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Using the life course paradigm to explain mechanisms that link family disruptions to compulsive buying.

This research examines compulsive buying as an impulse-control disorder, a form of maladaptive behavior believed to have its roots in early-in-life experiences of family adversities. Unlike previous research that has typically studied only the effects of family divorce on compulsive buying, this study examines the effects of disruptive family events within the broader multitheoretical life course framework. A sample of 327 young adults is used to test the hypothesized relationships derived from the main life course perspectives. The results show alternate paths leading to compulsive buying, beyond those uncovered in previous studies. By offering a broader overarching framework, the article shows how previous efforts to study compulsive buying can be improved, pointing to the value of the multitheoretical life course approach in understanding consumption phenomena.

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Compulsive buying has been viewed as a form of maladaptive behavior that affects the well-being of millions of consumers globally (e.g., Muller and de Zwaan 2004; Neuner, Raab, and Reisch 2005; Roberts 1998; Roberts and Jones 2001). It has negative consequences on consumers and their families, resulting in depression, unmanageable debt and lower satisfaction with life in general (e.g., Koran et al. 2006; Ridgway, Kukar-Kinney, and Monroe 2008). Given the known adverse consequences of compulsive buying, social workers and professionals in the field of family finance management need to understand the reasons consumers develop compulsive buying tendencies if they are to help consumers avoid and treat compulsive buying tendencies.

Little theory has been developed in this area to help understand how or why compulsive buying tendencies develop, although several possible explanations of its origins have been offered (e.g., Faber 1992; Faber et al. 1995; Hirschman 1992: Litt, Pirouz, and Shiv 2011; Rindfleisch et al. 1997). Most researchers, however, view compulsive buying as an impulse-control disorder that relates to other types of impulse-control disorders, such as binge-eating and alcoholism (e.g., Faber et al. 1995), that are viewed as deviant, antisocial, or maladaptive and have their roots in biological or genetic factors (e.g., neurological, chemical), psychological traits (e.g., low self-control), and sociological factors (e.g., early-life socialization experiences) (e.g., Burnett et al. 2011; Faber 1992; Faber and Christenson 1996: Faber et al. 1995; Hassay and Smith 1996; Litt et al. 2011: McLeod and Almazan 2003; Roberts 1998; Simons et al. 1998).

Given the broad landscape with respect to the etiology of compulsive buying, previous studies have suffered from a lack of consensus with regard to its conceptualization and measurement. Conceptually, compulsive buying has been viewed within a broad continuum, ranging from uncontrollable impulsive choices viewed as a result of the person's life experiences (e.g., Hirschman 1992) to addictions or pathologies (disease) due to consequences of programmed biological, genetic and chemical processes (e.g., Faber and Christenson 1996; Hassay and Smith 1996). As a result, researchers have viewed compulsive buying as a single-dimensional concept (Faber and O'Guinn 1992; Valence, d'Astous, and Fortier 1988). More recently, Ridgway and colleagues (2008) offered a more comprehensive conceptualization of compulsive buying by distinguishing between the impulse-control and obsessive-compulsive dimensions of compulsive buying, while others have distinguished between these two dimensions with respect to their etiology (e.g., DeSarbo and Edwards 1996; Hassay and Smith 1996). For example, DeSarbo and Edwards (1996) suggest that impulsive and compulsive buying differ in the underlying motivations for excessive shopping. Specifically, they are hypothesized to differ in the triggering stimuli for the behavior: impulsive buying is triggered by external stimuli (e.g., in-store, situational), whereas compulsive buying is triggered by internal factors (e.g., stress, anxiety). In line with this view, most studies and measures of compulsive buying have assumed a retailer-customer interface, although they put different emphasis on the two types of stimuli that tend to be incorporated into a single measure of compulsive buying (e.g., d'Astous, Maltais, and Roberge 1990; Faber and O'Guinn 1992; Valence et al. 1988).

Ridgway and associates (2008) emphasize the need to distinguish between variables that define compulsive buying from those that are the outcome of impulsive and obsessive disorders. Further research by these authors shows that compulsive buyers' tendencies to buy on the Internet were motivated by factors such as the need to buy unobserved, to avoid interaction with salespeople, and to obtain immediate gratification and underscores the importance of external factors such as special sales (Kukar-Kinney, Ridgway, and Monroe 2009). Finally, Hassay and Smith (1996) assert that compulsive buying is a manifestation of the buying impulse.

This study subscribes to views on the development of impulse-control disorders popular in other disciples. It conceptualizes and measures compulsive buying as an impulse-control disorder and uses psychological and sociological explanations for impulse-control disorders to explain its development. Specifically, this study seeks explanations for such behaviors in theories that link childhood experiences to adult maladaptive behaviors by focusing on processes that link childhood adversities to such outcomes (e.g., Hill, Yeung, and Duncan 2001; McLeod and Almazan 2003, Simons et al. 1998). While we recognize the importance of obsessive-compulsive dimensions of compulsive buying, this dimension may lend itself to pathological and biological explanations such as addiction, and may require neuroscientific research approaches (e.g., Burnett et al. 2011; Litt et al. 2011).

Previous research that has attempted to link family adversities experienced in early childhood to compulsive buying tendencies in later life provide less than adequate explanations of how and why these events lead to the development of compulsive buying (Rindfleisch et al. 1997). Although these authors suggest that compulsive consumption is rooted in stressful family life events, they offer limited insights into the mechanisms responsible for the presumed linkages. For example, they suggest that disruptive family events may alter family socialization practices responsible for the development of compulsive buying tendencies observed in early adulthood, but the effects of family socialization on compulsive buying remain largely unexplored. Previous researchers have made similar speculations (e.g., Faber 1992) but have offered little evidence in support of these arguments. Furthermore, although previous efforts to understand such a phenomenon recognize the influence of variables derived from diverse theories, they offer no viable multitheoretical framework to help examine the effects of these variables.

