings are intended to provide actionable insights for:

1)     Financial Institutions and FinTech Companies: To help them design innovative and inclusive financial products that truly fit the unpredictable income patterns of this workforce.

2)     Policymakers: To inform initiatives that foster greater financial stability and long-term well-being for this key segment of the Indian economy.

To provide a practical solution, this study introduces and evaluates the Daily Incremental Micro-Investment Model (DIIM). This framework is designed to encourage small, regular contributions that accumulate into meaningful savings over time. By reducing the psychological burden of committing large, fixed sums and adapting to irregular cash flow, DIIM offers a realistic pathway to long-term financial resilience and helps turn short-term cash flow into a foundation for future wealth.

 

Problem Statement

Despite being vital to the Indian economy, gig workers and entrepreneurs are financially vulnerable due to irregular income, low financial literacy, and lack of payroll-based savings. Their financial decisions are driven more by cognitive biases and emotional responses than rational planning, a reality not captured by existing financial research focused on salaried employees. The pressing need is to investigate these behavioral factors and design financial models aligned with their unpredictable earnings to ensure their long-term security.

 

Objectives of the Study

The main objectives of this study is to examine, The behavioral approaches to investment among gig workers and independent entrepreneurs in India. The specific aims are to:

1)     Assess the statistical significance and practical effect size of a single motivational factor on an individual’s willingness to adhere to a micro-investment plan.

2)     Assess the relationship between workers’ self-rated financial literacy and their awareness of various investment options.

3)     Assess how specific demographic variables (age, education, income) are associated with self-rated financial literacy.

4)     Examine the relationship between psychological and behavioral factors that may hinder investment decisions.

 

Scope and Limitations

Scope (Focus)	Limitations (Constraints)
Target: Gig workers and self-employed in urban India.	Data: Difficulty accessing a dispersed, informal workforce; reliance on self-reporting bias.
Focus: Influence of behavioral biases (e.g., loss aversion, herding) on investment decisions.	Design: Cross-sectional (snapshot in time); limited to select urban centers, not reflecting rural dynamics.
Tools: Role of digital financial tools and fintech apps in shaping investment patterns.	Time: Challenging to track investment outcomes due to the dynamic nature of gig work.
Goal: Provide actionable insights for policymakers and fintech firms to design inclusive solutions.

 

Significance of the Study

·        Gig Workers & Self-Entrepreneurs: By identifying behavioral traps and offering strategies for disciplined investment behavior.

·        Low-income individuals: By offering a practical, stress-free path to disciplined saving and wealth accumulation.

·        Policy Makers: For framing supportive policies including social security, micro-pension schemes, and financial literacy campaigns.

·        Academia: To enrich the body of knowledge on behavioral finance in non-traditional labor settings.

·        Fintech Companies: To personalize app interfaces, alerts, and nudges based on behavioral profiles.

 

Review of previous studies

Nkukpornu, E., Gyimah, P., & Sakyiwaa, L. (2020). This studies aimed to explore the role of behavioral biasis such as overconfidence, regret, belief, and snakebite effects on investors' decisions in Ghana's developing market. Using descriptive and multiple Regression analys, the authors confirmed that these biases significantly influence stock market behavior. The study reveals that belief bias and snakebite effects strongly impacts investment choices. However, its scope is limited to specific biases and Regional data.  which may not represent broader investor behavior. For gig workers or self-entrepreneurs, similar biases could lead to irrational financial planning or overcautious risk aversion, affecting business growth or self-investment decisions.

Bhavin Mukesh Patel, D., & Ayre, V.Patel and Ayre investigated how to cognitive biases such as overconfidenc, herd behavior, and loss aversion distort rational investment decisions. This conceptual and literature-based study emphasized how emotional and psychological factors disrupt objective decision-making. While useful, the research lacks primary data and real-world validation. For gig workers or self-entrepreneurs, understanding such biases is crucial, as overconfidence or herd behavior may push them into risky or unsuitable financial decisions, hindering sustainable wealth creation.

