Personality disorders constitute a major group in the classification of mental disorders. According to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM-IV-TR),1 these conditions are defined by maladaptive personality characteristics
beginning early in life that have consistent and serious effects on functioning. Borderline personality disorder (BPD) is frequently seen in clinical practice.2 Characterized by emotional turmoil and chronic suicidality (suicide ideation and attempts), this type of personality disorder presents some of the most difficult and troubling problems in all of psychiatry. The majority of patients with BPD are seen in psychiatric clinics or in primary care. The keys to successful management include making an accurate diagnosis, maintaining a supportive relationship with the patient and establishing limited goals. Although BPD may persist for years, it does not last forever, and one can be reasonably optimistic that most patients will recover with time.
Psychotherapy can help speed up the recovery from BPD. The most effective forms of treatment have been developed by psychologists, and therefore when making a referral, physicians should consider a patient’s ability to pay for such therapy. More research into the causes of BPD is needed, the results of which may help to develop evidence-based approaches to treatment that are practical and specifically designed for this challenging disorder.
THE EPIDEMIOLOGY OF BORDERLINE PERSONALITY DISORDER
Epidemiologic studies of personality disorders are at an early stage of development. Community surveys of adults have indicated that the prevalence of BPD is close to 1% (similar to that of schizophrenia).3,4 About 80% of patients receiving therapy for BPD are women,2 but sex differences are less striking in community samples.4 As is the case for personality disorders in general, BPD is associated with lower social class and lower levels of education.3,4
THE ETIOLOGY OF BORDERLINE PERSONALITY DISORDER
We are only beginning to understand the causes of BPD. As with most mental disorders, no single factor explains its development, and multiple factors (biological, psychological and social) all play a role. The biological factors in personality disorders consist of temperamental (inborn or heritable) characteristics that present in adulthood as stable personality traits: patterns of thought, affect and behaviour that characterize individuals and are stable over time.5 Heritable factors account for about half of the variability in virtually all traits that have been studied.6
Specifically, both affective instability6 and impulsivity7 have a heritable component of this magnitude, and studies involving twins have demonstrated that BPD itself shows a similar genetic influence.8 Also, family history studies have found that impulsive disorders such as antisocial personality and substance abuse are particularly common among firstdegree relatives of patients with BPD.9 Studies of central neurotransmitter activity have shown that impulsive traits, a major component of BPD, are associated with deficits in central serotonergic functioning.10,11 However, the biological correlates of affective instability are unknown, and no markers specific to the overall disorder have been identified.10
The psychological factors in BPD can be striking but are not consistent. BPD first presents clinically in adolescence, at a mean age of 18 years.12 Although many patients describe adversities such as family dysfunction as well as mood and impulsive symptoms that go back to childhood, longitudinal data are needed to determine the precise influence of early risk factors.
13 Reports of a high frequency of traumatic events during childhood in this population need to take into account community studies, which show extensive resilience following trauma, particularly for less severe adversities.13 The most careful studies have shown that a quarter of patients with BPD describe sexual abuse from a caretaker14 and that about a third report severe forms of abuse.15 However, although child abuse is clearly a risk factor, it is not specific to BPD.13 In general, adverse life events are not consistently pathogenic by themselves but, rather, produce sequelae in vulnerable populations.16
Social factors in BPD are suggested by indirect evidence. Thus far, there have been no cross-cultural studies of BPD, although characteristic symptoms such as recurrent suicide attempts are less common in traditional societies, in which there is little change from one generation to the next, but are on the increase in modern societies and in societies undergoing rapid change.17
DIAGNOSIS AND SYMPTOMS OF BORDERLINE PERSONALITY DISORDER
The term “borderline” is a misnomer, based on an old theory that this form of pathology lies on a border between psychosis and neurosis. Actually, BPD is a complex syndrome whose central features are instability of mood, impulse control and interpersonal relationships.2 Box 1 presents the DSM-IV-TR1 criteria, reorganized in relation to these basic dimensions, as well as cognitive symptoms. Since the DSM-IV-TR requires only 5 of 9 criteria to be present, making a diagnosis on this basis leads to heterogeneity; more precise research definitions have been developed that require high scores for all 3 dimensions.18
The affective symptoms in BPD involve rapid mood shifts, in which emotional states tend to last only a few hours.19 When affective instability is monitored with standardized instruments,20 emotions are found to be intense but reactive to external circumstances, with a strong tendency toward angry outbursts. Levels of affective instability are most predictive of suicide attempts.21 Impulsive symptoms include a wide range of behaviours and are central to diagnosis.22 The combination of affective instability with impulsivity in BPD23 helps account for a clinical presentation marked by chronic suicidality and by instability of interpersonal relationships.23 Finally, cognitive symptoms are also frequent. In one case series,24 about 40% of 50 patients with BPD had quasi-psychotic thoughts. In another series,25 27% of 92 patients experienced psychotic episodes. In a third series,26 psychotic symptoms were found to predict self-harm in patients with personality disorders.
