Integrating affect, cognition, and culture in Hispanic financial planning

Suri Weisfeld-Spolter (Nova Southeastern University, Fort Lauderdale, Florida, USA)
Fiona Sussan (Akita International University, Akita-City, Japan) (University of Phoenix, Tempe, Arizona, USA)
Cindy Rippé (Flagler College, Saint Augustine, Florida, USA)
Stephen Gould (Baruch College, The City University of New York, New York, New York, USA)

ISSN: 0265-2323

Publication date: 21 May 2018

Abstract

Purpose

Debt is at a peak and consumers purport needing help with financial planning. To better understand the antecedents of financial planning behavior, the purpose of this paper is to examine the importance of cultural values in financial decision making within the context of Hispanic American consumers. A new conceptual model is proposed to integrate affect (cultural value) and cognition (financial knowledge) in financial planning.

Design/methodology/approach

To uncover respondents’ views on cultural values, financial knowledge, financial attitude, and financial planning behavior, an online survey hosted on a business school’s website was distributed to members of two Hispanic Chambers of Commerce. The survey consisted of five parts, and took each respondent an average of 15 minutes to complete. The final data set has 158 observations.

Findings

Results analyzed using structural equation modeling confirmed the hypotheses that financial knowledge, attitude, and perceived control simultaneously influence Hispanic consumers’ intentions to purchase financial planning products or services. More interestingly, these results confirm that multiple different routes coexist in the decision-making process, especially within the Hispanic financial planning context.

Originality/value

Key contributions of this paper include the conceptualization of cultural value as an antecedent to Hispanic financial behavior; detailing the different routes to financial decision making for US Hispanic consumers; and informing financial service managers on marketing strategies toward Hispanic consumers.

Keywords

Citation

Weisfeld-Spolter, S., Sussan, F., Rippé, C. and Gould, S. (2018), "Integrating affect, cognition, and culture in Hispanic financial planning", International Journal of Bank Marketing, Vol. 36 No. 4, pp. 726-743. https://doi.org/10.1108/IJBM-09-2017-0201

Publisher

:

Emerald Publishing Limited

Introduction

More than half of Americans are living paycheck to paycheck and household debt has risen to $12.68 trillion in 2017, a peak that has not occurred since the Great Recession of 2008 (Fortell, 2017). In total, 58 percent of Americans believe that their financial planning efforts need improvement (Northwestern Mutual, 2015). Record high credit card and student loan debt combined with lack of savings for emergencies and retirements (Board of Governors of the Federal Reserve System, 2017) have necessitated a need for a better understanding of financial planning. As financial planning falls under the domain of financial well-being, it is important for marketers in wealth management to conduct research about consumer choice in areas such as insurance preference and investment portfolios, among others (Bruggen et al., 2017). Thus far, research in financial planning has focused on an individual’s financial knowledge or financial literacy as the main antecedent to future-oriented financial planning. In other words, prior research has mainly viewed whether a consumer makes financial plan for their future (e.g. retirement, college education) or not as a function of how well they are informed about financial products (Fernandes et al., 2014; Robb and Woodyard, 2011; Wang, 2009; Xiao et al., 2011). Some research investigated the emotional aspects of consumers in their financial decision making (Xiao et al., 2011) or in their financial well-being and satisfaction (Xiao and Porto, 2017) While one’s knowledge about financial products and one’s emotional status are important factors, these works have overlooked a person’s value system that might impact their future-oriented decision about financial planning. Much of the planning toward future financial health in retirement, college education, and life insurance rests on how much one values their own immediate and extended family. Missing from the extant research is an investigation that captures the cultural values as an antecedent to making future-oriented financial planning. In order to fill this gap, this present research conceptualizes cultural values as affect, knowledge as cognition, and investigates the co-contributing routes of cognition and affect to consumers’ financial planning behavior. While cultural values are often investigated as antecedents to consumer decision making for services (e.g. Leo et al., 2005; Mattila, 1999), they are seldom included in explaining decisions toward financial services. Investigating cultural values in financial behavior is particularly important in the USA. The USA is a very diverse country with many ethnic groups. These ethnic groups are laden with diverse cultural values. One such group is Hispanic Americans. Hispanic Americans are the largest and fastest growing ethnic group in the USA. In 2010, one in every six Americans is Hispanic (Ennis et al., 2011). From 2000 to 2010, Hispanic Americans had a 43 percent increase in population (Ennis et al., 2011), and is expected to grow 167 percent from 2010 to 2050 (Lowrey and Taylor, 2014). There is an urgent need for financial service managers to understand this ethnic group and their financial behavior as there are limited conclusive research findings other than that they are reported as acting differently than non-Hispanic groups (Plath and Stevenson, 2005; Wang and Hanna, 2007; Watchravesringkan, 2008; Yao et al., 2005). There is a lack of research that has investigated the causes of why this group acts differently than non-Hispanic consumers in the USA when it comes to their choices of financial services. One of the most distinguishing aforementioned features of Hispanic culture is its collectivist and family-centric cultural value, known as familism, and it is the main driving force behind their consumer behavior (Villarreal and Peterson, 2009). Familism is the most important value in the Hispanic culture when compared to other cultural values (Villarreal and Peterson, 2009). Because of this cultural value, many Hispanic Americans have extensive strong family ties and multigenerational relationships. As a result, financial matters are often viewed as family matters that are influenced and supported by parents, grandparents, and relatives (Lowrey and Taylor, 2014). As it is common in financial behavior research to view a family or household as a unit of measurement (Plath and Stevenson, 2005), it is appropriate in this paper to investigate familism as an important factor in Hispanic consumers’ financial planning. This paper proposes a new conceptual framework in which the cultural value of familism is added as an antecedent to financial decision-making behavior. In essence, the new framework conceptualizes the dual processes of cognition (expressed in financial knowledge) and affect (values on familism) co-contributing to financial planning decisions (theory of planned behavior: attitude, perceived, control, and behavior). This new conceptual framework differs from previous work and adds new knowledge to previous works in two areas: it adds cultural value as an antecedent to financial behavior, and by simultaneously measuring affect and cognition of the financial decision-making process, it adds nuance to the extant research that only focused on the impact of financial knowledge on financial behavior. The conceptual framework hypothesizes the following causal links: cultural values to both attitude and perceived behavioral control; financial knowledge to both attitude and perceived behavioral control; and financial knowledge, attitude, and perceived behavioral control concurrently linked to financial planning behavior intention. The empirical causal model is estimated using structural equation modeling (SmartPLS 2.0) based on survey data collected from members of Hispanic Chambers of Commerce on the East Coast, with results supporting most of the hypotheses. The remainder of the paper begins with a literature review of a discussion of the cultural value of familism (affect) among Hispanics, cognition in decision making, and cognition and affect routes to financial behavior, followed by hypotheses development. Data collection and empirical measurement are then presented with results and discussion, and concluding remarks address the theoretical contributions and managerial implications. Literature review and hypotheses development This section begins with a review of works related to affect in the form of familism within the Hispanic context, followed by literature that focuses on cognition in financial decision making. A summary of representative literature relevant to this research is listed in Table I. Based on the literature, hypotheses development is presented. Cultural value of familism and Hispanics decision making Familism impacts a person’s decision-making process in that family members or family priorities have a dominant influence on one’s own psychological process: the greater degree of familism, the more influenced individuals are by their spouses and children (Villarreal and Peterson, 2009). Familism is the most important value in the Hispanic culture when compared to other values of simpatia, respeto, fatalism, and machismo, and it is also the most prominent one that distinguishes it from non-Hispanic culture (Villarreal and Peterson, 2009). This sustainable and cultural value does not vary from country to country and is not subject to dilution over generations or acculturation among all Hispanic groups (Villarreal and Peterson, 2009). The relationship between cultural value and decision-making behavior is as follows. Cultural value is an affect and is an antecedent to attitude within the context of the theory of planned behavior. Affect differs from attitude in that affect is a subjective response while attitude is a summary evaluative judgment of an object (Malhotra, 2005). Extending this to the cultural value of familism, the role that familism plays in the decision-making process falls under the domain of the value-attitude-behavior hierarchy, which is salient across all cultural groups, but particularly so in Hispanics (Gregory et al., 2002; Gregory and Munch, 1997). In other words, there is strong evidence that Hispanics include their value system in their decision making, and tend to link their value to their decision-making process more so than groups from other culture. Another source to understand Hispanics and the role cultural values influencing their decision making is from cross-cultural studies. When investigating the linkage of cultural values and a person’s perceived control (locus of control, self-efficacy, and control of external events), it was found that people from collectivist culture have less perceived control than people from individualistic culture (Ji et al., 2000). People with Hispanic origins are viewed as being from a highly collective culture (Ji et al., 2000). However, a contrary argument was also made that people from collectivist cultures tend to place more importance on their relationship interdependencies with their group and so they tend to exercise more self-control when making financial decisions (Pirouz, 2009). Nevertheless, across various ethnic groups, locus of control is positively correlated to better financial behavior (Grable et al., 2015). Affect and financial planning From the evidence of strong value-attitude-behavior hierarchy among Hispanic consumers coupled with a salient cultural value component of familism in their decision-making process, we propose that the role of familism will extend into influencing the financial planning situation. The rationale is that as financial planning is a commitment that aims to protect one’s family and family assets, it is likely Hispanic consumers value their family highly, thus leading to their favorable attitude toward engaging in future-oriented financial planning that will protect the future of their families. Moreover, given that cultural values are linked to perceived control with the more control the better financial behavior, we believe that for Hispanic consumers, familism acts as a control mechanism that positively impacts their financial planning behavior. More formally, we hypothesize: H1. There are two routes that cultural value of familism impacts Hispanic American financial planning decisions: (a) familism positively impacts attitude and subsequently impacts intention to purchase financial products; and (b) familism positively impacts perceived control and subsequently impacts intention to purchase financial products. Cognition and financial decision making In decision making, knowledge represents a major cognitive or rational component (Loewenstein and Lerner, 2003). As a cognitive effort, financial knowledge directly impacts financial behavior. Financial knowledge impacts a consumer’s risk-taking behavior (Wang, 2009), and correlates with financial planning practices through areas such as risk management of life, health, and auto insurance; emergency funds; credit reports; overdraft; credit card payoff; and retirement accounts (Robb and Woodyard, 2011). Most research findings support the notion that the more knowledge individuals have about a financial product or service, the more effective they will be in using the financial product or service, whether it is credit card borrowing, saving for retirement, financial planning, or money management (for a review, see Robb and Woodyard, 2011). The financial literacy literature also confirms that financial knowledge is a mediator between financial education and financial satisfaction (Xiao and Porto, 2017). An exception to this positive correlation between knowledge and behavior is in the case of college students. Robb and Woodyard (2011) found that more knowledge about credit cards is associated with a higher debt balance, whereas Xiao et al. (2011) demonstrated that knowledge does not impact debt balance one way or another. Similarly, Tang et al. (2015) found a weak association between financial knowledge alone and behavior in young adults. Indeed, Huston’s (2010) meta-study of financial literacy concluded that financial knowledge itself might not improve financial behavior due to cognitive biases and self-control problems, among others. Financial knowledge has two dimensions: objective knowledge, defined as what a person actually knows, and subjective knowledge, defined as the degree of confidence a person has in his or her own knowledge (Robb and Woodyard, 2011). Research results show that the impact of objective and subjective knowledge on financial behavior is mixed. Sivaramakrishnan et al. (2017) found that only objective financial knowledge influences financial behavior in a study of more than 500 retail