This research contends that the life course paradigm (e.g., Elder 1998) offers a viable framework for studying childhood-adulthood links because it focuses on several mechanisms, not only the stressful family events suggested by previous researchers. The life course paradigm has been used to study similar phenomena of maladaptive behavior, such as the impulse-control disorders of binge-eating and binge-drinking (e.g., Simons et al. 1998, 2002), and seems appropriate for studying the impulse-control dimension of compulsive buying. This multitheoretical paradigm has flourished in recent decades as a framework that extends across substantive and theoretical boundaries of the social and behavioral sciences (e.g., Elder, Johnson, and Crosnoe 2003), and is considered "one of the most important achievements in social science in the second half of the 20th century" (Colby 1998, preface x).

In this article, we first present the general life course paradigm as the overarching framework that serves as a blueprint for organizing, integrating, and developing a theory-driven model to explain compulsive buying as an impulse-control disorder. Next, we test our model of compulsive buying using a sample of young adults. Finally, we discuss implications of the research findings for theory development and suggest directions for further research.

MODEL AND HYPOTHESES

The life course approach views behavior at any stage in life or given point in time to be the result of responses to earlier life conditions and the way the individual has adapted to these circumstances. The life course paradigm suggests that biological and psychological changes during an individual's life and social demands across the life course that are defined by typical life events and social roles create physical, social and emotional demands and circumstances to which one must adapt (e.g., Elder 1998). This adaptation entails stress and coping, socialization, and development and growth or decline which are the underlying change mechanisms of the three most widely accepted life course perspectives: stress, socialization (normative), and human capital, respectively (Moschis 2007a).

Compulsive behaviors that are often conceived of as addictions to products (e.g., alcohol, drugs, cigarettes), activities (e.g., gambling, shopping, shoplifting, overspending) and negative emotions (low self-esteem, depression, withdrawal) have been suggested as possible consequences of coping behaviors used over time to handle stress (e.g., Hirschman 1992; O'Guinn and Faber 1989). Behaviors that provide short-term relief from negative emotional states and enhance one's sense of control are originally effortful and reflect coping; they tend to be likely to be positively reinforced and, over time, they may become conditioned responses to stressful situations (Hirschman 1992) that characterize the individual's attitudinal and behavioral orientations (Faber et al. 1995: Moschis 2007a).

Socialization explanations of compulsive buying focus on sociological theories of the ways people are socialized to social norms (e.g., Faber 1992; Faber and Christenson 1996; Moschis 2007a). As Faber (1992, 815) states: "To some degree, how society views a particular behavior influences the likelihood that some people will engage in this behavior." He also points out that in the United States, not only is shopping an easily accessible activity, but it is also often glorified in the mass media. This may contribute to the development of excessive buying as a socially acceptable norm. Other researchers also share this view, especially those subscribing to cultivation theory (cf. Kwak et al. 2003; O'Guinn and Shrum 1997; Roberts 1998).

Human capital explanations view the development of compulsive buying tendencies as the result of skills and knowledge that people acquire in different socio-cultural contexts. These theories allow for change in processes and outcomes over time that are not socially desirable. Faber (1992) provides explanations for compulsive behaviors that can be viewed in the context of human capital theories. He suggests that cultural and subcultural factors may determine the types of compulsive behavioral tendencies a person acquires. For example, rates of addiction to alcohol and tobacco products are likely to be rather low in countries like Iran and Bhutan that prohibit the consumption of such products. As Faber (1992) points out, the reason studies in countries like United States find a much larger percentage of women than men to be compulsive buyers may reflect cultural differences in socialization. Thus, the macro-system (e.g., culture, subculture) defines the structure and function of proximal settings (e.g., socialization). For example, the prevalence and effectiveness of parenting and family communication styles may change as a result of changes in the family structure (e.g., Amato and Sobolewski 2001; Conger et al. 1994; Uhlenberg and Mueller 2003), and outcomes may include maladaptive (e.g., excessive, compulsive) and deviant or antisocial responses (Mortimer and Simmons 1978; Uhlenberg and Mueller 2003). Consumers in different cultures and subcultures develop human capital related to different consumption activities, and these behaviors are likely to become compulsive for some individuals.

The life course paradigm provides a framework for integrating the three perspectives, and specific hypotheses can be based on more than one theoretical perspective (Moschis 2007a). For example, various portrayals in the mass media as to how a person might cope with stress (e.g., Faber 1992) suggest the integration of the socialization and stress perspectives, while cultural and subcultural differences in socialization practices and norms suggest the integration of the socialization and human capital perspectives. Thus, unlike previous research that view stress as the mechanism or process that links family disruption to compulsive buying (e.g., Rindfleisch et al. 1997; Roberts, Manolis, and Tanner 2003), the life course paradigm suggests a number of causal mechanisms may relate family structure to a child's problem behavior in general, and compulsive buying in particular.

We use the life course paradigm as an overarching conceptual framework to select variables and develop our hypotheses. The selection of variables used to develop our models and hypothesized relationships is guided by two main factors. First, the variables are suggested by theory and research relevant to one or more theoretical life course perspectives. Second, because of the retrospective nature of this study we consider only variables that are likely to be reliable when measured retrospectively. We exclude variables that previous research has suggested are not likely to be reliable in retrospective studies (Henry et al. 1994). Thus, for example, many psychological and marketing variables that have been linked to compulsive buying, such as anxiety, self-esteem, exposure to mass media and other marketing stimuli (e.g., advertising, sales promotions), and parental mediation of advertising effects that could trigger or deter compulsive buying (e.g., DeSarbo and Edwards 1996: Faber and Christenson 1996; Kukar-Kinney et al. 2012; Roberts 1998), are excluded from our model (Figure 1).