BPD is common in practice. A recent study involving patients in an emergency department who had attempted suicide showed that 41% of those with a history of multiple suicide attempts met the criteria for BPD this disorder.27 However, many cases are also seen in primary care settings. Data from a survey conducted in a US urban primary care practice indicated that BPD was present in 6.4% of a sample of 218 patients.28 Because of the wide range of symptoms seen in BPD that are also typical of other disorders (Table 1), such as mood and anxiety disorders, substance abuse and eating disorders,29 patients may be felt to have one of these conditions while their BPD goes undetected. The most common disorder associated with BPD is depression, but in BPD, symptoms are usually associated with mood instability rather than with the extended and continuous periods of lower mood seen in classic mood disorders.19
Also, because of characteristic mood swings, BPD is often mistaken for bipolar disorder.30 However, patients with BPD do not show continuously elevated mood but instead exhibit a pattern of rapid shifts in affect related to environmental events, with “high” periods that last for hours rather than for days or weeks.30 BPD may be mistaken for schizophrenia; however, instead of long-term psychotic symptoms, patients with BPD experience “micropsychotic” phenomena of short duration (lasting hours or at most a few days), auditory hallucinations without loss of insight (patients with schizophrenia do not recognize that a hallucination is imaginary, whereas patients with BPD do), paranoid trends and depersonalization states in which patients experience themselves or their environment as unreal. 24 Finally, patients with BPD are at increased risk of substance abuse, which forms part of the clinical picture of widespread impulsivity.2
To diagnose BPD in practice, clinicians must first establish whether a patient has the overall characteristics of a personality disorder described in the DSM-IV-TR;1 that is, long-term problems affecting cognition, mood, interpersonal functioning and impulse control that begin early in life and are associated with maladaptive personality traits, such as neuroticism (being easily prone to anxiety or depression, or both) or impulsivity. Personality disorders can often account better for the multiplicity and chronicity of symptoms than can alternative diagnoses such as mood or anxiety disorders.
The next step is a personality assessment, which requires a good history. Although practitioners will be able to obtain needed information from most patients during a routine visit, they may also, with the patient’s consent, wish to speak to family members or friends. The final step is to determine the category that best fits the clinical picture. To diagnose BPD, clinicians need to establish that patterns of affective instability, impulsivity and unstable relationships have been consistent over time.
THE COURSE AND MANAGEMENT OF BORDERLINE PERSONALITY DISODERs
Managing patients with BPD can be burdensome for clinicians because they may have to deal with repeated suicide threats and attempts over years. Also, patients with BPD do not easily respect boundaries and may become overly attached to their therapists.31 When practitioners fail to diagnose BPD, they may be at risk of becoming overinvolved with patients who suffer greatly but can be personally appealing to the physicians.
Fortunately, most patients with BPD improve with time.32–34 About 75% will regain close to normal functioning by the age of 35 to 40 years, and 90% will recover by the age of 50.32 Unfortunately, about 1 in 10 patients eventually succeeds in committing suicide.35 However, this outcome is difficult to predict, and 90% of patients improve despite having threatened to end their lives on multiple occasions.
The mechanism of recovery in BPD is not fully understood, but impulsivity generally decreases with age, and patients learn over time how to avoid the situations that give them the most trouble (e.g., intense love affairs), finding stable niches that provide the structure they need.35
BPD is a therapeutic challenge. A series of randomized controlled trials of pharmacotherapy and psychotherapy36–47,50,52–54 have been published; however, the trials had a number of defects, most particularly small samples, attrition and durations that were too short (usually 8–12 weeks) for a chronic disorder that can last for years. Finally, outcomes in these studies were generally measured by self-report and did not indicate whether the clinical picture had actually shown full remission. The pharmacologic treatment of BPD remains limited in scope. By and large, the result can be described as a mild degree of symptom relief. A number of agents, including low-dose atypical neuroleptics,38 specific serotonin reuptake inhibitors39,41–43 and mood stabilizers,44,45 all alleviate impulsive symptoms.