investors in India. Other researchers found among consumers in the USA that subjective knowledge has a larger impact than objective knowledge in financial planning practices (Hadar et al., 2013; Robb and Woodyard, 2011) and in mutual fund investment (Hadar et al., 2013; Wang, 2009). More specifically, in mutual fund investment, Wang (2009) found that individuals with higher subjective or self-assessed knowledge are more likely to take more risk than people with less subjective knowledge. Similarly, in financial planning, Hadar et al. (2013) found that consumers who have more confidence in their own knowledge engage more in positive financial planning behavior than consumers having less confidence in their knowledge. Financial knowledge and attitude toward decision making Financial knowledge is also an antecedent to attitude toward financial behavior. In other words, the impact of financial knowledge on financial behavior can be mediated through attitude (Xiao et al., 2011), with attitude being a major component of the Fishbein TPB model. Attitude, while simply defined as a summary evaluative judgment of an object, has evolved into multiple interacting relationships with affect and cognitive resulting in cognitive attitude, affective attitude, and conative attitudes (Malhotra, 2005). Researchers have various definitions of financial attitude. One interpretation is that financial attitude is an evaluative response toward a particular financial behavior, such as risky credit card borrowing (Xiao et al., 2011). Another interpretation is that financial attitude is described as one’s subjective confidence in one’s financial knowledge of financial planning (Robb and Woodyard, 2011). According to Plath and Stevenson (2005), financial attitude is not the same as money attitude; money attitude is a belief about what money symbolizes and its perceived value. They reported that, based on money attitude, only two of the four dimensions (power, time, anxiety, and quality) explained a Hispanic American household’s financial assets portfolio. They also reported that Hispanic Americans are more short term in their investments when compared to non-Hispanic White Americans. While there is no convergence in the definitions of financial attitude, research findings converge to link financial knowledge positively to financial attitude, with more knowledge leading to a more positive attitude. Fry et al. (2008) reported that an increase in knowledge about savings eliminated pre-existing negative attitudes toward saving for retirement. More knowledge about credit cards improves one’s attitude toward borrowing through credit cards (Xiao et al., 2011). Xiao et al. (2011) concluded that college students internalized their subjective knowledge and incorporated it into their positive attitude toward credit card usage. Financial attitude was more relevant than financial knowledge in impacting financial planning (Robb and Woodyard, 2011). Financial knowledge and perceived control There are two dimensions of perceived control when it comes to financial behavior: internal (self-efficacy) and external (controllability) (Xiao et al., 2011). They found that students who reported higher self-assessed credit card knowledge were more likely to perceive themselves as being able to control (both internal and external) their risky credit card spending. Similarly, Perry and Morris (2005) found that a locus of control mediated financial knowledge in consumers’ likelihood to save, budget, and control spending. In a multiple cultural group setting, researchers found that the higher the locus of control, the better their financial behavior (Grable et al., 2015). Based on prior works that confirm the direct positive impact of consumers’ financial knowledge to their financial behavior that includes mutual fund risk-taking investment, financial planning, and others; the positive impact of financial knowledge to both attitude; and knowledge to perceived control leading to financial behavior, we propose that these relationships between financial knowledge and financial behavior also exist among Hispanic American consumers. More formally, we hypothesize: H2. There are three routes from financial knowledge to financial behavior among Hispanic American consumers: (a) financial knowledge directly and positively impacts intention to engage in financial planning; (b) financial knowledge positively impacts attitude, and attitude, in turn, impacts intention to engage in financial planning; and (c) financial knowledge positively impacts perceived control, and perceived control positively impacts intention to engage in financial planning. Concurring affect and cognition routes to financial decision making Extant literature has examined cognition/affect, mind/hearts, thinking/feeling, conscious/unconscious, system 2/system 1, cold/hot dichotomous routes to economic decision making (Alos-Ferrer and Strack, 2014; Kahneman, 2011), they converge toward the consensus that like other decision-making situations, consumers use dual processes of cognition and affect in making decisions (Schaller and Malhotra, 2015). In some high-stake scenarios (hormone replacement therapy), researchers found both affective and cognitive components leading to consumers’ attitudes and intentions toward their decisions (Schaller and Malhotra, 2015). However, in financial decision-making research, affect is used as an endowment effect prior to decision making. In Lerner et al. (2004), they manipulated affect (sadness in watching a movie clip) prior to asking individuals to set a selling price for a product and found the group that was exposed to sadness resulted in setting lower selling prices when compared to the group that was exposed to a neutral emotion movie clip. Their dual process of affect and cognition was sequential and did not occur at the same time. A more nuanced suggestion is that evaluative judgment (i.e. attitude) could be preceded by both low-level affective and low-level cognitive processes (Malhotra, 2005, p. 480). In extending prior works and their results to the evaluation of various future-oriented financial services such as financial planning, one can argue that it is possible that consumers may possess limited to extensive information about a specific financial product, and that they simultaneously have some weak or strong affective feelings (positive or negative) toward their spouses or children, thus resulting in both low- or high-level thinking and feeling funneling into their evaluative judgment of financial planning services. This leads us to the following hypothesis: H3. Both affect and cognition have concurring impact on financial behavior: (a) affect in the form of cultural value of familism positively impacts attitude, and attitude subsequently impacts Hispanic American consumers’ intention to engage in financial planning; and (b) cognition in the form of financial knowledge positively impacts attitude, and attitude subsequently impacts Hispanic American consumers’ intention to engage in financial planning. The hypothesized relationships of affect, cognition, and financial behavior are depicted in Figure 1. Methodology Data collection and sample As Hispanic Americans are a highly sought group of consumers by financial service industries for their economic importance and demographic growth in the USA, financial institutions often invite experts from higher education institutions and collaborate with local Chambers of Commerce to conduct research. This study was conducted under such a triadic industry-university-community collaboration. In this collaboration, the Hispanic Chamber of Commerce in Palm Beach County, the Puerto Rican/Hispanic Chamber of Commerce for Palm Beach County, a nationwide insurance company, and a business school at a Florida university collected survey data from Hispanics living in South Florida. Florida is one of the top three states with the largest Hispanic population (Ennis et al., 2011). A survey questionnaire was developed based on established instruments from prior literature (Ajzen, 2002; Robb and Woodyard, 2011; Villarreal and Peterson, 2009; Wang, 2009; Xiao et al., 2011). The online survey was hosted on the business school’s website. The researchers collaborated with the aforementioned Chambers of Commerce and sent e-mail invitations to members of these organizations to solicit their participation in the survey. More than 500 e-mails were sent to members in these two Chambers of Commerce. The survey consisted of five parts, which took each respondent an average of 15 minutes to complete. Each participant signed a consent form to comply with the Institutional Review Board for human subject approval purposes. There were 169 respondents who participated in the survey. In total, 11 cases with missing data appeared to be random and were removed from the analysis. The final data set has 158 usable observations. Measures The following constructs were measured: cultural values (VALUE), subjective financial knowledge (KNOWLEDGE), attitude (ATTITUDE), perceived behavioral control (CONTROL), and financial planning intentions (INTENTION). The indicators used for each construct are listed in Table AI. For VALUE, we asked respondents about the relative importance of their family. They were asked to indicate their agreement or disagreement with three statements: I always help family members when they are in trouble; the welfare of the family is more important than my own; and I expect to care for my parents financially when they are older. The scale ranged from 1 (strongly disagree) to 7 (strongly agree). The measurement is similar to those from Villarreal and Peterson (2009). To measure KNOWLEDGE, we provided the survey participants with three financial services and asked them to indicate how knowledgeable they were in each area using a five-point Likert scale, with 1 representing not at all knowledgeable and 5 representing extremely knowledgeable. In our survey, we asked for knowledge about three products: life insurance, retirement planning, and college savings. These questions are similar to ones used in prior work (Robb and Woodyard, 2011; Wang, 2009; Xiao et al., 2011). To measure ATTITUDE, we provided respondents with a list of statements and asked them to agree or disagree using a seven-point Likert scale, with 1 representing strongly disagree and 7 representing strongly agree. We asked respondents the following three statements: Financial planning is one of my top priorities; I know that developing a financial plan will increase my success; and I wish to know more about how to plan for my financial future. These questions are adopted from Ajzen (2002). Respondents were asked the following three statements for CONTROL: I feel confident selecting investment alternatives to meet my goals (e.g. IRAs or cash deposits); I am doing a good job of preparing financially for my retirement; and I wish I were more in control of my finances. Similar perceived financial control measurement has been used in Xiao et al. (2011). For INTENTION, three items that correspond to the items asked about KNOWLEDGE were used: life insurance, retirement planning, and college savings. Again, respondents were asked to indicate their interest in purchasing these using a five-point Likert scale, with 1 being not at all interested and 5 being extremely interested. These questions are adopted from Ajzen (2002), which were also used in Xiao et al. (2011). Demographic variables Table II depicts the demographic analysis of the overall sample. The age of respondents ranged from 25 to 69 years, with the average person being 44.3 years old. The majority of respondents were well educated, with 72 percent having obtained a Bachelor’s degree or higher. In fact, 22 percent of respondents had a graduate degree. On ethnicity distribution, 80 percent of our respondents were originated from either a Caribbean island, Puerto Rico, or South America. As for marital status, 73 percent of our overall sample was married and 81 percent had dependent children. In total, 42 percent of our respondents said they had parents and other family members to support them. Slightly more men (55 percent) filled out the survey than did women. We asked respondents to indicate their generation level and found that almost half of them (49 percent) were not born in the USA, classifying them as the first generation in the USA. In all, 43 percent were born in the USA and are classified as second generation, whereas only 8 percent had parents and grandparents born in the USA, classifying them as third generation. In our models, we included income groups and education as control variables. The majority had an annual household income of greater than$100,000.