Figure 1 suggests that changing family conditions experienced in early life result in psychological, social and economic changes--mechanisms for the three life course perspectives (stress, socialization and human capital, respectively)--that contribute to the development of impulse-control for compulsive buying tendencies observed in early adulthood. The hypothesized relationships in the model derive from one or more life course perspectives. The stress perspective views stress and coping as causal mechanisms underlying the development of compulsive behaviors. While the traditional or objective view on stress assumes that any change is inherently stressful, a more recent popular view (subject to less criticism) assumes a response-based definition that focuses on perceived (felt) rather than mere experience of change (George 1989). In this context, the number of experienced family disruption events (e.g., loss of a parent, divorce or separation) will increase perceived stress (H1a). Further, the duration of experienced family disruption events will increase perceived stress (H1b).

Further, shopping and buying products have been viewed as self-indulgent coping responses (e.g., O'Guinn and Faber 1989). Prolonged experience of perceived stress due to adverse family events during childhood years may lead to the development of these activities as an uncontrollable, hence compulsive, form of behavior (Faber et al. 1995). In other words, increased levels of perceived stress due to family disruption events will increase compulsive buying (H2). Extant research also points to the value of communications with peers as coping mechanisms employed by youths experiencing aversive family conditions (e.g., Gecas 2003; Moschis 2007b), suggesting a link between perceived stress from disruptive family events and peer communications about consumption matters. Thus, increased levels of perceived stress will increase the importance of peers as coping mechanisms and socialization agents, leading to increased communications with peers about consumption (H3).

The socialization perspective views adult supervision and monitoring of children's behavior as important means by which children internalize social norms and are kept from engaging in socially undesirable behaviors. Previous research shows that disruptive family events can undermine effective socialization. Parent-child bonds are weakened due to disruptive family events and prolonged experience of such events (Amato and Sobolewski 2001). We characterize this weakening of parent-child bonds as a reduction in intangible family support available to the child that may result in impaired or delayed development in human capital that is believed to further promote impulsive choices in response to stressful events (Moschis 2007a: Pechman et al. 2005). Thus, increasing numbers of family disruption events will decrease intangible family support (H4a). Likewise, longer durations of family disruption events will lead to decreases in intangible family support (H4b).

Further, family disruptions may affect parenting practices. Social control theory and research suggest that family disruptions have direct effects on the two styles of communication that parents use with their children and are relatively unrelated--i.e., a family can use either, both or neither. The socio-oriented family communication, which places emphasis on obedience and conformity to the parent's desires, and the concept-oriented family communication style, which encourages autonomy and self-expression (Moschis 1985). The former style includes coercive strategies a parent uses to monitor a child's behavior. This style is more likely to characterize communications within dislocated families; research shows that parents in such families tend to display less warmth toward their children and discipline them more harshly (Amato and Sobolewski 2001; Conger et al. 1994). The consumer socialization literature suggests that such a parenting style that appears to promote a socio-oriented family communication style (Carlson, Laczniak, and Wertley 2011) is likely to deter human capital development and create stress for the child (Moschis 1985, 1987). Parents in nondisrupted families are more likely to have a higher level of commitment and involvement in their child's life (Uhlenberg and Mueller 2003), which promotes a concept-oriented communication style that in turn facilitates human capital development (Moschis 1985, 1987). Thus, increases in family disruptive events will increase socio-oriented family communication styles (H5a). Likewise, longer durations of family disruption events will lead to increases in socio-oriented family communication styles (H5b). In contrast, parents in nondisruptive families are likely to have a higher level of commitment and involvement in their child's life (Uhlenberg and Mueller 2003), and more likely to adopt a concept-oriented communication style. Thus, a concept-oriented communication style is more likely to be the result of greater number of family disruption events (H6a) and longer durations of such events (H6b).

Furthermore, both socialization and stress perspectives and research suggest that when young people receive inadequate emotional support from their parents, they are likely to gravitate toward non-familial socialization agents, especially peers who are in a position to help the youth cope with adverse life circumstances (Uhlenberg and Mueller 2003). Thus, decreases in intangible family support should lead to increases in peer communication about consumption matters (H7).

The human capital perspective suggests that family disruption events interfere with the development of knowledge and skills, leading to impairment and delay in human capital development (Frytak et al. 2003), with reduced income viewed as the operative mechanism in the economic hardship theory that links social structure to problem behaviors (Hill et al. 2001). Thus, the occurrence of disruptive events such as parental divorce is associated with decreases in socioeconomic status (e.g., Amato and Sobolewski 2001) (H8a). The duration of such disruptive events is also expected to contribute to decreases in the family's socioeconomic status (H8b). Specifically, the emphasis families place on conformity in socializing their children--a characteristic of socio-orientation family communication style (Moschis 1985)--might be the result of the family's social position (Kasser et al. 1995). People in lower socio-economic status (SES) families are more likely to have jobs and social roles that require conformity rather than self-direction, and such roles and orientations can then influence the values parents have regarding their offspring. Thus, higher levels of SES should be associated with decreased levels of socio-oriented family communication styles (H9). Conversely, higher levels of SES in families may encourage self-expression and self-direction in interacting with their children, promoting increased levels of concept-oriented family communication (H10).