However, antidepressants are much less effective for mood symptoms in BPD patients than in patients without a personality disorder.48 Benzodiazepines are not very useful in BPD and carry some danger of abuse.49 Thus, although several drugs “take the edge off” symptoms, they do not produce remission of BPD. Failure to understand this point has led to polypharmacy regimens, on the assumption that multiple drugs are needed to target all aspects of the disorder. The result is that many patients receive 4–5 agents — with all their attendant side effects12 — in the absence of evidence from clinical trials supporting the efficacy of such combinations. Future research may lead to the development of agents more specific to the symptoms seen in BPD. The mainstay of treatment for BPD is still psychotherapy.
Dialectical behaviour therapy is a form of cognitive behavioural therapy that targets affective instability and impulsivity, using group and individual sessions to teach patients how to regulate their emotions. This form of behaviour therapy has been shown to be effective in bringing suicidal behaviours under control within a year.50–53 However, whether this method is effective in the long term is unknown.
There is evidence from a randomized controlled trial supporting the use of a modified form of psychoanalytic therapy in a day-treatment setting that also makes us of cognitive techniques.54 Unfortunately, these forms of psychotherapy for BPD are expensive in terms of resources and are not generally available. In practice, therapy tends to be practical and supportive. Practitioners who manage these cases can also use educational materials for patients and their families.31
BORDERLINE PERSONALITY DISORDER AND SUICIDE
The main problem that practitioners face in managing cases of BPD is chronic suicidality. Physicians in primary care settings are prepared to care for many patients with psychotic disorders but are likely to ask psychiatrists to manage patients who make repeated suicide threats and attempts, or to suggest hospital admission. However, there has been little research on the Efectiveness of hospitalization for the treatment of BPD and no evidence that it prevents completion of suicide.55
Suicidality in BPD peaks when patients are in their early 20s, but completed suicide is most common after 3035 and usually occurs in patients who fail to recover after many attempts at treatment. In contrast, suicidal actions such as impulsive overdoses, most often seen in younger patients, do not usually carry a high short-term risk and function to communicate distress.56 Self-mutilating behaviours such as chronic cutting, often referred to as “suicidal,” are problematic but are not associated with suicidal intent and instead serve to regulate dysphoric emotional states.56 Practitioners should move beyond their concerns about these patients and instead concentrate on managing symptoms and the life problems that exacerbate suicidal thoughts or behaviours.
THEORIES OF BEHAVIOR INTENT
Explaining and predicting consumer behavior has been the focus of research for many years. Marketing research seeks to find the answers as to why people make specific choices and how can these be predicted. Are there commonalities among purchasing groups that can be identified as predictors? The literature available is rich, as researchers try to understand the drive forces and motivators of the consumer.
Hovland and Rosenberg (1960) proposed that attitude, acting as an intervening or moderating variable, consists of three components: cognition (knowledge, ability), affect (beliefs, opinions) and conation (behavior or intent of behavior) (Fishbein and Ajzen, 1975; Hansen, 1972). In order for behavior intent to exist, the three components must be present (Fazio & Olsen, 2003).
Fishbein and Ajzen (1975) proposed that attitude does not consist of three components, but is the moderating or intervening variable between cognition and the behavioral intent. Attitude is derived from cognition, which in turn determines the intent to act or not (Ryan, 1982) They proposed that researchers need to look at four categories: 1) knowledge, opinions and beliefs (cognition) about the object, 2) attitude (affect) towards the object, 3) behavior intent (conation) and 4) observed behavior to the object (Fishbein and Ajzen, 1975).
The specific action cannot be determined by the assessment of the knowledge of attitude toward an object but rather through the person’s intention to perform the act (Fishbein and Ajzen, 1975). Previous studies have shown that people may have a positive attitude toward an object; however, the intention of behavior will be negative. This was found in studies concerning blood donation, condom use, and racial prejudice (Ajzen and Fishbein, 2005; Burnkrant and Page, 1982; Fazio and Olson, 2003; Fishbein and Ajzen, 1975).
Although many previous surveys showed favorable attitudes toward blood donation, condom use, and other races, their intention to give blood, use condoms or socialize with racial groups was negative. Therefore, the intent of behavior of an individual must be determined, as well as his beliefs and attitude. An in-depth discussion of each component will be addressed at a later point of this chapter.