The classification of income group follows that of Plath and Stevenson (2005).

Data analysis

As our data are collected from same-respondent replies, we took the following steps to address the common method bias that may arise from such data set. We follow the guidance of Podsakoff et al. (2003) and Chang et al. (2010) to address this issue. First, we reported the correlation matrix of all the survey questions in Table III. Following that we used SPSS 24.0 and conducted two ex-post tests: first, Harman’s one-factor test and found that not one single factor consists of more than 50 percent of the total variance, suggesting that the variables used in this study did not fall into one factor. In other words, the items were not one-dimensional and measuring the same thing. Table IV reports such results. We further conducted principal components analysis (without and with rotation) and confirmed that there are more than one factor in our data set. The Bartlett’s test at 0.85 (p=0.00) confirmed that there are more than one factor since the suggested criterion is 0.60. Table V reports the results of this test. The scree plot in Figure 1 further suggests that there are four or five components, with the total variance and component matrix presented in Table VI suggesting four components after rotation. Furthermore, from an ex-ante approach to address common method bias, our survey questionnaires consist of varied Likert scales with some questions asked in the range of 1-5 and some questions asked in the range of 1-7, with some questions being asked in reverse scale and re-coded subsequently for analysis (Figure 2).

We used partial least squares (PLS) implemented in SmartPLS 2.0 (Ringle et al., 2005) for its robustness to violations of data normality regarding distribution or measurements, a condition commonly found in behavioral studies (Hair et al., 2012) including this study. Because PLS uses partial or one part of the model being estimated at a time, it has large statistical power for a smaller sample size under 200 observations (Hair et al., 2012). Our data set contains 158 observations, in line with the 180 average sample size reported using PLS in business research (Hair et al., 2012). PLS is also selected because of the discovery-oriented and theory-primitive nature of this paper (Wold, 1985, p. 589).

PLS comprises two levels of analyses: the measurement model and the structural model. The former analyzes the relationship between the latent variables (LVs) and their indicators. The structural model analyzes the relationship between LVs. For our models, we have three paths leading to the dependent variable, with 3 being the highest number of indicators within a LV. Our sample size for the model therefore satisfies the ten-times rule, which recommended that sample size be either ten times the largest number of paths (indicators) leading to any LV in the outer model, or the number of paths leading to the dependent variable (Hair et al., 2012). The LVs – that is, VALUE, KNOWLEDGE, ATTITUDE, and CONTROL – in the model are reflective. INTENTION is the dependent variable.