Armansyah, R.F. (2021). Armansyah studied the effects of overconfidence, mental accounting, and loss aversion on Indonesian investors through a survey of 250 respondents. Regression analysis revealed that overconfidence and mental accounting significantly impact investment decisions, whereas loss aversion showed no significant influence. The research is region-specific, limiting broader generalization. Gig workers, like these investors, may also fall prey to overconfidence or flawed mental accounting, impacting their self-employment savings or reinvestment choices, stressing the need for better financial literacy.

Go, T.A., Lim, V.N., Albao, M.O., & Baylon, D.P. (2020) investigated the relationship between investment preferences and factors like personality traits, risk perception, and investment goals among 96 working adults investing in various assets. Using surveys and statisticial analysis, they found that investor profiles and risk aversion significantly influenced medium-term investments, with aggressive investors preferring long-term options. However, they could have not establish a firm links between all the personality traits with investment decisions. These findings relate to gig workers and self-entrepreneurs, whose financial behavior may also be shaped by personal traits and risk perceptions when planning business or personal investments.

Quang, L.T., Linh, N.D., Nguyen, D.V., & Khoa, D.D. (2023) explored how behavioral factors such as mood, overconfidence, underreaction, overreaction, and herding behavior impact individual investor decisions in Vietnam. They employed a structured questionnaire among 400 investors and used partial multiple regression for analysis. The findings showed that demographic variables like gender, age, and education positively influenced decision- making, while experience reduced emotional interference. A limitation is the region-specific sample, which might not generalize globally. This study highlights that just as investors exhibit biases in stock markets, gig workers or self-entrepreneurs may also rely on emotional and cognitive biases when making crucial financial or business decisions.

 

Research Gap

While these studies confirm the pervasive role of behavioral biases, they are primarily region-specific, focus on formal stock market investors or salaried employees, and lack primary data from the non-traditional labor market. They do not address the unique financial context of irregular income and absence of structured savings (like payroll deductions), which is central to the gig economy. This highlights a critical need to investigate these biases directly among gig workers and self-entrepreneurs to design targeted, behaviorally informed financial solutions.

 

RESEARCH METHODOLOGY

Research Design

This study adopts an exploratory and descriptive Research design to investigate the behavioral investment approaches among gig workers and self-employed entrepreneurs. The exploratory aspect allows initial probing into relatively under- researched behavioral patterns, while the descriptive component facilitates detailed documentation of financial literacy, investment preferences, and demographic influences in this population.

 

Methodological Approach

A mixed-method approach is employed, integrating quantitative and qualitative techniques. Quantitative data are collected via structured questionnaires to capture measurable aspects such as financial literacy levels, investment awareness, and behavioral biases. Qualitative data are obtained through interviews to explore deeper insights into motivations, challenges, and cognitive biases affecting investment decisions.

 

Sampling Method and Size

Purposive and convenience by sampling techniques which are used to select study participants from urban gig workers and self-employed individuals in India, including drivers, food delivery workers, freelancers, and online sellers. While the exact sample size is not rigidly fixed, approximately 390 respondents participated in the quantitative survey; Qualitative interviews are conducted with a smaller subset to complement the findings.

 

Data Collection Methods

Data collection combines surveys and interviews. The structured questionnaire assesses demographic details, investment behavior, financial literacy, and attitudes towards a daily incremental investment model. Interviews provide qualitative elaboration on behavioral biases, financial challenges, and the role of fintech tools in shaping investment habits.

 

Research Instruments

Two main instruments are utilized: a structured questionnaire for the quantitative survey and an interview guide for qualitative discussions. The questionnaire includes demographic questions, Likert-scale items on behavioral traits, investment preferences, and awareness of financial tools, while the interview guide probes behavioral influences and barriers in investment planning.