Related to the behavioral intent theories is the motivation-opportunity-ability theory of processing information. Although this theory is in response to communication outcomes, the components are relative to this study. According to the MOA theory, a person must have motivation, opportunity, and ability to process information in order to develop an attitude towards a brand, which can be enhanced through advertising cues (MacInnes et al., 1991). Motivation in ad processing refers to the consumers’ willingness to allocate processing resources; whereas, opportunity is the amount of attention that is allocated without disruption; and ability is the “skills or proficiencies” or prior knowledge (MacInnes et al., 1991). Each component of the MacInnes et al. model will be discussed in greater detail.
Cognition – Knowledge, Opinions and Beliefs
The cognition or knowledge, opinions and beliefs component of the Fishbein and Ajzen model is considered to be the driving force of the model. Beliefs about an object are formed through direct observation, with information received from outside sources or by inference processes (Fazio and Olsen, 2003; Fishbein and Ajzen, 1975). The information or knowledge sought in belief formation in a specific situation can be influenced by the effort needed to obtain the information, the time constraint, and the likelihood that the information will be useful (Hansen, 1972).
Opportunity pertains to those distractions or environmental factors which affect the consumers’ attention to information (Agho et al., 1993; MacInnes and Jaworski, 1991; Mooy and Robben, 2002). Fazio and Olsen (2003) further proposed in their MODE or Motivation and Opportunity as DEterminants of attitude-behavior relationship that in order for deliberate processes such as activities used in belief formation, opportunity to engage in the deliberate process must first be available, otherwise, the consumer will resort to memory (Fazio & Olsen, 2003).
Time is reflective of opportunity as it influences consumer behavior and choices. This finite and intangible resource is allocated by the consumer by choice, and is acquired by trading for another resource such as money (Bergadaa, 1990). Therefore, consumers must choose how to use and manage their time. Okada and Hoch (2004) found that consumers place a higher value on time spent if the outcome is positive and a lesser value if the experience is negative. Consumers who have little time pressure will process the information in a leisurely fashion. However, consumers who experience greater time pressure will generally use less time to process the information (Suri and Monroe, 2003).
Therefore, this study will propose that if the consumer has little time or reduced opportunity to expend on search and information gathering, he or she will be more likely to enlist the services of a realtor. However, if the consumer is seeking monetary savings, and believes that time is less than the value of monetary costs, that consumer will participate in a For Sale by Owner transaction.
Reference groups, friends, and family are important resources for the search of information, which is an integral part of buying or selling real estate. This social network provides a means for sending and receiving information. Word-of-mouth communication is important in shaping the attitudes and behaviors of the consumer. “Personal word-of-mouth influence has a more decisive role in influencing behavior than advertising and other marketer dominated sources (Herr et al., 1991). Brown and Reingen (1987) found that the stronger the relationship tie, the more influential the communication. The weaker relationships, on the other hand, were instrumental in developing a bridge in the communication flow and in providing a means for referrals. The opportunity to obtain information increases as the number of people a person comes into contact with increases.
Ability comprises the second component of cognition. Not only does the consumer need opportunity to process information, but he or she must have the skill set or ability to access and process the information (MacInnes et al., 1991; Mooy and Robben, 2002). Any increase in ability can reduce the search process for information, as consumers will rely more on internal information than external information (Gibler and Nelson, 2003).
The Internet has become a primary source for product research. By using the Internet, consumers are afforded the ability to research a specific product, as well as compare products, attributes and prices. “Retail businesses must struggle with facing an era of unprecedented consumer power obtained through Internet information” (Schoenbachler and Gordon, 2002). This phenomenon would apply to the sale or purchase of a home as well (Muhanna, 2000).
Technology and the Internet have provided consumers access to information and products that were previously difficult, if not impossible to obtain, as well as have significantly influenced lowering of search costs. Armed with this advantage, consumers are now afforded with possibilities of researching on the Internet and taking virtual tours, or viewing pictures and descriptions of available properties from the comfort of their own home. The use of the Internet as one source of information will reduce the cost to the consumer during the search process (Baen, 1997; Baen and Guttery, 1997; Bakos, 1998; Seiler et al., 2001; Giaglis et al., 2002).
Ability is an intangible attribute that is often related to age and education. As a person ages, or attains higher levels of education, the level of ability increases (Alba and Marmorstein, 1987; Huneke et al., 2004; Maheswaran and Sternthal, 1990). Age contributes to the informal knowledge base while education contributes to the formal knowledge.