Estimation results for the measurement or outer model

For PLS analysis, we use the default algorithm settings in SmartPLS version 2.0, which uses a uniform value of 1 as an initial value for each of the outer weights, with the path weighing scheme with stop criterion being the sum of the outer weights’ changes between two iterations <10−5, and the maximum number of iterations set at 300. In reporting the results of the reflective outer model, we follow the Hair et al. (2012, pp. 429-430, Table V) recommendations for PLS, namely, indicator reliability, internal consistency validity, convergent validity, and discriminant validity. The majority of the standardized outer loadings of indicators in various LVs in the model are larger than 0.70, except for four indicators loaded between 0.60 and 0.70.

Xiao et al. (2011) included indicators with loadings below 0.50. Table VII lists the loadings of indicators onto their respective latent constructs for the model.

For internal consistency and reliability, we report in Table VIII the composite reliability (CR) of the LVs in Table IV. CR is the appropriate measure to report instead of Cronbach’s α, and the recommended value of CR is larger or equal to 0.70 (Hair et al., 2012). The CRs of all LVs for the models are above 0.70. The table reports the convergent validity of our constructs by means of average variance extracted (AVE). A minimum of 0.50 is required (Hair et al., 2012), and all our constructs AVEs are above 0.50. We use both the Fornell-Larcker criterion and cross-loadings to report discriminant validity. For the former, the square root of each construct’s AVE should be larger than the correlation with any other construct. In our results, none of the correlations with any other construct is larger than the square root of each construct’s AVE. We also confirmed that no indicators cross-loaded on another construct. In other words, each indicator has the highest correlation with its own construct than with any other constructs. For brevity, we did not report these numbers.

Estimation results for the structural or inner model

For the analysis of the inner model, we use the bootstrapping procedure in SmartPLS 2.0. We use the original number of observations (158) as the number of cases, and we use a sample of 1,000 for each model. Our reporting follows the recommendations of Hair et al. (2012). Figure 3 reports the R2, signs and the significance of path coefficients, and Table VIII reports the path coefficients, standard errors, and t-statistics of the model and summarizes the hypotheses’ test results. H1a is supported, meaning value positively and significantly impacts attitude (path from value to attitude) and subsequently impacts intention (path from value to attitude to behavior intention). This is also true for H1b in which value positively and significantly impacts control (path from value to control) and subsequently impacts intention (path from value to control to intention). H2a is supported in that knowledge directly impacts intention positively and significantly (path from knowledge to intention), but knowledge path to attitude H2b was not statistically significant and its subsequently path to attitude to intention was not statistically significant either. The same is true for H2c; the path from knowledge to control not being statistically significant nor its subsequent path from knowledge to control to intention. Finally H3 is partially supported. Value is processed toward attitude and then intention while knowledge goes directly to intention without passing through attitude nor control. These results and hypotheses support are reported in Table IX.

Discussion

Our results found positive and statistically significant impact of familism on attitude and subsequent attitude to intention to purchase financial planning services (H1a, H1b, and H3a). These results strongly support prior literature of the value-attitude-behavior hierarchy that is particularly salient among Hispanics (Gregory et al., 2002; Gregory and Munch, 1997; Villarreal and Peterson, 2009). Our results extend prior research to the financial service area. We also found significant impact of familism on control and subsequently control to intention to purchase financial planning services among Hispanic American consumers (H2a, H2b, and H3a). These results support prior research that addressed the linkage of cultural values to a person’s perceived control within a collectivist culture (Ji et al., 2000). Also, our results found positive and statistically significant impact of financial knowledge on financial planning, supporting prior research that found that financial knowledge impacts financial behavior (Fry et al., 2008; Robb and Woodyard, 2011; Wang, 2009).

However, our results did not find financial knowledge linking to attitude or control, contrary to results found by prior works (Robb and Woodyard, 2011; Xiao et al., 2011). This is intriguing. One explanation could be that prior research separates knowledge into subjective and objective dimensions and were able to find subjective knowledge impacting attitude and control (Xiao et al., 2011). As for the co-concurrence of affect and cognition in the attitude-intention decision-making process, our results only found it to be true with affect but not cognitive. This perhaps can be explained by affect may sometimes dominate over cognitive route in some decision-making process (Young and O’Neil, 1992) or affective emotions in some cases precede cognitive processing in economic decision making (Lerner et al., 2004). Nevertheless our results did show knowledge matter in decision making but it just took a different or shorter route from knowledge directly to intention instead of going through attitude or control.

Conclusion

We introduced an integrative framework that combined the cultural value of familism, knowledge, and the theory of planned behavior within the context of Hispanic consumers. We hypothesized the simultaneous impact of affect, cognition, attitude, and perceived control on financial planning behaviors. Our results confirm the workings of affect and cognition on financial behavior for our sample of Hispanic participants.

Academics and financial institutions continue to seek a better understanding of the link between financial knowledge and behavior (Hill and Perdue, 2008; Tang et al., 2015), the main theoretical contribution of this paper is that we introduced the concept of cultural value (familism) as affect in financial behavior. We substantiated our conceptual framework by empirically testing the antecedents of cultural value to the theory of planned behavior within the financial behavior domain. Another contribution is that we integrated affect and cognition together in one causal model and thus consolidated prior works that have treated knowledge separately from the theory of planned behavior context (e.g. Wang, 2009). Our results shed light on the urgent need to understand the financial behavior of an increasingly important consumer group in the USA – Hispanic Americans (Lowrey and Taylor, 2014).

Our results have several managerial implications for financial service: first, cultural values are important antecedents to financial behavior, and thus managers may consider calibrate their tactics according to their clients’ cultural values. Second, our results found cultural value impacts attitude. We suggest that managers find ways to change their customers’ attitudes through their cultural value. Third, our results found that cultural values impact control.

Managers could leverage their customers’ cultural value to boost their customers’ confidence toward their own financial behavior.

Although we have collected data from various Hispanic American groups, our sample size remains small. Future work should consider collecting a larger sample size and a wider range of samples to enable research on additional, different groups of Hispanics. Another limitation of this research is the small number of financial products used in our measurement. Future research should include more products and possibly further categorize them into investment, savings, protection of assets, and others.