 

Data Analysis Plan

Quantitative data are analyzed using statistical tools including regression analysis, correlation, and ANOVA to explore relationships among behavioral biases, financial literacy, demographic factors, and investment behavior. Qualitative data from interviews are thematically coded to identify recurring patterns related to cognitive biases, challenges, and facilitators in investment decision-making. This combined analysis offers a comprehensive understanding of behavioral investment patterns among gig workers. This mixed-method methodology ensures both breadth and depth in addressing the research questions, providing empirical evidence and rich narrative insights to inform practical interventions, policy frameworks, and financial product design tailored for the gig economy workforce.

 

 

DATA ANALYSIS AND INTERPRETATION

Demographic Profile of Respondents

Table 1

Table 1 Age Group
SI. No	Age Group	Number of Respondents	Percentage
1	Below 20	17	4.40%
2	21-30	177	45.40%
3	31-40	118	30.30%
4	41–50	56	14.40%
5	Above 50	22	5.60%
Total		390	100%
Source Structured Questionnaire

 

The survey shows that the largest proportion of respondents are between 21–30 years (45.4%), followed by those aged 31–40 years (30.3%). This indicates that younger adults form the core of the participant base, highlighting a workforce in its prime earning and investing years. The share of individuals below 20 years is very low (4.4%), suggesting limited participation of teenagers in gig or entrepreneurial activities. Respondents above 50 years form 5.6%, which reflects that only a few continue to engage in such work at older ages. The middle age groups dominate, showing that gig work and small businesses are more attractive to early and mid- career individuals. This age distribution suggests a youthful inclination toward alternative work structures.

Table 2

Table 2 Gender
SI No.	Gender	Number of Respondents	Percentage
1	Male	229	58.70%
2	Female	157	40.30%
3	Other	4	1%
Total		390	100%

 

The gender distribution highlights male dominance with 58.7% respondents, while females account for 40.3%. The small presence of “Other” (1%) shows inclusivity but also reveals that participation from diverse genders remains minimal. The high share of male respondents may reflect that gig work and entrepreneurship are male-driven fields, though the female share is not insignificant. This results also indicate that women are increasingly exploring flexible earning opportunities. The data points to a potential shift in traditional roles as females enter self-employment sectors.

Table 3

Table 3 Educational Qualification
SI No.	Qualification	Number of Respondents	Percentage
1	Below 10th	16	4.10%
2	PUC/Intermediate	46	11.80%
3	Undergraduate	155	39.70%
4	Postgraduate	169	43.30%
5	Doctorate or above	4	1%
Total		390	100%
Source Structured Questionnaire

              

The data indicates that most respondents are highly educated, with 43.3% being postgraduates and 39.7% undergraduates. A small portion (11.8%) completed only PUC/Intermediate, and 4.1% are below 10th standard, showing limited low-education representation. The presence of doctorate holders (1%) is minimal, but it adds academic diversity. This suggests that self- employment and gig opportunities attract people from varied educational backgrounds, though higher education dominates. The strong representation of graduates shows that educated youth are increasingly turning to gig work or small businesses instead of traditional jobs.

Table 4

Table 4 How Would you Rate your Financial Literacy
SI No.	financial literacy	Number of Respondents	Percentage
1	Very high	45	11.50%
2	Good	113	29%
3	Average	150	38.50%
4	Low	73	18.70%
5	No knowledge	9	2.30%
Total		390	100%
Source Structured Questionnaire

                     

The majority of respondents rate themselves as having average financial literacy (38.5%). About 29% believe they have good knowledge, while 11.5% consider it very high. On the other hand, 18.7% admit to having low literacy, and 2.3% claim no knowledge at all. This shows that while a fair share of participants has reasonable understanding, there remains a significant knowledge gap. Financial literacy plays a crucial role in investment decisions, and the data suggests the need for awareness programs.