Experience is also often associated with the level of ability (Alba and Marmorstein, 1987; Huneke et al., 2004; Maheswaran and Sternthal, 1990). Alba and Marmorstein (1987) studied the correlation of frequency or the number of times an event occurs, of experience to knowledge levels. The greater the number of times a person was exposed to information or experience, the process of decision making was observed to be faster and less complicated. Furthermore, “task performance is improved by different types of experiences” (Alba and Hutchinson, 1987). Gibler and Nelson (2003) described that experienced home buyers remember which dimensions were useful in the past; on the other hand, inexperienced buyers ar
e more susceptible to external influences, such as real estate agents, in determining their criteria for selection. Therefore, the more homes a person has bought and/or sold, the more experience he/she has gained, and the less likely will that person enlist the services of a real estate agent. The measurement of the levels of ability by the consumer can be ascertained by examining age, education level and prior experience. “The greater the accumulation of experience and knowledge as one ages creates a reduced desire for additional information” (Gibler and Nelson, 2003).
Conation/Motivation – Dependent Variable
Conation is defined as behavior or behavior intent. Fishbein and Ajzen (1975) determined that conation is motivation or behavior intent. Behavior only occurs if motivation is present to perform the behavior. MacInnes et al. (1991) stated in their MOA theory that motivation is defined as the consumers’ desire or readiness to process the information. Therefore, motivation can be defined as behavior intent. Opportunity, measured by time and social contacts, and ability, measured by Internet access, education and experience (cognition) directly influence the level of motivation or behavior intent (conation).
Hovland and Rosenberg (1959) proposed that attitude consists of three elements: cognition, affect and conation. Fishbein and Ajzen (1975) argued, however, that attitude is affect, or the feelings toward a behavior. “Attitudes reflect reasons for acting, and focus on what the decision maker does or can do” (Bagozzi et al., 2003) For the purposes of this study, affect and attitude will be treated as the same and will be referred to as affect. Affect is the result of cognition (Perugini and Bagozzi, 2001).
Therefore, if behavior intent is a result of persuasion and persuasion is the result of cognition, then persuasion will act as a moderating variable. As the persuasion increases positively and based upon previous studies, behavior intent will increase positively. Media habits, or message exposure, will also moderate cognition-affect-behavior intent (MacInnes et al., 1991; Mooy and Rubben, 2003). The higher the levels of exposure to television, radio, newspaper, and internet, the more frequency the messages will occur (Alba and Marmorstein, 1987).
Demographics have been routinely used in marketing to assist in segmenting markets based upon gender, age group, income, culture, marital status, education and household size. These variables are often referred to as demographics; however, as pointed out by Art Weinstein (1994), many variables used for demography are often socioeconomic. It is common in marketing research to refer to all of these variables as “demographics” (Weinstein, 1994).
Demographics are commonly used in business management due to the fact that they are easy to collect, group and analyze. Furthermore, demographic variables typically have an interrelated correlation, which facilitates generalization and analysis of demographic data (Weinstein, 1994).
Household income and household size have a direct correlation with the monetary asset or value. Consumers with lower incomes, or who have a large number of members in the household, are generally more price conscious. Therefore, it is proposed that these consumers would prefer to participate in a For Sale by Owner transaction, foregoing the commissions paid to a real estate agent.
The purpose of this study is to identify those determinants which persuade a consumer to participate in a For Sale by Owner transaction. Therefore, in order to identify these factors, the proposed model is an integration of the three major theoretical models discussed.
Fishbein and Ajzen’s expectancy value model, and Hovland and Rosenberg’s tripartite theory of behavior, provides the cognition-affect-conation model and cognition-attitude-motivation. Integrated with this model, is the MOA model as proposed by MacInnes, Moorman and Jaworski (1991), in which behavior is influenced by motivation, opportunity and ability. Through literature, it has been determined that opportunity and ability are components of cognition, and motivation is influenced by cognition and moderated by affect.
The selected population for this study is the participants of a study conducted by Bluefield State College School of Business. The purpose of the study was to collect raw data regarding the real estate buying and selling behavior of the consumer in the local area, which would be available for future analysis and interpretation. Their sample is composed of participants over the age of 18 at a local annual exposition held in Mercer County, West Virginia. Mercer County has a population of 61, 589 people with a median income of $28,130.
In 2004, 30,207 housing units existed in the County, with 63.5% of the population living in the same house in 2000. The homeownership rate was 76.8% in 2000 (US Census Bureau). The attendance rate at this particular event was approximately 6000 people, approximately 10% of the population (Princeton Mercer County Chamber of Commerce, 2006). Table 2 provides a summation of the demographics of Mercer County, West Virginia, in comparison to the State of West Virginia and United States averages.
