Building a Model on Influence of Behavioural and Cognitive Factors on Personal Financial Planning: A Study Among Indian Households

Abstract

Personal financial planning is an emerging area of interest, given the complexities and risks involved in investments and financial markets. As an extension to studies in the field of cognitive and behavioural decision theories, the role of financial cognition (FC) and mental accounting (MA) is examined as antecedents to personal financial planning (PFP) of Indian households and established as a model to represent the same. The study considers factors, that is, financial attitude (FA), risk attitude (RA) and financial knowledge (FK), to measure FC and uses the components of mental budgeting (MB), current income (CI), current assets (CAs) and future income (FI) to study MA as per the behavioural life cycle hypothesis. The study used a structured questionnaire to explore PFP in all the areas of financial interest, that is, cash flow management (CM), investment (INV), insurance (INSU), retirement (RP), tax (TAX) and estate planning (EP). The salaried individuals from different sectors are considered as samples for the study. The data have been collected from all-India representative samples. The analysis has been done with 359 responses from Indian households. The confirmatory factor analysis (CFA) has been used to establish and validates the measurement model.

Keywords

Mental accounting personal financial planning households financial attitude risk attitude financial knowledge

Introduction

The personal financial planning (PFP) process is increasingly gaining prominence, given the complexities in financial markets and availability of a range of financial products. PFP has become important from the point of view of investors’ awareness and preparedness to meet future financial needs. Given this fact, the study applies key concepts, that is, mental accounting (MA), the concept of behavioural finance and financial cognition (FC), a term that has developed from cognitive psychology, that influence PFP and attempt to build a model to represent Indian households’ PFP process.

The MA concept was first brought out by Richard Thaler (1980), who describes that MA is a process whereby people code, categorize and evaluate their economic outcomes. The detailed application of MA was given by Shefrin and Thaler (1988), in their behavioural life cycle hypothesis. Thus, they propose that people mentally divide their assets into current income (CI), current assets (CAs) and future income (FI); on the basis of that, they treat their account as non-fungible, and the marginal propensity to consume (MPC) of each account is different. The MPC is more for the CI account than CAs and it is almost zero when income is saved in the FI account; we also mention that the formation of MA helps in segregating the income based on the source and amount of inflow and we have earmarked the income as money spent or assets. As per the theory of MA, individuals formulated MAs for particular expenses and impose precommitment constraints to limit their expenses. Every individual does the same thing by controlling and prioritizing their expenditure. MA is the definition of various methods they undertake (Khajavi & Ghaemi, 2006).

Even though the financial decision-making process stems from the individual’s unique psychological structure, within this framework, individuals have very limited abilities to process all the available information. To manage the quantity and complexity of information, they use few mental shortcuts as the cognitive rule of thumb to simplify their financial decision-making process known as heuristics. The present study assumes that the cognitive rules of thumb of financial decisions get influenced by other cognitive factors which in turn influence individuals’ PFP processes. Importantly, the present article defines measures and tests the role of cognitive factors or psychological components that trigger financial thinking, in other words, the determinants of money usage by an individual. The term ‘FC’ considers three influencing components of cognitive financial decision-making, that is, financial attitude (FA), risk attitude (RA) and financial knowledge (FK).

Jana (2016) studies the behavioural financial concept of irrationality in investment decisions among Indian investors and pointed out the role of investors’ sentiments in the Indian stock market. Given the increasing relevance of behavioural economics in understanding decisions, this article analyses and presents a model to depict the relationship among MA, FC and PFP to indicate the influence of various behavioural and cognitive factors in the financial decisions-making process of Indian households. This study is the first of its kind to develop measures of MA and validate the same using appropriate statistical techniques and also introduce the concept of FC by combining two attitudinal factors, that is, FA and RA, and one informational factor, that is, FK. Conversely, the components of PFP have been taken from previous literature (Altfest, 2004; Koh, 2012).

Joshi and Sikdar (2015) use confirmatory factor analysis (CFA) to build a model to depict informal mentor characteristics. The study also uses CFA to establish the model, while the study develops the items of each scale and validates the same with reliability criteria, that is, Chronbach’s alpha and composite reliability and validity criteria, that is, convergent and discriminant validity (Fornell & Larcker, 1981).

To date no comprehensive academic study has been undertaken in India to determine the extent of influence of MA in PFP of Indian households. The present study identifies and presents a conceptual model proposing the relationship among MA, FC and PFP.

The rest of the article is organized as follows. The second section provides a review of past literature, the third section explains the study’s objectives and methodology adopted along with variables, the fourth section discusses the questionnaire design, data collection and analysis and the fifth section presents the conclusions, discussion, practical implications and study limitations.