Table 5

Table 5 Are you Aware of Various Investment Options
SI No.	aware of various investment options	Number of Respondents	Percentage
1	Yes	279	71.60%
2	No	111	28.50%
Total		390	100%
Source Structured Questionnaire

        

Most respondents (71.6%) are aware of various investment options, while 28.5% are not. This shows a positive trend of financial awareness among gig workers and entrepreneurs. However, nearly one-third lacking awareness highlights a gap in financial knowledge. The results emphasize the needed for wider financial education and exposure to diverse investment instruments. Awareness is a key factor in making sound financial decisions, and these findings reveal both strengths and limitations among respondents.

Table 6

Table 6 Which Investment Style Suits your Income Pattern Best?
SI No.	Investment style	Number of Respondents	Percentage
1	Small daily/weekly investments	81	20.80%
2	Monthly SIP/recurring deposits	142	36.40%
3	Lump-sum investments occasionally	142	36.40%
4	Not investing currently	25	6.40%
Total		390	100%
Source Structured Questionnaire

      

Monthly SIPs or recurring deposits (36.4%) and lump-sum occasional investments (36.4%) are equally popular, highlighting two distinct strategies. Smaller daily or weekly contributions are chosen by 20.8%, showing the appeal of flexible micro-investments. Only 6.4% are not investing currently, suggesting broad participation in some form of investment. The dominance of SIPs and lump sums shows preference for structured yet adaptable patterns. This also reflects how income stability influences the style of saving and investing.

Table 7

Table 7 What Influences your Investment Decisions the Most
SI No.	investment decisions	Number of Respondents	Percentage
1	My own research	71	18.20%
2	Family/friends	119	30.50%
3	Social media/online groups	105	26.90%
4	Bank/financial advisor	83	21.30%
5	News/market trends	12	3.10%
Total		390	100%
Source Structured Questionnaire

              

Family and friends have the strongest influence (30.5%), followed by social media and online groups (26.9%). Banks and financial advisors impact 21.3%, while 18.2% rely on personal research. News and market trends influence only 3.1%, suggesting limited reliance on formal financial news. This shows that informal networks play a bigger role than expert guidance. It also highlights the increasing impact of digital platforms in shaping investment decisions.

Table 8

Table 8 I Believe I Can Make Better Investment Decisions than Most People
SI No.	Agreement Level	Number of Respondents	Percentage
1	Strongly agree	87	22.30%
2	Agree	128	32.80%
3	Neutral	103	26.40%
4	Disagree	59	15.10%
5	Strongly disagree	13	3.30%
Total		390	100%
Source Structured Questionnaire

     

A combined 55.1% (22.3% strongly agree, 32.8% agree) feel they can make better investment choices than others. Meanwhile, 26.4% remain neutral, showing uncertainty. Only 18.4% disagree or strongly disagree. This suggests a strong level of self-confidence among respondents in handling investments. However, the presence of neutrality and disagreement also points to gaps in actual knowledge versus perception. Confidence may   not always translate into effective financial outcomes.

Table 9

Table 9 I Often Invest in Risky Options Hoping for High Returns
SI No.	Agreement Level	Number of Respondents	Percentage
1	Strongly agree	72	18.50%
2	Agree	144	36.90%
3	Neutral	97	24.90%
4	Disagree	61	15.60%
5	Strongly disagree	16	4.10%
Total		390	100%
Source Structured Questionnaire

      

Risk-taking is fairly common, with 36.9% agreeing and 18.5% strongly agreeing that they invest in risky options for higher returns. Around 24.9% stay neutral, while 19.7% prefer safer options. This indicates a balanced outlook where a majority are open to risk but not overwhelmingly so. Neutral responses suggest uncertainty or selective risk-taking depending on circumstances. The data shows that while risk appetite exists, it is not universal.

Table 10

Table 10 Fear of Losing Money Stops Me from Investing More
SI No.	Agreement Level	Number of Respondents	Percentage
1	Strongly agree	83	21.30%
2	Agree	127	32.60%
3	Neutral	90	23.10%
4	Disagree	77	19.70%
5	Strongly disagree	13	3.30%
Total		390	100%
Source Structured Questionnaire

          

A significant portion, 32.6% agree and 21.3% strongly agree, that fear of losses prevents them from investing more. About 23.1% remain neutral, while 23% (disagree + strongly disagree) are not held back by fear. This reveals that psychological barriers plays important role in investment behavior. Risk aversion limits growth opportunities, especially among uncertain investors. The results highlight the importance of financial confidence and education in overcoming such fears.