Figures

Figure 1

Scree plot from factor analysis

Figure 2

The conceptual framework

PLS results

Table I

Summary of representative literature investigating Hispanic financial behavior

Hispanic population Financial knowledge Theory of planned behavior Cultural values Financial behavior measured Data
One group Compare groups Attitude Perceived control
Perry (2008) x x country-of-origin Have bank account Survey Hispanic renters
Perry and Moore x x locus of control Propensity to save, budget, and control spending Freddie Mac Consumer Credit Survey
Plath and Stevenson (2005) x x money attitude: power, prestige Assets: real estate, life insurance, bonds, etc. SCF 1998 household-level data
Robb and Woodyard (2011) x Financial planning FINRA National Financial Capability Study
Stevenson and Plath (2006) x Financial investment portfolio Survey of Consumer Finance
Villegas and Shah (2008) x Cuban vs Mexican x humor vs non- humor ads Attitude toward financial service ad Experiment
Wang (2009) x Mutual fund Survey of financial /investors
Xiao et al. (2011) x x x College student credit card Survey college students
Yao et al. (2005) x Financial risk tolerance Survey of Consumer Finance
This work x x x x x Financial planning Survey Hispanics

Table II

Demographic profile of overall sample

Variable Category Percentage
Age Age Average 44.3
Annual household income Income <50 k 3
Income 50 k-100 k 39
Income 100 k-150 k 35
Income 150 k and above 23
Education level Without any higher education 9
Associate degree 19
Ethnicity Caribbean 28
Central America 12
Mexico 6
Puerto Rico 20
South America 32
Brazil 1
Others 1
Family status Single 10
Married 73
Divorced 8
Others 9
Children to support 81
Parents or other family members to support 52
Children and parents or family members to support 42
Gender Female 45
Male 55
Generation Generation 1 49
Generation 2 43
Generation 3 8

Table III

Correlation matrix

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)
(1) Value_help 1.00
(2) Value_welfare 0.74 1.00
(3) Value_care 0.60 0.61 1.00
(4) Know_retire 0.08 0.08 −0.08 1.00
(5) Know_college 0.10 0.03 −0.05 0.57 1.00
(6) Know_life 0.07 0.23 −0.26 0.60 0.44 1.00
(7) Att_priority 0.56 0.48 0.44 0.01 0.04 −0.12 1.00
(8) Att_success 0.43 0.36 0.39 −0.00 −0.04 −0.10 0.60 1.00
(9) Att_know 0.61 0.65 0.63 −0.06 −0.04 −0.29 0.45 0.31 1.00
(10) Control_conf 0.50 0.41 0.41 0.13 0.07 −0.10 0.56 0.55 0.41 1.00
(11) Control_good 0.53 0.39 0.29 0.22 0.11 0.01 0.56 0.53 0.40 0.64 1.00
(12) Control_wish 0.59 0.58 0.61 −0.00 0.03 −0.23 0.44 0.36 0.78 0.39 0.40 1.00
(13) Intent_retire 0.22 0.17 0.23 0.10 0.05 0.01 0.41 0.19 0.37 0.26 0.26 0.38 1.00
(14) Intent_college 0.31 0.33 0.29 0.07 0.08 −0.04 0.44 0.26 0.44 0.41 0.33 0.41 0.77 1.00
(15) Intent_life 0.27 0.26 0.24 0.11 0.10 −0.06 0.38 0.10 0.37 0.35 0.30 0.34 0.77 0.76 1.00

Table IV

Results of Harman’s one-factor test

Harman’s one-factor test (SPSS 24.0)
Component Total % of variance Cumulative % Total % of variance Cumulative %
1 5.89 39.26 39.26 5.89 39.26 39.26
2 2.30 15.32 54.58
3 1.75 11.64 66.22
4 1.20 7.99 74.22
5 0.57 3.83 78.05
6 0.54 3.62 81.66
7 0.53 3.54 85.20
8 0.45 3.02 88.22
9 0.36 2.39 90.61
10 0.31 2.08 92.69
11 0.29 1.95 94.64
12 0.24 1.60 96.24
13 0.20 1.35 97.59
14 0.19 1.25 98.85
15 0.17 1.15 100.00

Table V

Principal component analysis

 Kaiser-Meyer-Olkin measure of sampling adequacy 0.85 Bartlett’s test of sphericity Approx. χ2 1,386.94 df 105 Sig. 0.00

Table VI

Principal component analysis – total variance explained after rotation

Component Total % of variance Cumulative % Total % of variance Cumulative % Total % of variance Cumulative %
1 5.89 39.26 39.26 5.89 39.26 39.26 3.54 23.59 23.59
2 2.30 15.32 54.58 2.30 15.32 54.58 2.78 18.56 42.15
3 1.75 11.64 66.22 1.75 11.64 66.22 2.68 17.85 59.99
4 1.20 7.99 74.22 1.20 7.99 74.22 2.13 14.22 74.22
5 0.57 3.83 78.05
6 0.54 3.62 81.66
7 0.53 3.54 85.20
8 0.45 3.02 88.22
9 0.36 2.39 90.61
10 0.31 2.08 92.69
11 0.29 1.95 94.64
12 0.24 1.60 96.24
13 0.20 1.35 97.59
14 0.19 1.25 98.85
15 0.17 1.15 100.00

Note: Extraction method: principal component analysis

Table VII

Results of indicator reliability

ATTITUDE Financial planning top priorities 0.84
Financial plan increase success 0.72
Know more how to plan 0.81
CONTROL I feel confident selecting investment alternatives to meet my goal 0.81
I am doing a good job of preparing financially for my retirement 0.80
Wish in control of finance 0.80
INTENTION Interested … purchase retirement planning..? 0.92
Interested … purchase college savings 0.93
Interested … purchase life insurance 0.91
VALUE Always help family members 0.90
Welfare of family more important 0.90
Expect care for parents financially when older 0.84