Table 2. Demographic Data Mercer County, WV, State of West Virginia and United States (US Census Bureau, 2000)
|Demographic||Mercer County||West Virginia||United States|
|Median Household Income||28,120||32,967||43,318|
|For Sale By Owner||N/A||N/A||13%|
|Living in the same home in 1995 and 2000||63.5%||63.3%||54.1%|
|High School Graduates||72.1%||75.2%||80.4%|
|Bachelor’s Degree or above||13.8%||14.8%||24.4%|
In order to determine the appropriate sample size needed to complete this study, the following formula was used (Malhotra, 372); whereas the number of possible homeowners is 76.8% or 77% (US Census, 2000),
Proportion of population that are homeowners (π) = .70
Desired precision level (D) =.05
Confidence Level (CL) = 95%
z value associated with 95% confidence level =1.96:
Therefore, the number of samples needed:
n = π(1-π)z2/D2
n=272.13 or 272 samples needed
The Bluefield State College study contains 356 usable surveys of individuals rather than households, which is in excess of the 272 samples required for this study. Based upon attendance of 6,000, this represents .0593% or 6% of the attendees surveyed.
The questionnaire developed consists of 42 questions including 35 opinion statements followed by 4-point Likert Scale responses and 8 demographic questions. The Likert responses ranged from “mostly disagree” = 1 to “mostly agree” = 4. Therefore, those who prefer to purchase or sell real estate without the assistance of a real estate agent will answer 1’s or mostly disagree. These questions were drawn from Mitchell’s 1980 VALS; however, drawing from the works of Wells (1975) the constructs were changed to reflect product specific behavior.
H1 As the level of opportunity, measured by time and social contacts, increases, the behavior intent or motivation to buy or sell real estate without a professional agent will increase.
Two variables will be measured to identify the positive or negative level of opportunity. As previously stated in the literature, opportunity is influenced by time and social contacts.
Six opinion statements are used to identify respondents’ attitudes and opinions regarding time, or the lack of time. These statements are followed by four Lickert-scale responses to choose from with 1 = “mostly disagree” and 4 = “mostly agree”. An example statement from the questionnaire is, “I spend more than 40 hours a week outside of the home”. Those respondents, who disagree with this statement, will have more time available to search or sell a home.
Previous research cited has shown that reference groups are an important factor during the information search phase of the decision making process. Therefore, the more people a consumer comes into contact with, the greater access to information. The questionnaire contains eight opinion statements with 4-point Lickert-scale responses. These statements represent the respondent’s network by asking questions in regards to school, community, church and family gatherings.
It is proposed that respondents who have a larger network of social contacts will have access to more information than those who choose not to participate in outside of the home activities. Therefore, based upon the scale responses, 1=mostly disagree and 4=mostly agree, responses that are higher numbers, will most likely have a stronger social network. For instance, the statement “I am active in my community”, reflects the activities of the respondent. If the response is a 4, then the respondent has outside of the home social contacts and access to information.
H1a The direction of the level of affect will moderate the level of motivation to purchase or sell real estate without a professional agent.
In order to determine affect, or beliefs, the survey provided seven belief statements. Respondents responded using a Lickert scale, with “1” = mostly disagree to “4” = mostly agree. A sample statement from the questionnaire is “I believe real estate agents are a necessity when buying or selling a home”. Responses with higher numbers will have a strong belief concerning real estate agents.
H2 As ability, measured by age, education and experience, increases, behavior intent or motivation to purchase or sell real estate without a professional agent will increase.
Ability is measured by three variables: experience, Internet access and education.
In order to determine experience, the survey provides two questions and twelve opinion statements. Experience can be measured by the number of homes purchased or sold in a lifetime. Respondents to the questionnaire were asked to choose 1, 2, 3, or 4 or more. As the number of homes purchased or sold in a lifetime increases, the level of experience increases. The highest possible response will be a 4 and the lowest 1. Furthermore, experience with a real estate agent is questioned. If the respondent had used an agent to buy or sell his/her home the answer would be no, represented by the number 1. If yes, then number 2.
Internet presence, which is also an indicator of information access, is determined in the questionnaire by requesting the respondent to choose which email providers they use for email. The more email providers would indicate a higher Internet usage of the respondent. Also, based upon the provider, it can be determined if the respondent has high speed cable or DSL access. Those respondents without email would respond to “none”.
Information regarding education level will then be analyzed to determine correlation with the questions and statements regarding ability. According to the literature cited, it is proposed that as the level of education, Internet access, and experience increases ability will increase, which will directly impact behavior intent.