Literature Review

Mental Accounting

MA is a sensitive process of bookkeeping that individuals practise to keep track of expenses and control their consumption (Thaler, 1985). As per the MA theory people subjectively frame the utility of a transaction in their mind which they expect to receive. Shefrin and Thaler (1988) mention that formation of MAs results in treating money as non-fungible, money in one MA is not a perfect substitute for money in another account. Heath and Soll (1996) described the impact of MA in investors’ perceptions towards their income and found that it influences individual saving and spending decisions and is helpful in restraining spending by allotting budget limits in certain categories. They also explore the notion of mental budgeting (MB) in relation to consumer decisions and describe the process of MB which affects individual behaviour and provides evidence of budget settings and expenditure tracking and concludes that budgeting affects consumption decisions.

Karlsson, Garling, and Selart (1997), in support of the behavioural life cycle theory, found that willingness to buy was higher when subjects used money from CI rather than from CAs. Selart, Karlsson, and Gärling (1997) highlight that regarding windfall receive as a lumpsum coded as CAs and windfall receive as increments per month coded as CI, consumption preference is more from CAs than CIs. Levin (1998) analyses that MPC is more for CI. Karlsson (1998) concludes that CAs are considered for future consumption, whereas preferences are given to CI account for short-term consumption.

Feldman (2010) provides real-world evidence in favour of MA models and describes its influence on saving for retirement. Ko and Huang (2012) study the MA influence on impulsive investment behaviour and evidence of disposition effect. MA is also helpful to individuals in planning their future financial needs as it influences individuals to become risk averse and save funds for future crucial matters and financial uncertainty in the present or future period or financial security at old age. To measure MA formation among Indian households the present study considers four concepts of behavioural life cycle hypotheses, that is, MB, CI, CAs and FI.

Financial Cognition

Cognitive perception and information processing are extensively studied in the domain of education and experimental psychology (Gringorenko & Sternberg, 1995). Bhandary et al. (2008) indicate that cognitive support is very important for investors as their psychological biases influence their investment decisions. Cools and Broeck (2007) define cognitive style as the way people perceive stimuli and use information to guide their behaviour. The present study focuses on this core principle of associative information processing and created the model of FC which includes FA, RA and FK of an individual.

Financial attitude: Eagly and Chaiken (1993) define attitude as ‘a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor’. Armitage and Conner (2001) conclude that the theory of planned behaviour showed a significant contribution of attitudes in forming intention, mutually with the subjective norm and perceived behaviour control. Godwin (1994) measures FA as an independent variable and predicts its high impact on cash flow management (CM). Shockey (2002) concludes that FA is the combination of concepts, information and emotions on learning, which together results in a readiness to react favourably. Joo and Grable (2004) mention that an individual’s financial behaviour is influenced by his/her FA, whereas Masuo et al. (2004) report that a family’s financial culture and financial socialization play an important role in structuring FA. Shim et al. (2010) indicate that individual financial learning through parents’ financial behaviour and individual financial literacy influence their FA. Fu et al. (2010) conclude that the understanding of the attitude of an individual helps in predicting intention and the behaviour of an individual.

Risk attitude: Kahnemonn and Teversky (1979) studied the difference in RA for gains and losses and found that investors are risk averse for gains and risk seekers for losses. Bromiley et al. (1992) observe the influence of particular situations and individual characteristics on risk-taking behaviour of individuals. Cicchetti and Dubin (1994) conclude that people with high income have more positive attitudes towards financial risk-taking than people with lower incomes. Weber, Blais, and Betz (2002) have developed an RA scale by considering five components of different domains, that is, financial decisions for investment and gambling, health and safety, recreational decisions, ethical decisions and social decisions, and support the domain-specific assumption of RA. Lusardi and Mitchell (2011) mentions that RA is an important factor which influences individual financial decisions and determines their portfolio choice and asset accumulation for retirement.

Financial knowledge: Various studies show a strong relationship between FK and households’ well-being. Bematzi and Thaler (1993) indicated the importance of basic FK in PFP. Chen and Volpe (1998) found that students with higher FK are more likely to keep financial records and select the correct financial choice compared to students with less FK. Hogarth and O’Donnell (1999) suggest that less FK results in less participation in financial markets. Less FK among households results in no retirement planning (RP) (Lusardi & Mitchell, 2007), borrowing at higher interest rates (Lusardi & Tufano, 2009) and acquiring fewer assets (Lusardi & Mitchell, 2007).