Table VIII

CR, AVE, and LV correlations

LV correlations
Model CR AVE SQ. RT. AVE ATTITUDE CONTROL INTENTION KNOWLEDGE VALUE
All (base)
ATTITUDE 0.83 0.63 0.81 1.00
CONTROL 0.85 0.65 0.81 0.81 1.00
INTENTION 0.94 0.84 0.92 0.48 0.47 1.00
KNOWLEDGE 0.82 0.62 0.79 −0.19 −0.08 0.00 1.00
VALUE 0.91 0.77 0.88 0.75 0.70 0.32 −0.17 1.00

Notes: CR, composite reliability for internal consistency; AVE, average variance extracted for convergent validity; SQ. RT. AVE, square root of AVE measuring discriminant validity

Table IX

Structural model results

From To Path coefficient SE t-Statistics Hypotheses support
VALUE ATTITUDE 0.74 0.06 12.76* H1a supported
VALUE ATTITUDE → INTENTION 0.17 H1a, H3a supported
VALUE CONTROL 0.71 0.06 11.29* H1b supported
VALUE CONTROL → INTENTION 0.18 H1b, H3a supported
KNOWLEDGE ATTITUDE −0.07 0.05 1.34 H2b not supported
KNOWLEDGE ATTITUDE → INTENTION −0.02 ns H2b, H3b not supported
KNOWLEDGE CONTROL 0.04 0.07 0.51 H2c not supported
KNOWLEDGE CONTROL → INTENTION 0.04 ns H2c, H3b not supported
KNOWLEDGE INTENTION 0.13 0.07 1.66*** H2a supported
ATTITUDE INTENTION 0.23 0.12 1.90***
CONTROL INTENTION 0.25 0.13 2.00**

Notes: The indirect path from value to attitude to intention is a multiplication of value to attitude × attitude to intention. Similarly for path from value to control to intention. Same multiplication is performed for knowledge to attitude to intention. *,**,***Significant at the 1, 5 and 10 percent levels, respectively

Table AI

Indicators and their sources

Latent variable (LV) Indicators Source of survey question
Attitude Financial planning is one of my top priorities
I know that developing a financial plan will increase my success
I wish to know more about how to plan for my financial future
Ajzen (2002)
Cultural values I expect to care for my parents financially when they are older
I always help family members when they are in trouble
The welfare of my family is more important than my own
Villarreal and Peterson (2009)
Financial behavior intentions How interested would you be in purchasing or upgrading the following service for yourself or your family?
Retirement planning …?
How interested … college savings
How interested … life insurance
Robb and Woodyard (2011), Xiao et al. (2011)
Perceived control I feel confident selecting investment alternatives to meet my goals (e.g. IRA or cash deposits)
I am doing a good job of preparing financially for my retirement
I wish to be more in control of my finances
Ajzen (2002), Xiao et al. (2011)
Subjective financial knowledge How knowledgeable are you about retirement planning?
How knowledgeable are you about college savings?
How knowledgeable are you about life insurance?
Robb and Woodyard (2011), Wang (2009), Xiao et al. (2011)

Table AI

References

Ajzen, I. (2002), “Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior”, Journal of Applied Social Psychology, Vol. 32 No. 4, pp. 665-683.

Alos-Ferrer, C. and Strack, F. (2014), “From dual processes to multiple selves: implications for economic behavior”, Journal of Economic Psychology, Vol. 41, April, pp. 1-11.

Board of Governors of the Federal Reserve System (2017), “The Fed Consumer Credit”, available at: www.federalreserve.gov/releases/g19/current/default.htm (accessed May 15, 2017).

Bruggen, E., Hogreve, J., Holmlund, M., Kabadayi, S. and Lofgren, M. (2017), “Financial well-being: a conceptualization and research agenda”, Journal of Business Research, Vol. 79, October, pp. 228-237.

Chang, S., Van Witteloostuijin, A. and Eden, L. (2010), “From the editors: common method variances in international business research”, Journal of International Business Studies, Vol. 41 No. 2, pp. 178-184.

Ennis, S.R., Rios-Vargas, M. and Albert, N.G. (2011), The Hispanic Population: 2010, US Department of Commerce, Economics and Statistics Administration, US Census Bureau, Washington, DC.

Fernandes, D., Lynch, J. and Netemeyer, R.G. (2014), “Financial literacy, financial education, and downstream financial behaviors”, Management Science, Vol. 60 No. 8, pp. 1861-1883.

Fortell, Q. (2017), “Half of American families are living paycheck to paycheck”, MarketWatch, available at: www.marketwatch.com/story/half-of-americans-are-desperately-living-paycheck-to-paycheck-2017-04-04 (accessed June 20, 2017).

Fry, T.R.L., Mihajilo, S., Russell, R. and Brooks, R. (2008), “The factors influencing saving in a matched savings program: goals, knowledge of payment instruments, and other behavior”, Journal of Family and Economic Issues, Vol. 29 No. 2, pp. 234-250.

Grable, J.E., Joo, S.H. and Park, J. (2015), “Exploring the antecedents of financial behavior for Asians and non-Hispanic Whites: the role of financial capability and locus of control”, Journal of Personal Finance, Vol. 14 No. 1, pp. 28-37.

Gregory, G.D. and Munch, J.M. (1997), “Cultural values in international advertising: an examination of familial norms and roles in Mexico”, Psychology & Marketing, Vol. 14 No. 2, pp. 99-119.

Gregory, G.D., Munch, J.M. and Peterson, M. (2002), “Attitude functions in consumer research: comparing value-attitude relations in individualist and collectivist cultures”, Journal of Business Research, Vol. 55 No. 11, pp. 933-942.

Hadar, L., Sood, S. and Fox, C.R. (2013), “Subjective knowledge in consumer financial decisions”, Journal of Marketing Research, Vol. 50 No. 3, pp. 303-316.

Hair, J.F., Sarstedt, M., Ringle, C.M. and Mena, J.A. (2012), “An assessment of the use of partial least squares structural equation modeling in marketing research”, Journal of the Academy Marketing Science, Vol. 40 No. 3, pp. 414-433.

Hill, R.R. and Perdue, G. (2008), “A methodological issue in the measurement of financial literacy”, Journal of Economics & Economic Education Research, Vol. 9 No. 2, pp. 43-60.