In addition, a family's social position may not only affect parents' socialization practices and human capital development in young people but may also affect the child's behavior outside the home. Parents of a relatively low SES and those who value conformity more than self-expression may encourage their children to value more the demands and strictures of others rather than their own true desires (Kasser et al. 1995), creating a stronger need for their children to talk to their peers about consumption matters in order to validate their views about consumption. In other words, decreased SES should lead to increased levels of peer communication about consumption (H11). The self-agency developed and fostered in peer groups may be directed toward constructive or destructive ends (Gecas 2003), and thus may be influenced by interactions with peers who exhibit compulsive buying tendencies. Theories of antisocial or deviant behavior and data link interaction with deviant peers in early life to problem behavior in later life (e.g., Simons et al. 1998, 2002), including compulsive buying tendencies (d'Astous, Maltais, and Roberge 1990). Research in neuroscience shows that the triadic circuit of the brain that controls impulsive behavior is actively maturing during adolescence, making adolescents particularly vulnerable to disruption during late development (Ernst and Fudge 2009: Litt et al. 2011). This may elevate the youth's impulsive behaviors in peer settings (Burnett et al. 2011) and could explain the positive relationship between peer interactions and compulsive buying (d'Astous et al. 1990). Thus, increased levels of peer communications about consumption are expected to lead to greater compulsive buying tendencies (H12).

Finally, studies have shown a link between certain types of socialization practices and human capital development. Specifically, research suggests that a family environment rich with concept-oriented family communication may facilitate capital development, such as the ability to process information and make rational decisions, while a socio-oriented family communication environment that restrains youths from acquiring independence in decision making may deter capital development and promote emotionally based behaviors (e.g., Moschis 1985, 1987; Saphir and Chaffee 2002). Research findings show that controlling family environments are likely to rear children who are oriented toward hedonically gratifying behaviors (Conger et al. 1994; Kasser et al. 1995). The delayed development of human capital that has been observed in controlling family environments, such as those that characterize the socio-oriented communication style, makes children more susceptible to impulsive choices that are precursors to compulsive behaviors (Pechman et al. 2005); and it may explain the positive relationship between socio-oriented communication and compulsive buying reported by Gwin et al. (2005). Thus, a concept-oriented family communication style may promote self-control and deter impulse-oriented choices. In other words, increased levels of concept-oriented family communication style should tend to decrease compulsive buying tendencies (H13), while a socio-oriented family communication environment may lead to increases of compulsive buying tendencies (H14).

METHODS

Sample

Previous research suggests that adolescence is a period during which a person may begin to display compulsive buying tendencies if these will later prove to be present (d'Astous, Maltais, and Roberge 1990; Roberts et al. 2003). According to the (US) National Comorbidity Study Replication, the median age of onset for many impulse-control disorders is the mid-teens (Kessler et al. 2005). Therefore, we assessed young adults' experiences during their adolescent years retrospectively, since people at early adulthood years are young enough to recall their adolescent experiences while also being old enough to be aware of and report their compulsive buying tendencies (Wooten 2006). Retrospective measures are widely used in life course studies (Henry et al. 1994), especially when people are expected to recall important events because the recall of important events experienced in the recent past tends to be accurate (Henry et al. 1994). Experience of the perceived stress from events during adolescent years tends to be remembered and recalled even after several decades, according to biographic and longitudinal studies reported by Thomae and Lehr (1986), and previous studies have used samples of young adults to assess their family experiences during adolescent years retrospectively (e.g., Rindfleisch, Burroughs and Denton 1996; Rindfleisch et al. 1997; Saphir and Chaffee 2002).

The sample for this study consists of 327 young adults attending marketing classes as full-time or part-time students in two large universities. One of these universities is located in a large city of a southern state, while the other is located in a semi-rural area of a northern state. All students who were asked to participate in the study for extra credit cooperated; they completed anonymous surveys in-class and returned them to a secure location. The surveys were edited and those that were not complete were eliminated from the analyses. Of the total number of participants who provided usable questionnaires, 42.5% were male and 57.2% were female. Their ages ranged from 18 to 32 (average age = 23.3 years, SD 2.5 years), and 38.6% of respondents were of minority background (black, Hispanic and Asian), figures that are fairly representative of the population characteristics in the two geographic areas. Characteristics between students from each school were similar. For example, 43.8% of respondents were male for the southern school and 41.5% were male in the northern school. The mean age of respondents was 23.7 years and 22.8 years, respectively. The southern school respondents were 38.9% minority while the northern school had 38.3% minority respondents.

Measures

In constructing measures for the variables of our study, we relied on past research and used measures similar to those that had performed well in previous studies. The items used in these measures are shown in the Appendix. Descriptive statistics, correlations and reliabilities (Cronbach's [alpha]) are reported in Table 1.

Compulsive Buying

Because our study focused on the impulse-control disorder of compulsive buying, we measured compulsive buying using nine of the eleven items from Valence et al.'s (1988) scale. This scale was used because it has been recognized by Ridgway and associates (2008) as a measure of impulse-control compulsive buying. Two items were dropped when results from an initial confirmatory factor analysis (CFA) reported poor loadings of these items (<.45). The CFA of the final nine items modeled as a unidimensional scale produced acceptable fit ([chi square] = 53.00, df = 23, CFI = .99, SRMR= .032, RMSEA = .066), and Cronbach's [alpha] was .91.

Family Communication

The measures of parents' socio-oriented and concept-oriented family communication styles were developed more than forty years ago by Chaffee and McLeod (cf. Moschis 1985; Rubin, Palmgreen, and Sypher 1994), and have been measured in various ways with respect to number of items used and response formats (e.g., Rubin et al. 1994). The number of items used has varied from one or two (Saphir and Chaffee 2002) to fifteen (Ritchie and Fitzpatrick 1990), while response formats have been in the form of frequency (very often-never) and (dis)agreement with statements (Rubin et al. 1994). We decided to use the same six items originally developed by Chaffee and McLeod because longer scales have included these items and are part of the same factors. Previous research has validated the two family communication measures (e.g., Saphir and Chaffee 2002), but has provided variance in reliabilities depending on the number of items used and the nature of the sample (Rubin et al. 1994). One item for concept-oriented family communication and two items for socio-oriented family communication were dropped because of poor loadings on the initial CFA (<.25). The items retained are shown in the Appendix. The reliability coefficients for the scales were consistent with those reported in the literature (socio-oriented [alpha] = .67, and concept-oriented [alpha] = .76).