Personal Financial Planning

PFP has grown rapidly over the past centuries and it has established itself as a relatively important discipline in the area of finance. It is not only about how people should manage their finances, it also talks about how they should act, which gets misbalanced by cognitive errors, which normally occur in the absence of full knowledge, shortcomings in mental processing, weaknesses in perceptions and distorted perceptions. Palmer, Goetz, and Chatterjee (2009) report that the current uncertain economic environment developed the need of a financial planning culture in individual investors more than ever. Proper financial planning involves two things, that is, saving and asset creation for the future. The behavioural life cycle hypothesis developed by Shefrin and Thaler (1988) provides the theoretical framework for savings and asset creation. Individuals use some self-created rules such as restricting borrowing to certain types of purchases, paying off credit card bills every month or setting a goal to save a certain amount each month (Beverly, McBride, & Schreiner, 2003). Barberis and Huang (2001) analysed that investors are loss averse and apply narrow framing in their MA while investing in firm-level stock returns. Grinblatt and Han (2005) mentioned that individuals often get derived by MA formation while evaluating performance of stocks in terms of revenues and risks as they consider recent outcomes and performance of stocks while investing and make mistakes in long-term investments. Rockenbach (2004) analyses the relevance of MA for the pricing of option contracts. Hanna and Lindamood (2010) determine the theoretical model of economic benefits of PFP by combining three benefiting areas, that is, increasing wealth, preventing loss and smoothing consumption. Chase, Gjertson, and Collins (2011) mention that most personal saving researches are concerned about retirement savings than short-term financial uncertainty or emergency savings. Lusardi and Mitchell (2011) record the lack of emergency savings among many households in the USA. Altfest (2004) in his study describes six components of financial planning, that is, tax planning (TAX) to minimize tax burden, cash flow planning by creating some saving and spending policies, investment planning (INV) by deploying current resources efficiently to meet future requirements, risk management to reduce household exposures to uncertainty, RP for the life cycle period when income ceases and estate planning (EP) by distributing income and wealth among other family members. Koh (2012) prescribed eight types of financial plans based on financial goals, that is, money management plan (called budget planning or budgeting), savings plan, investment plan, liability plan, housing plan, insurance plan, retirement plan and estate plan. Murphy and Yetmar (2010) find that most respondents feel financial planning is important and are interested to prepare their own financial plans, but very few have the necessary knowledge and skills to do so.

The present study considers six components of PFP, that is, CM, INV, insurance planning (INSU), TAX, RP and EP.

The abovementioned literatures indicate the influence of MA and FC components on individuals’ PFP processes. But the literatures support the influence of each individual component on financial planning decisions. Perhaps, no studies have been done in India to understand the influence of both, that is, behavioural and cognitive components on individuals’ PFP process. The present study seeks to fulfil this gap. The study considers the role of both components behavioural, that is, MA, and cognitive, that is, FC, of an individual in influence of PFP decisions of Indian households. The study also establishes the model by using the structural equation model (SEM) technique in Amos 3.0 and has verified the model by identifying desirable limit of the goodness of fit indices (GFIs) through CFA.

By considering the abovementioned studies in support of the fact that individuals’ decisions on savings and expenses are influenced by their own created behavioural and perceived cognitive traits, the main objective of the study is to build a model by combining all the mentioned individuals’ traits that influence their PFP.

Scope of the Study

Investing might be a simple activity but achieving one’s financial objectives poses all the challenges. Understanding the behavioural and cognitive antecedents adds to the decision-making literature and helps understand the diligent PFP decisions. The present study analyses and links the concepts of individual behavioural and cognitive factors that influence their PFP. The study focuses on the core principle of associative information processing and explains the model of FC which includes FA, RA and FK of an individual; the behavioural component, that is, MA, includes MB, CI, CA and FI and the components of PFP.

Objective of the Study

The main objective of the model is to create the bare minimum structure to demonstrate the influence of FC and MA on PFP of Indian households. The study mainly tried to explore the fundamental principle of subjective thinking towards financial decisions. The model may not be the exact representation of reality but can simplify the understanding of a diverse array of people’s financial decisions and can be helpful in exploring the financial information process.