Huston, S.J. (2010), “Measuring financial literacy”, The Journal of Consumer Affairs, Vol. 44 No. 2, pp. 296-316.

Ji, L.J., Peng, K. and Nisbett, R.E. (2000), “Culture, control, and perception of relationships in the environment”, Journal of Personality and Social Psychology, Vol. 78 No. 5, pp. 943-955.

Kahneman, D. (2011), Thinking, Fast and Slow, Macmillan, New York, NY.

Leo, C., Bennett, R. and Hartel, C.E.J. (2005), “Cross-cultural differences in consumer decision-making styles”, Cross Cultural Management, Vol. 12 No. 3, pp. 32-62.

Lerner, J.S., Small, D.A. and Loewenstein, G. (2004), “Heart strings and purse strings carryover effects of emotions on economic decisions”, Psychological Science, Vol. 15 No. 5, pp. 337-341.

Loewenstein, G. and Lerner, J.S. (2003), “The role of affect in decision making”, Handbook of Affective Science, Vol. 619 No. 642, pp. 619-642.

Lowrey, C. and Taylor, S. (2014), “The Hispanic American financial experience”, Prudential Research, Newark, NJ.

Malhotra, N. (2005), “Attitude and affect: new frontiers of research in the 21st century”, Journal of Business Research, Vol. 58 No. 4, pp. 477-482.

Mattila, A.S. (1999), “The role of culture and purchase motivation in service encounter evaluations”, Journal of Services Marketing, Vol. 13 No. 4, pp. 376-389.

Northwestern Mutual (2015), “Planning and Progress Study 2015”, available at: www.northwesternmutual.com/about-us/studies/planning-and-progress-2015-study (accessed March 4, 2017).

Perry, V.G. (2008), “Acculturation, microculture and banking: an analysis of Hispanic consumers in the USA”, The Journal of Services Marketing, Vol. 22 No. 6, pp. 423-433.

Perry, V.G. and Morris, M.D. (2005), “Who is in control? The role of self-perception, knowledge, and income in consumer financial behavior”, Journal of Consumer Affairs, Vol. 39 No. 2, pp. 299-313.

Pirouz, D. (2009), “Culture, self-control, and consumer financial behavior”, Advances in Consumer Research, Vol. 36, pp. 908-909.

Plath, A.D. and Stevenson, T.H. (2005), “Financial services consumption behavior across Hispanic American consumers”, Journal of Business Research, Vol. 58 No. 8, pp. 1089-1099.

Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y. and Podsakoff, N.P. (2003), “Common method biases in behavioral research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903.

Ringle, C.M., Wende, S. and Will, A. (2005), “SmarPLS 2.0 (beta)”, available at: www.smartpls.de (accessed September 13, 2015).

Robb, C.A. and Woodyard, A.S. (2011), “Financial knowledge and best practice behavior”, Journal of Financial Counseling and Planning, Vol. 22 No. 1, pp. 60-70.

Schaller, T. and Malhotra, N. (2015), “Affective and cognitive components of attitudes in high-stakes decisions: an application of the theory of planned behavior to hormone replacement therapy use”, Psychology and Marketing, Vol. 32 No. 6, pp. 678-695.

Sivaramakrishnan, S., Srivastava, M. and Rastogi, A. (2017), “Attitudinal factors, financial literacy, and stock market participation”, International Journal of Bank Marketing, Vol. 35 No. 5, pp. 818-841.

Stevenson, T.H. and Plath, D.A. (2006), “Marketing financial services to Hispanic American consumers: a portfolio-centric analysis”, The Journal of Services Marketing, Vol. 20 No. 1, pp. 37-50.

Tang, N., Baker, A. and Peter, P.C. (2015), “Investigating the disconnect between financial knowledge and behavior: the role of parental influence and psychological characteristics in responsible financial behaviors among young adults”, Journal of Consumer Affairs, Vol. 49 No. 2, pp. 376-406.

Villarreal, R. and Peterson, R.A. (2009), “The concept and marketing implications of Hispanicness”, The Journal of Marketing Theory and Practice, Vol. 17 No. 4, pp. 303-316.

Villegas, J. and Shah, A. (2008), “The price of laughter: differences between Hispanic groups’ responses to the use of humor in financial services advertising”, Family and Consumer Sciences Research Journal, Vol. 37 No. 1, pp. 39-51.

Wang, A. (2009), “Interplay of investor’s financial knowledge and risk taking”, Journal of Behavioral Finance, Vol. 10 No. 4, pp. 204-213.

Wang, C. and Hanna, S.D. (2007), “Racial/ethnic disparities in stock ownership: a decomposition analysis”, Consumer Interests Annual, Vol. 53, Belleair Bluffs, FL, pp. 113-130.

Watchravesringkan, K. (2008), “Financial behavior of Hispanic Americans”, in Xiao, J.J. (Ed.), Handbook of Consumer Finance Research, Springer, New York, NY, pp. 271-285.

Wold, H. (1985), “Partial least squares”, in Kotz, S. and Johnson, N.L. (Eds), Encyclopedia of Statistical Sciences, Vol. 6, John Wiley, New York, NY, pp. 581-591.

Xiao, J. and Porto, N. (2017), “Financial education and financial satisfaction: financial literacy, behavior, and capability as mediators”, International Journal of Bank Marketing, Vol. 35 No. 5, pp. 805-817.

Xiao, J.J., Tang, C., Serido, J. and Shim, S. (2011), “Antecedents and consequences of risky credit behavior among college students: application and extension of the theory of planned behavior”, Journal of Public Policy & Marketing, Vol. 30 No. 2, pp. 239-245.

Yao, R., Gutter, M. and Hanna, S. (2005), “The financial risk tolerance of blacks, Hispanics and whites”, Financial Counseling and Planning, Vol. 16 No. 1, pp. 51-62.

Young, M.C. and O’Neil, B.M. (1992), “Mind over money: the emotional aspects of financial decisions”, Journal of Financial Planning, Vol. 5 No. 1, pp. 32-36.