Peer Communication

We used a measure of peer communication about consumption that had been used in previous consumer socialization studies (e.g., Churchill and Moschis 1979; Moschis and Moore 1982). This measure, which consists of eight items, had an alpha reliability coefficient of 0.83. One item was not used in model testing because of poor loadings (<.47).

Intangible Family Support

Intangible family support was also similar to that used in previous studies (Rindfleisch et al. 1997; Roberts et al. 2003). Respondents were asked to indicate the degree to which their family's support was adequate or inadequate by providing their responses to five items on a five-point "Inadequate support" (1) to "Exceptional support" (5) scale. The five items are shown in the Appendix. The average of the items was used in the analysis to operationalize this formative construct.

Disruptive Family Events

We collected data on the ten disruptive events used in previous studies (Rindfleisch et al. 1997; Roberts et al. 2003). Because the life course principle of "linked lives" focuses on parents' experience of life events that affect their children (e.g., Elder 1998), we used those events that relate to the youth's interface with his or her parents over which they have little control (temporary parental absence, loss/separation from family members, not living with both biological parents, physical abuse by parents and family arguments). We excluded those over which the child has control and are likely to be a consequence of family disruptions (e.g., poor school performance, trouble with the police, conflict with friends) (Amato and Sobolewski 2001). Respondents were asked to (a) indicate if they experienced each of the events when they were between the ages of 12 and 18, (b) write the age at which they had experienced it (respondents could write multiple ages, if they had experienced the event more than once), and (c) write how long (in years and months) each event had lasted after it was experienced. The five items comprised the index measure of disruptive family events (M = 1.79, SD = 1.29), while the measure of duration of disruptive family events was calculated as the average length of time, in years, a respondent endured the family disruptive events they personally experienced (M =4.58, SD=4.99). Given that respondents had experienced different numbers and types of events, formative measurement is appropriate for these two variables (Jarvis, MacKenzie, and Podsakoff 2003).

Perceived Family Stress

In line with response-based definitions of stress (Elder et al. 1996; George 1989), we used the same perceived family stress scale and response format used in previous studies (e.g., Rindfleisch et al. 1997; Roberts et al. 2003) to assess perceived stress due to disruptive family events. For each of the five disruptive family events respondents had experienced during their adolescent years, they were asked to reflect on whether they thought the event had a positive or negative impact on their life. Their responses were measured on a five-point "strongly positive" (+2) to "strongly negative" (-2) scale and summed to construct our perceived family stress scale (M = -0.18, SD = .36). Given that each event can affect one differently and respondents had experienced different numbers and types of events, from a psychometric standpoint, formative measurement is appropriate (Jarvis et al. 2003).

Socio-Economic Status

SES was measured in a similar way as in previous research (Rindfleisch et al. 1997) by asking respondents to indicate (1) how financially well-off their household was when they were growing up, on a four-point "very well-off" (4) to "not well-off at all" (1) scale, (2) whether at the time of their eighteenth birthday their family owned (a score of 2) or rented (a score of 1) the home where they lived, and (3) total number of years of both parents' education. The three measures were standardized and summed. Scores above the mean indicated higher SES while scores below the mean indicated a relatively lower SES.

In addition, our study collected data on other demographic variables that were used as covariates. Gender was dummy-coded (1 = female, male = 0), race (white, non-Hispanic = 1, other = 0), and the respondent' s age at the time of survey were all included as covariates.

DATA ANALYSES AND RESULTS

Overall Analysis Strategy

All models were analyzed using MPlus 5 structural equation modeling software with full information, robust, maximum likelihood estimation. We briefly discuss modeling decisions here, and report empirical results next. The model was evaluated in two steps. First, a measurement model with all study variables correlating was used to assess the measurement properties of the latent variables (Model 1). When multi-item reflective measures were used to measure a construct, a first-order factor model with uncorrelated measurement errors was specified for the construct (e.g., compulsive buying, peer communication about consumption, socio-oriented family communication and concept-oriented family communication). Other study variables (e.g., SES, family disruptions, perceived family stress and intangible family support) were operationalized simply as single-item observed variables. Next, the research hypotheses were tested using a SEM that only included relationships between variables hypothesized in the theoretical model (Model 2).

Model and Hypothesis Testing

Our multitheoretical model is presented in Figure 1, while the results are reported in detail in Table 2. Table 2 presents a summary of the hypothesized relationships in Figure 1, the nature of each of the expected relationship, regression coefficients with significance levels, standard errors and Z-values. The measurement model (Model 1) had acceptable fit ([chi square] (df) = 718.63 (382); CFI = .904; SRMR = .060; RMSEA = .052). The SEM (Model 2) fit statistics were: [chi square] (df)= 848.37 (460), CFI = .884, SRMR = .051, RMSEA = .061. The [R.sup.2] of compulsive buying in the life course model is .201. We also tested a multigroup model that estimated the SEM for both the southern school respondents and northern school respondents separately. Multigroup path equivalence tests revealed no significant differences (p > .05). These results indicate pooling the respondents into one sample for analysis is appropriate. Demographic controls on study variables were generally insignificant (p > .05), with a few exceptions. First, female respondents reported greater compulsive buying tendencies (B =.43, p < .001) and higher levels of peer communication about consumption (B =.19, p < .05). White, non-Hispanic respondents had lower levels of socio-oriented family communication (B = -.29, p < .05) and higher SES (B =.57, p < .001).