Data Collection and Sample Profile

Selection of the respondent in the sample is based on the assumption that the earning members of the family are mainly involved in the financial planning process, irrespective of whether they are males or females. The present study uses a purposive sampling technique. The sample individuals are selected mainly from four sectors which is pan-Indian, that is, defence, banks, pharmaceuticals and IT companies from the Hyderabad–Secunderabad region. A structured questionnaire was created and the hard copy of the questionnaire was distributed to 525 respondents. A total of 390 respondents reverted back with their opinion. A total of 31 forms contained missing values; therefore, the analysis was done with 359 responses. Out of the total sample almost 63 per cent of respondents are male. A cumulative of 88 per cent falls between the age group 25–45. Approximately half (55.2 per cent) of the total respondents are post-graduates and work in private sectors (64.9 per cent). Nearly 64.9 per cent of the total sample have a family income between 0.6 and 0.9 million.

Analytical Framework and Results

The initial refinement of the scale was conducted through the interactive process of explorative factor analysis (EFA) and reliability tests. A total of 13 factors emerged after refinement. Principal component analysis was done to extract factors and Promax with Kaiser normalization method used for rotation. EFA was found to be satisfactory with each factor loading greater than 0.4. A greater than 0.6 of Chronbach’s alpha value was considered to have good internal consistency (Hair, Anderson, Tatham, & Black, 1999).

Confirmatory factor analysis: The study uses CFA by using Amos 2.0 to test the proposed hypotheses. In order to check the model fit, the study considered various model fit measures. To report the goodness of fit measures, five indices, that is, CMIN, GFI, adjusted GFI (AGFI), comparative fit index (CFI) and the root mean square error of approximation (RMSEA), were considered based on the guidelines given by various researchers (Kline, 2005; McDonald & Ho, 2002). The acceptable values of different goodness of fit measures have been recommended by different authors. The CMIN is the value of chi-square divided by degree of freedom. Hair, Anderson, Tatham, and Black (1999) suggested a value between 2.0 and 5.0 for close fit model.

Bentler (1990) first introduced the concept of CFI and it is least affected by sample size; he recommended that a value greater than 0.90 is essential to make it certain that incorrectly specified models are not considered. Tabachnick and Fidell (2007) mentioned that the GFI is an alternative to the chi-square test and calculated the proportion of variance by the estimated population covariance. MacCallum and Hong (1997) suggested that GFI decreased with a large degree of freedom and increased with both more parameters and large samples and a value of 0.90 has been recommended for the GFI.

The AGFI adjusts the GFI based on degrees of freedom (Tabachnick & Fidell, 2007). Diamantopoulos and Siguaw (2000) described RMSEA as the most informative fit index to check model fit, and with parameter estimates and population covariance matrix he suggested a value between 0.05 and 0.08 for a reasonably accepted model.

Figure 1.

Source: The authors.

At the first stage of the CFA analysis, the values of model fit indices (i.e. Model 1) are close but not at the satisfactory level of acceptance for all GFIs. As CMIN value is 1.737, CFI = 0.887, GFI = 0.825, AGFI = 0.796 and RMSEA = 0.045 with PCLOSE = 0.987. To further increase the values of fit indices and derive a close fit model, few manual methods of improvement of the model have been done.

To increase the value of goodness of fit measure, the study uses manual method of analysis in AMOS, First, the values of error term covariance are considered and a covariance line has been drawn between the higher values (see figure 2) and the analysis is done again in AMOS, and the model specified as Model 2. The values of fit indices are further improved for a close fit model. The CMIN value decreased to 1.699.

However, the values of GFI and AGFI are also still less than 0.9. However, there is not much change in the RMSEA value but PCLOSE increased to 0.931. As the CFA (Model 2) also shows below-satisfactory GFI values, the items with less-standardized regression weight (below 0.45)¹

The study considered standardized regression weight below 0.45 for deletion of items to increase GFIs in order to retain maximum items for measuring a variable.

have been deleted to reach a satisfactory value of GFIs. In this process the study deleted one time in EP, that is, EP4, with regression weight 0.44. After deletion of the item, again, the analysis was run in AMOS to check the further increase in values of all fit indices. The CMIN value decreased to 1.664, CFI = 0.904, GFI = 0.842, AGFI = 0.814 and RMSEA = 0.043 with PCLOSE = 0.999. All GFI values are considered to be reasonably satisfactory for a close fit measurement model (Joshi & Sikdar, 2015). The study considers Model 3 as the measurement model for further analysis.

Figure 2.

Source: The authors.

After satisfying the model fit indices the study also checked the validity and reliability of the construct. For validation of the model the study considered three types of required validation methods, that is, content validity, convergent validity and discriminant validity (Campbell & Fiske, 1959). The content validity was measured by taking experts’ opinion about the items of the construct. The questionnaire was sent to selected subject experts and after considering expert judgement about the items of the measuring variables, the questionnaire was distributed among subjected respondents.