Table 11

Table 11 Would you be Comfortable with a Daily Investment Plan Starting with
SI No.	Agreement Level	Number of Respondents	Percentage
1	Yes, definitely	106	27.20%
2	Maybe	124	31.80%
3	Not sure	96	24.60%
4	Probably not	63	16.20%
5	No	0	0%
Total		390	100%
Source Structured Questionnaire

           

Nearly 27.2% are definitely comfortable, while 31.8% say maybe, and 24.6% are unsure. Only 16.2% are likely not interested, and none completely reject the idea. This reflects curiosity and openness toward daily micro-investments, though not yet full acceptance. The high number of uncertain responses suggests a need for awareness and clarity. With proper education, this model could attract wider acceptance.

 

Table 12

Table 12 What Would Motivate You to Stick to Such a Daily Investment Plan
SI No.	Agreement Level	Number of Respondents	Percentage
1	Automatic deductions through apps	67	17.20%
2	Daily reminders	127	32.60%
3	Seeing gradual growth in savings	118	30.30%
4	Support from family/friends	78	20%
Total		390	100%
Source Structured Questionnaire

           

Daily reminders (32.6%) are the top motivator, followed by seeing gradual savings growth (30.3%). Support from family/friends (20%) and automatic deductions (17.2%) are also important. The findings show that behavioral nudges and visible progress encourage consistency. Psychological motivation plays a greater role than automation. This highlights the importance of habit-building and peer influence in financial discipline.

Table 13

Table 13 How Often Do You Discuss Your Finances with Peers or Family? (1=Never, 5=Always)
SI No.	Agreement Level	Number of Respondents	Percentage
1	Never	12	3.10%
2	Rarely	27	6.90%
3	Sometimes	81	20.80%
4	Yes, regularly	177	45.40%
5	Always	93	23.80%
Total		390	100%
Source Structured Questionnaire

      

Almost half (45.4%) regularly discuss finances, while 23.8% always engage in such talks. About 20.8% sometimes share, and only 10% rarely or never. This indicates that financial discussions are common, showing openness and reliance on peer advice. Such interactions can lead to better awareness but may also spread misinformation. The findings underline the role of family and community in shaping financial decisions.

 

Summary of Findings, Discussion, and Implications

The study, based on a survey of 390 Indian gig workers and self-entrepreneurs, provides a critical look at their financial behaviors, revealing the profound influence of behavioral biases on saving and investment decisions within an irregular income context. The demographic profile confirms that the gig economy is primarily youth-driven (nearly three-fourths are 21–40 years old) and highly educated (over 83% are graduates or postgraduates), yet a significant gap exists between formal education and practical financial awareness. Key findings indicate that while most respondents are active savers and digitally inclined, their practices are unstructured, highlighting a need for disciplined financial tools.

Key Findings and Behavioral Insights

The core challenge lies in a state of cognitive dissonance: 55.4% of respondents were open to risky investments for high returns, but 53.9% admitted that fear of losing money (loss aversion) prevents them from investing at all. Furthermore, a substantial majority displayed herding behavior (61% rely on informal advice) and overconfidence bias (55.1% believe they make better financial decisions than others despite low/average literacy). This tension demonstrates how psychological factors strongly override rational economic choices in an uncertain earning environment. The study found that nearly of participants rated their financial literacy as average or low, confirming a serious knowledge deficit that makes them highly susceptible to these biases.

Crucially, the research introduced and tested the Daily Incremental Micro-Investment (DIIM) Model, a practical solution designed to counter these biases. Acceptance of DIIM was strong, with nearly two-thirds of respondents expressing willingness to adopt it, recognizing its potential to match savings with cash flow unpredictability and reduce the psychological barrier of lump-sum investing. Regression analysis affirmed that motivational factors, such as daily reminders and seeing gradual growth, significantly influence adherence to the plan.