While the results are somewhat consistent with the structural model, they are not entirely so. Disruptive family events experienced during adolescent years create perceived family stress, as expected (H1a). However, the duration of these disruptions is not associated with perceived family stress, providing no support for H1b. The impact of perceived family stress due to family disruptions on our compulsive buying measure is positive, as hypothesized (H2), but is not significant. Perceived family stress experienced from disruptive family events at home are associated with peer communications about consumption, as posited (H3). Family disruptions also reduce intangible family support, as hypothesized (H4a), and the duration of such disruptions also has a negative association with intangible family support, although it is not significant (H4b).

The results also suggest that family disruptions affect parent-child communications. As expected, socio-oriented family communication is more likely to be reported by young adults who experienced disruptive family events (H5a) for longer periods (H5b); and family disruptions have a marginal negative impact on concept-oriented family communication styles, as expected (H6a), although this association does not hold with respect to the effects of duration of such family adversities (H6b). Contrary to our expectations, intangible family support is not inversely related to peer communications about consumption. Rather, our data show that the two variables positively relate, providing no support for H7.

The study findings also suggest direct and indirect effects of disruptive family events by impacting the family's social position (SES). Young adults who experienced family adversities during their adolescent years, both in terms of number and duration, were also more likely to have experienced degraded SES, as posited (H8a and H8b, respectively). Furthermore, lower SES appears to foster a socio-oriented family communication style, as hypothesized (H9); it appears to deter a concept-oriented family communication style at home, as hypothesized (H10); and it increases peer communication about consumption, also providing support for H11.

Furthermore, our data suggest that interactions with family and peers promote compulsive buying tendencies. Specifically, the frequency of communication about consumption with peers during one's adolescent years has a strong impact on his or her compulsive buying tendencies reported as a young adult, in line with H12. A socio-oriented family communication style is also likely to have a positive impact on the young adult's compulsive buying tendencies, providing support for H13. However, the data do not provide support for an inverse relationship between concept-oriented family communication at home and compulsive buying (H14) (the relationship is positive but not significant).

DISCUSSION

As with any empirical study, our study has limitations that should be noted. First, the sample of our study was a convenience sample of young adult university students. It is not nationally representative and is likely biased toward more affluent and educated consumers. While we do not know the extent to which our sample is so idiosyncratic that the uncovered relationships of this study could not be extended to those young consumers who did not attend college, the reader should exercise caution in interpreting these findings. A second limitation of our study is that it focuses on the impulse-control dimension of compulsive buying as a dispositional orientation, at the exclusion of the examination of the obsessive-compulsive buying dimension that should also be considered from a pathological or neurological perspective on addiction (e.g., Litt et al. 2011). Third, the issues in using retrospective data should not be ignored. Although retrospective measures are widely used and common substitutes for more expensive and time-consuming longitudinal studies in life-course research (e.g., Henry et al. 1994; Mayer and Tuma 1990), and previous studies in different cultural settings have used similar retrospective measures (e.g., Benmoyal-Bouzaglo and Moschis 2009; Rindfleisch et al. 1997), error in measurement can occur due to inaccurate recall (such as our response-based measure of perceived family stress) one experienced in the distant past (Henry et al. 1994). Fourth, we recognize that adolescence may be a limited period for assessing experience of disruptive family events; perhaps such disruptions have greater effects when experienced during childhood rather than adolescent years. Finally, our model is not by any means inclusive of the many factors in the life course paradigm that directly or indirectly can affect compulsive buying, such as self-esteem and exposure to mass media, nor does it consider the effects of variables that can moderate the hypothesized relationships (e.g., Moschis 2007a); and some of our results may be due to the presence of myriad other individual or social influences (e.g., Litt et al. 2011) that may account for the observed relationships. While we recognize the limitation of cross-sectional studies, such as this one, in determining "causality," we subscribe to Popper's (1959) view that the main value of correlational data stems from their ability to falsify hypotheses, not confirm them in a positive sense.

Although it has been suggested that perceived family stress and social processes may play a role in the development of compulsive buying tendencies (e.g., Gwin et al. 2005: Rindfleisch et al. 1997), theoretical perspectives on the nature of these influences are sparse. By placing previous efforts to explain compulsive buying in the context of the broader multitheoretical life course framework, we can examine the influence of variables derived from diverse and complementary perspectives. In comparison to findings of previous studies, which offer stress as a mechanism that links family adversities experienced in earlier life to compulsive buying tendencies in adulthood (e.g., Rindfleisch et al. 1997; Roberts et al. 2003), our data show that neither family disruptions nor perceived stress of family disruptions is directly associated with the impulse-control dimension of compulsive buying. Rather, our results indicate that the effects of family disruptions are indirect, operating through mechanisms that underscore the importance of social relationships. They support the multitheoretical life course paradigm as a useful framework for identifying the larger array of mediating variables that relate aversive childhood events to compulsive buying tendencies in young adulthood.

Further, our findings provide some insight about the role of the occurrence of disruptive family events versus the duration of these disruptive family events on the onset of compulsive buying tendencies. Our results are in line with previous life course research which shows that the mere occurrence of disruptive family events, regardless of their duration, leads to problem behavior (Hill et al. 2001). This is illustrated by the highly significant total indirect effect of the occurrence of disruptive family events on compulsive buying (total indirect effect = .06, p < .01) while the indirect effect of the duration of disruptive family events was not significant (total indirect effect = .004, p = .59).

The positive effect of concept-oriented communication on compulsive buying in the development of human capital, although only significant at the p < .10 level, was contrary to expectations. As a post hoc investigation of this finding, we split the sample between respondents who had not lived with both biological parents during their childhood (n = 127) and those who had lived with both biological parents during their entire childhood (n = 199), and examined the correlation between compulsive buying and concept-oriented family communication. The dislocated group had a .122 correlation while the correlation in the intact family group was only .031. A chi-square difference test (df = 1) between the groups was not significant, but the results do suggest that youths in disruptive homes may be so disadvantaged that concept-oriented family communication styles in single-parent households do not promote human capital development and instead the single-parent communications tend to reinforce maladaptive behaviors like compulsive buying. Another unexpected finding was the positive association of intangible family support with the frequency of communication with peers about consumption matters. This finding, along with the positive association of concept-oriented family communication style with peer communication, suggests that children in families with supportive parents may develop strong self-esteem and confidence in interacting with others about consumption.