The average variance extracted (AVE) values for all the factors were greater than 0.50 and satisfied convergent validity. The discriminant validity was satisfied by checking square root value of AVE. The square root of AVE was greater than the correlation among the representative variables. The composite reliability describes the internal consistency of the measurement model. Bagozzi and Yi (1988) provide a value greater than 0.60 as the benchmark of composite reliability. The composite reliability of the present measurement model varies from 0.75to 0.90, which is above the benchmark limit.

Figure 3.

Source: The authors.

Conclusion

The present study analyses and empirically proves the model of PFP of Indian households which include one behavioural component, that is, MA, and one cognitive component, that is, FC. The study first used principle component analysis and grouped the items into different factors. The use of CFA helped in establishing the measurement model of MA, FC and PFP. The results of CFA of different fit indices were found to be satisfactory. Further studies validate the model by considering construct reliability and discriminant validity.

The result establishes that FC and MA influence the PFP process of an investor, reinforcing the role of psychological processes that drive financial decisions. The elements of FA, RA and FK would help in the formation of MA with a focus towards MB, CI, CA and FI, which further influence money usage for current and future needs.

The study would be useful to investors to check and control their self-driven MA process and would be helpful towards understanding investors’ preferences. As the model represents the behavioural and cognitive influence of individuals’ PFP process, the study would be useful to banks, mutual funds and other financial institutes in profiling their customers and developing products based on the characteristics of targeted customers. The study would also be useful in understanding the dimensions while choosing various INV, INSU and RP products of individual investors. The presentation of the model can help banks and mutual fund companies introduce new schemes/products by capturing the CA and FI focus of Indian households and also can consider their FA and RA influence in their saving and investment decisions. Banks can take initiatives to improve FK of Indian households which will in turn improve their PFP process. The financial institute can introduce small investment tools for RP-orientated products, where individuals can invest their CI-/MB-focused income which can result in increased savings that further extend to long-term investments.

The study would also be useful to financial professionals and advisors in understanding customer perceptions and attitudes for profiling their customers and provide a fine-tuned understanding of saving and expenditure patterns, when looked at through the MA and FC lens. The results establish that MA influences the PFP process of an individual household and reinforces the role of cognitive processes that drive financial decisions.

Limitation

The model has been tested in limited data set in Indian context. The further validation of model can be done in a different set of samples with large number of data. The model can further be tested in different populations of other developed and developing countries where mindsets of people are considered to be different from Indians.

Annexure 1:

Operational Definitions of the Variables

Variables	Definition
MA	A cognitive process of segregating incomes based on the source and extent of receipt, which generates account-based spending habits, and each component would display a different propensity to consume.
MB	The process of mental segregation of money by providing a particular budget to each type of expenditure, aimed as a mechanism to control spending.
CI	The MA component with a cognitive focus on incomes that are regularly received or expected to be regularly received.
CA	The MA component deemed as savings (excess of income over expenditure) or as accumulated savings of the past, which could as well be used to meet current consumption requirements.
FI	The component with cognitive focus on income expected to be received at a future point in time or current savings that generate income for future.
PFP	The process of planning finances for current and future requirements and implementing the plan as a course of action in an efficient manner.
CM	The process of managing inflow and outflow of money in a calculated manner.
INV	The process of employing money in different short-, medium- or long-term avenues with an expected rate of return.
INSU	The process of preparing for future financial uncertainties as a cover against risks through investing in various insurance schemes/policies.
TAX	The process of financial planning to reduce tax burdens by investing in tax-saving plans and in those schemes that yield tax advantages.
RP	The process of preparing for financial security at old age through making appropriate investments in pension plans and other financial products that give retirement solutions.
EP	The process of planning for distribution of wealth among future generations by writing a will.
FC	The influence of individuals’ beliefs, attitudes and actions towards financial thinking based on certain assumptions.
FA	Mental disposition towards the application of financial principles to manage finances.
RA	Individuals’ positive or negative state of mind towards the probabilities of taking risks in given financial uncertainty.
FK	Individuals’ understanding about various financial concepts, terms and products and awareness about various financial risks and opportunities to make informed financial decisions.

Source: The authors.

Annexure 2:

Variable Name	Initial Items	Items After Scale Refinement
MB	9	5
CI	4	2
CA	6	4
FI	6	3
CM	7	5
INV	6	4
INSU	6	5
TAX	7	3
RP	5	3
EP	6	4
FA	13	3
RA	9	3
FK	8	3
Total	92	47

Source: The authors.

Footnotes

Acknowledgement

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Declaration of Conflicting Interests

The authors declare no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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