Motivation (Behavior Intent)
The next twelve statements contained in the survey are opinion statements regarding the use of real estate agents, brokers and intentions of the respondent. A sample statement from the questionnaire is “I would always use a real estate agent to help with purchasing a home”. Respondents were given four Lickert-scale responses to choose from with 1 = “mostly disagree” and 4 = “mostly agree”. Therefore, “3” and “4” would indicate the respondent’s intent to use a real estate agent, rather than for sale by owner.
H1b An increase in the level of media habits will moderate the level of opportunity and its relationship with motivation to purchase or sell real estate without a professional agent..
H2b An increase in the level of media habits will moderate the level of ability and its relationship with
Media habits, is also an indicator of information access. Survey questions ask respondents the number of hours spent weekly watching television, listening to the radio, as well as newspapers read. It is proposed that as the hours spent watching television or listening to the radio will moderate cognition and behavior intent. As the number of hours exposed to media increases, the level of behavior intent will increase.
H1c Demographics, measured by age, household income and household size will mediate the relationship between opportunity and motivation to purchase or sell real estate without a professional agent.
H2c Demographics, measured by age, household income and household size will mediate the relationship between ability and motivation to purchase or sell real estate without a professional agent.
Demographic information regarding age, household income and household size will be collected. This information will mediate cognition and behavior intent.
Questions concerning gender, marital status and zip code will be used as descriptor or extraneous variables which are not statistically significant in this study.
The data that will be used in this study has been collected by the Bluefield State College School of Business; however, statistical analysis has not been completed. Therefore, raw data obtained will be used for this study.
The first step will be determining the descriptive statistics of the variables used in the study. This will provide the mean, median and standard deviation of each survey question. The aggregate mean will then be used for each variable. The results of this analysis will then be used to conduct inferential statistic analysis.
Inferential statistic analysis will be conducted in four steps. Multiple regression analysis will be conducted to determine the affect of the moderating and mediating variables. The dependent variable is dichotomous; therefore, logit analysis will be conducted, followed by model fit and significance testing.
Due to the existence of several independent variables, mediators and moderators influencing the dependent variable, multiple regression analysis will be conducted to determine the relationships (Hair, 2003, p579). The steps that will be taken to accomplish this, as recommended by Hair (2003, p579) are:
- assess the statistical significance of the overall regression model using the F statistic with a level of significance <= 4.95 (Hair, 2003, p663)
- evaluate the obtained r2 for magnitude which will lie between -1 and 1 and not equal to 0
- examine the individual regression coefficients and their t statistics to see which are statistically significant <= 2.96 ((Hair, 2003, p655)
- examine the beta coefficients to assess relative influence, within a range of .25 to .8 (Lane, 2006).
Multiple regression analysis uses the following formula (Lane, 2006); whereby, Y= predicted variable (For Sale by Owner), X=predictor variables or independent variables: knowledge, wealth, accessibility, and b=beta coefficient.
Y’ = b1X1 + b2X2 + … + bkXk + A
Those variables not meeting the criteria set forth above, will be removed from further statistical analysis.
This study has a binary dependent variable. The respondents are likely to participate in buying or selling real estate without an agent, or they are not. Therefore, the binary logit model will be used to estimate the probability of the behavior intent.
According to Malhotra (2007, p.596), the logit model is as follows:
loge (P/1-P) = a0 + a1X1 + a2X2 + …+akX
P = probability of participating in buying or selling without an agent
Xi = independent variable
ai = parameter to be estimated
The second step in logit analysis, is determining the model fit, which determines the proportion of correct predictions (Malhotora, p.597). The two likelihood functions that will be used in this study are Cox and Snell R square and Nagelkerke R square. Both functions will be used, as Cox and Snell is limited in that the measure can not equal 1; however, the Nagelkerke overcomes this limitation (Malhotora, p.597). Based upon the results of these functions, the predicted values can be compared to actual values to determine the percentage of correct predictions.
The third step in logit analysis is significance testing. Wald’s statistic is used to test the significance of the estimated coefficients. Wald’s statistic is tested as follows:
ai = logistical coefficient for the predictor variable
SEai = standard error of the logistical coefficient
“The Wald statistic is chi-square distributed with 1 degree of freedom if the variable is metric” (Malhotora, p. 597).
All statistical analysis for this study will be conducted using the Statistical Package for the Social Sciences (SPSS) Grad Pack version 14.0.
Data analysis of this data will include exploring the relationship among independent variables and likelihood of behavior intent or motivation. Statistical analysis will follow the stages set forth in the following chart:
The possible limitations of this study include, but are not limited to:
- This study will identify propensity to participate as either buyer or seller. Differences may exist among the two groups, which can be addressed in a future study.