While our data support the view that economic hardship may have adverse effects on the development of impulse-control disorders, as suggested by the negative correlation between SES and compulsive buying (Table 1), our study makes a theoretical contribution by suggesting that it is not the mere experience of family disruptions that has a bearing on the child's relations with his or her parents and friends; rather, it is the degraded SES that appears to have direct and indirect effects on this form of maladaptive behavior. Our findings suggest other consequences of socio-economic hardship due to adverse family disruptions, beyond those suggested by economic hardship theory (e.g., Conger et al. 1994; Elder 1998). Degraded SES status also appears to affect family communication practices and the frequency of communications about consumption with peers, which could be underlying mechanisms of children's development of and susceptibility to impulse control behaviors such as compulsive buying.

The study findings also have implications for those concerned with public education and consumer welfare. They point to the groups of young consumers who appear to be at risk of developing compulsive buying tendencies, either because of economic hardship experienced as a result of lower or degraded SES, or due to less effective socialization practices that parents of disrupted families tend to adopt. Minority females in particular, and youths from socioeconomically disadvantaged families might be the most vulnerable groups. Thus, education and intervention programs should be designed for parents and children. Adult education programs designed for groups at risk and disrupted families should make these consumer groups aware of the parenting and communication styles that may promote and deter the development of compulsive tendencies in their children. Specifically, parents experiencing disruptive events such as divorce and separation should be sensitized to the stressful effects of these events on their children: they should be encouraged to continue providing the same level of emotional support.

Single parents in particular should be informed that their need for tighter supervision of their children using coercive strategies to monitor their behavior (Conger et al. 1994) and disciplining them more harshly (Amato and Sobolewski 2001) is likely to affect their children's perception that their parents are less supportive. Such parenting strategies could affect the parent's communication style with the child and promote the socio-oriented style, which emphasizes conformity to authority rather than self-expression; it does not promote independence and self-direction, which are characteristic of the concept-oriented family communication style and promote early cognitive development (Moschis 1987). The young person's aversive feelings of stress and loss of parental affection, and the degraded SES status created by family disruptions appear to be antecedents to family communication styles and interactions with peers that affect compulsive buying tendencies. Parents in disrupted families and vulnerable groups should not encouraged to provide material support in the form of money or credit cards as a substitute for emotional support, and they should attempt to socialize their children in making proper use of money available to them. Compulsive buying tendencies appear to be fostered among youths that have access to money (Faber 1992), and recent technological innovations (e.g., the Internet) give youths an easy access to a wide array of products and services. Therefore, parents should try to teach their children money management skills at a young age.

APPENDIX: ITEMS USED IN MEASURES

Compulsive Buying

Please circle one number to show the extent to which you agree or disagree with each statement: (1 = Strongly Disagree, 5 = Strongly Agree)

1. When I have money, I cannot help but spend part or all of it.

2. I often buy something I see in a store without planning, just because I've got to have it.

3. Shopping is a way of relaxing and forgetting my problems.

4. I sometimes feel that something inside pushes me to go shopping.

5. There are times when I have a strong urge to buy clothing, tapes, jewelry, etc.

6. I often have a real desire to go shopping and buy something.

7. I have often bought things that I don't need even when I knew I had very little money left.

8. As soon as I enter a shopping center, I want to go in a store and buy something.

9. I like to spend money.

Peer Communication about Consumption

How often did you: (1 = Never, 5 = Very Often)

1. Ask friends for advice on buying things.

2. Talk to friends about buying things.

3. Talk about things you heard advertised.

4. Wonder what friends would think if you bought something.

5. Ask friends for advice about buying things.

6. Tell friends what to, or not, buy.

7. Go shopping with friends.

Socio-Oriented Family Communication Style

How often did your parents: (1 = Not At All, 5 = Very Much)

1. Say that their ideas were correct and you shouldn't question them.

2. Say that you should give in on arguments rather than make people angry.

3. Answer your arguments by saying something like "You'll know better when you grow up."

4. Say that you shouldn't argue with adults.

Concept-Oriented Family Communication Style

How often did your parents: (1 = Not At All, 5 = Very Much)

1. Emphasize that every member of your family should have some say in family decisions.

2. Say that getting your ideas across is important even if others don't like it.

3. Stress that you should make your own decisions on things that affect you.

4. Say that you should always look at both sides of an issue.

5. Admit that children know more about some things than adults do.

Intangible Family Support

(1 = Inadequate Support, 5 = Exceptional Support)

1. Time and attention

2. Discipline

3. Life skills and instruction

4. Emotional support and love

5. Role modeling and guidance

Disruptive Family Events

1. Frequent or lengthy periods in which one or both parents were absent.

2. The loss (other than death) or separation from family members or loved ones.

3. Arguments between parents or other family members (including self).

4. Not living in the same house as both your biological mother and father up to your 18th birthday.

5. Physical abuse by parents.

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Andrew Baker (abaker@mail.sdsu.edu) is Assistant Professor at San Diego State University: Anil Mathur (Anil.Mathur@hofstra.edu) is Professor of Marketing and Brodlieb Distinguished Professor of Business at Hofstra University; Choong Kwai Fall (kwaifatt@um.edu.my) is Associate Professor of Business at University of Malaya, Malaysia: George P. Moschis (gmoschis@gsu.edu) is Alfred Bernhardt Research Professor of Marketing and Director of the Center for Mature Consumer Studies at Georgia State University and Visiting Professor at Mahidol University, Thailand; Edward E. Rigdon (erigdon@gsu.edu) is Professor of Marketing at Georgia State University.