Implications and Recommendations

The findings offer actionable insights for financial stakeholders. Given the high willingness to adopt DIIM, Fintech companies should prioritize designing flexible micro-investment schemes that allow contributions as low as with pause-and-resume functions to accommodate income volatility. They must integrate gamification (streaks, badges) and behavioral nudges (reminders, progress visualization) to overcome loss aversion and foster saving consistency. Gig platforms and Policymakers should collaborate to integrate auto-deduction mechanisms directly into payout systems and formalize micro-investment schemes with tax incentives to build trust and systemic support, especially for the high number of semi-urban workers. The managerial imperative is to shift from generic financial products to behaviorally adaptive, personalized solutions.

 

Conclusion

This study explored the behavioural investment approaches of gig workers and self-employed entrepreneurs in India, focusing on their saving practices, decision-making barriers, and openness to the Daily Incremental Investment Model (DIIM). Using responses from 390 participants, the analysis applied descriptive statistics, correlation, regression, and ANOVA to evaluate the influence of demographics, financial literacy, behavioural biases, and fintech adoption on financial behaviour.

The findings revealed that gig workers and entrepreneurs operate under irregular income streams, are heavily influenced by peer networks, and are constrained by loss aversion and overconfidence biases. Despite these challenges, there is strong openness toward adaptive and flexible models like DIIM. Education, income, and financial literacy emerged as the strongest predictors of disciplined investment, while fintech tools were effective only when combined with behavioural nudges such as daily reminders, progress tracking, and community-based challenges. Overall, the study demonstrates both the opportunities and limitations in advancing financial inclusion for irregular earners. It shows that behaviourally adaptive frameworks such as DIIM can bridge the gap between financial vulnerability and long-term security, transforming sporadic savings into structured habits.

 

Key Conclusions and The Potential of DIIM

The research establishes that to achieve financial stability for this workforce, interventions must be behaviorally adaptive. The Daily Incremental Investment Model (DIIM) emerged as a validated concept, with of respondents expressing willingness to adopt it. DIIM’s flexibility directly addresses cash-flow volatility and reduces the psychological barrier associated with large, fixed commitments, positioning it as a powerful bridge between financial vulnerability and long-term security.

 

Limitations and Future Research

The study is limited by its cross-sectional design and reliance on self-reported data, and its sample is concentrated in urban/semi-urban areas. Future research should prioritize the field implementation and piloting of DIIM with fintech partners to measure real adoption rates and long-term wealth accumulation. Further studies are also needed to explore how demographics moderate the effects of behavioral biases and to test the effectiveness of targeted literacy interventions.

 

 

CONFLICT OF INTERESTS

None. 

 

ACKNOWLEDGMENTS

None.

 

REFERENCES

Armansyah, R.F. (2021). Over Confidence, Mental Accounting, and Loss Aversion In Investment Decision. https://doi.org/10.21107/JAFFA.V9I1.10523

Go, T.A., Lim, V.N., Albao, M.O., & Baylon, D.P. (2020). Delving into Behavioral Finance: Uncovering the Linkage between Investment Preference, Investor’s Profile, Personality Traits, Risk Perception, and Investment Goals. People: International Journal of Social Sciences, 6, 18-39. https://doi.org/10.20319/pijss.2020.63.1839

Nkukpornu, E., Gyimah, P., & Sakyiwaa, L. (2020). Behavioural Finance and Investment Decisions: Does Behavioral Bias Matter? International Business Research. https://doi.org/10.5539/ibr.v13n11p65

Quang, L.T., Linh, N.D., Nguyen, D.V., & Khoa, D.D. (2023). Behavioral factors influencing individual investors' decision making in Vietnam market. Journal of Eastern European and Central Asian Research (JEECAR). https://doi.org/10.15549/jeecar.v10i2.1032

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