DOI: 10.1111/joca.12008

TABLE 1
Statistics of Variables in the Life Course Model

                                           Average    SD    [alpha]

1. Compulsive buying                         NA *     .93       .91
2. Socio-oriented family communication       NA *     .85       .67
3. Concept-oriented family communication     NA *     .73       .76
4. Peer communication                        NA *     .61       .83
5. Socioeconomic status                        .00    .98        NA
6. Intangible family support                  3.24    .76        NA
7. Perceived family stress                   -0.18    .36        NA
8. Disruptive family events (count)           1.79   1.29        NA
9. Disruptive family events (avg. years)      4.58   4.99        NA

                                            1      2      3      4

1. Compulsive buying
2. Socio-oriented family communication      .15
3. Concept-oriented family communication    .06   -.47
4. Peer communication                       .41    .08    .16
5. Socioeconomic status                    -.14   -.24    .20   -.15
6. Intangible family support                .01   -.30    .51    .13
7. Perceived family stress                  .08    .16   -.23    .09
8. Disruptive family events (count)         .06    .23   -.09    .11
9. Disruptive family events (avg. years)    .12    .22   -.19    .09

                                            5      6      7     8

1. Compulsive buying
2. Socio-oriented family communication
3. Concept-oriented family communication
4. Peer communication
5. Socioeconomic status
6. Intangible family support                .19
7. Perceived family stress                 -.22   -.37
8. Disruptive family events (count)        -.29   -.28   .40
9. Disruptive family events (avg. years)   -.26   -.19   .13   .37

Note: Means are not reported (NA *) for latent variables because
latent means are arbitrary in single group SEM analyses. Cronhach's
alpha ([alpha]) for reflective constructs is reported for the retained
questionnaire items after some were dropped for poor factor
loadings. Correlations of .14 or greater are significant at .05
level.

TABLE 2
Results of the Life Course Model

                                              Hypotheses
                                                   &
                                              Perspective   Coefficient

Compulsive buying ([R.sup.2] = .201)
  Socio-oriented family communication (+)       H13 (h)
  Concept-oriented family communication (-)     H14 (h)      .20 **
  Peer communication (+)                        H12 (h)      .10
  Perceived family stress (+)                   H2 (s)       .37 ***
  Gender (female)                               Covar.       .07
Socio-oriented family communication                          .43 ***
    ([R.sup.2.] = .090)
  Family disruptions (+)                        H5a (n)
  Disruption duration (+)                       H5b (n)      .10 *
  SES (-)                                       H9 (h)       .03 *
  Race (white, non-Hispanic)                    Covar.      -.17 *
Concept-oriented family communication                       -.29 *
    ([R.sup.2] = .064)
  Family disruption (-)                         H6a (n)
  Disruption duration (-)                       H6b (n)     -.01 +
  SES (+)                                       H10 (h)     -.03
Peer communication ([R.sup.2] = .082)                        .17 *
  Perceived family stress (+)                   H3 (s)
  Intangible family support (-)                 H7 (n)       .15 *
  SES (-)                                       H11 (h)      .21 **
  Gender (female)                               Covar.      -.14 **
Perceived family stress ([R.sup.2] = .146)                   .19 *
  Family disruption (+)                         H1a (s)
  Disruption duration (+)                       H1b (s)      .35 **
Intangible family support ([R.sup.2] =                      -.00
    .059)
  Family disruption (-)                         H4a (n)
  Disruption duration (-)                       H4b (n)     -.20 ***
SES (R2=.099)                                               -.10
  Family disruption (-)                         H8a (h)
  Disruption duration (-)                       H8b (h)     -.20 ***
  Race (white, non-Hispanic)                    Covar.      -.04 **
                                                             .57 ***

                                                     Z-
                                              SE    Value

Compulsive buying ([R.sup.2] = .201)
  Socio-oriented family communication (+)     .07    2.65
  Concept-oriented family communication (-)   .07    1.34
  Peer communication (+)                      .07    5.46
  Perceived family stress (+)                 .06    1.10
  Gender (female)                             .12    3.62
Socio-oriented family communication
    ([R.sup.2.] = .090)
  Family disruptions (+)                      .04    2.48
  Disruption duration (+)                     .10    2.00
  SES (-)                                     .08   -2.13
  Race (white, non-Hispanic)                  .12   -2.42
Concept-oriented family communication
    ([R.sup.2] = .064)
  Family disruption (-)                       .01   -1.67
  Disruption duration (-)                     .02   -1.62
  SES (+)                                     .05    2.21
Peer communication ([R.sup.2] = .082)
  Perceived family stress (+)                 .07    2.25
  Intangible family support (-)               .07    3.08
  SES (-)                                     .07   -2.16
  Gender (female)                             .08    2.21
Perceived family stress ([R.sup.2] = .146)
  Family disruption (+)                       .05    6.87
  Disruption duration (+)                     .06   -0.06
Intangible family support ([R.sup.2] =
    .059)
  Family disruption (-)                       .05   -3.47
  Disruption duration (-)                     .06   -1.59
SES (R2=.099)
  Family disruption (-)                       .06   -3.62
  Disruption duration (-)                     .01   -2.98
  Race (white, non-Hispanic)                  .11    5.01

Note: s, stress perspective; n, normative perspective: h, human
capital perspective. CFA (Model 1) fit: ([chi square] (df) = 718.63
(382); CFI = .904: SRMR = .060; RMSEA = .052). SEM (Model 2) fit:
([chi square] (df) = 725.01 (390), CFI = .901, SRMR = .074, RMSEA =
.057).

+ p < .10, * p < .05, ** p < .01, *** p < .001. Only significant
covariates reported in table.
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Date:Jun 22, 2013
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