- The questions, although similar to previously published questionnaires, may not result in the same validity.
A detailed summary of the variables and statistical analysis to be used in this study is as follows:
|MOA Theory Categories||Variable fn||Expectancy Value Theory Categories||Variable Name||Statistical Analysis|
|Motivation Intent||DV||Conation||Descriptive Statistics
|Cognitive||Social Contacts||Descriptive Statistics
|Cognitive||Internet Access||Descriptive Statistics
|Media Habits||Moderator||Descriptive Statistics
|HH Income||Descriptive Statistics
|HH Size||Descriptive Statistics
Agarwal, S. and Teas, R.K. (2001). Perceived value: mediating role of perceived risk. Journal of Marketing Theory and Practice, 9(4), 1-14
Ailawadi, K.L., Neslin, S.A. and Gedenk, K. (2001). Pursuing the value-conscious consumer: store brands versus national brand promotions. Journal of Marketing, 65 (1), 71-89.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.
Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracín, B. T. Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (pp. 173-221). Mahwah, NJ: Erlbaum.
Alba, J.W. and Hutchinson, J.W. (1987). Dimensions of consumer expertise. Journal of Consumer Research (1986-1998), 13(4), 411-454.
Alba, J.W. and Marmorsstein, H. (1987). The effects of frequency knowledge on consumer decision making. Journal of Consumer Research (1986-1998), 14(1), 14-25.
American Psychological Association, (2002). Publication Manual of the American Psychological Association. (5th ed.) Washington, D.C.:Author.
Anglin, P.M. (1997). Determinants of buyer search in a housing market. Real Estate Economics, 25(4),567-589.
Baen, J.S. and Guttery, R.S. (1997). The coming downsizing of real estate: Implications of technology. Journal of Real Estate Portfolio Management, 3(1), 10-16.
Bagozzi, R.P., Dholakia, U.M, and Basuroy, S. (2003). How effortful decisions get enacted: the motivating role of decision processes, desires, and anticipated emotions. Journal of Behavioral Decision Making, 16(4), 273-295.
Bakos, Y. (1998). The emerging role of electronic marketplaces on the Internet. Association for Computing Machinery. Communications of the ACM. 41(8), 35-42.
Bamberg, S., Ajzen, I., & Schmidt, P. (2003). Choice of travel mode in the theory of planned behavior: The roles of past behavior, habit, and reasoned action. Basic and Applied Social Psychology, 25, 175-188.
Bergadda, M.(1990). The role of time in the action of the consumer. Journal of Consumer Research, 17(3), 289-302.
Bettman, J.R. and Jones, M.J. (1972). Formal models of consumer behavior: a conceptual overview. The Journal of Business (pre-1986), 45 (4), 544-562.
Black, R.T., Brown, M.G., Diaz, J., Gibler, K., and Grissom, T.V. (2003). Behavioral research in real estate: a search for the boundaries. Journal of Real Estate Practice and Education, 6(1), 85-112.
Brown, J.J. and Reingen, P.H. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer Research, 14(3), 350-362.
Burnkrant, R.E. and Page, T.J. Jr. (1982). An examination of the convergent, discriminant, and predictive validity of Fishbein’s behavioral intention model. Journal of Marketing Research, 19(4), 550-561.
Bushman, F.A. (1982). Systematic lifestyles for new product segmentation. Academy of Marketing Science Journal (pre 1986), 10 (4), 377-394.
Chakravarti, A. and Janiszewski, C. (2003). The influence of macro-level motives on consideration set composition in novel purchase situations. The Journal of Consumer Research, 30(2), 244-258.
Chaudrhuri, A. (2006). Emotion and Reason in Consumer Behavior. Butterworth-Heinemann: Oxford: UK.
Cohen, J.B. and Reed, A.(2006). A multiple pathway anchoring and adjustment (MPAA) model of attitude generation and recruitment. Journal of Consumer Research, 33(June), 1-15.
Fazio, R.H. and Olsen, M.A. (2003). Attitudes, foundations, functions, and consequences. In M.A. Hogg & J. Cooper (eds), The Sage Handbook of Social Psychology, London:Sage,139-160.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley
Fishbein, M., & Ajzen, I. (2005). Theory-based behavior change interventions: Comments on Hobbis and Sutton. Journal of Health Psychology, 10, 27-31.
Gibler, N.M. and Nelson, S.L. (2003). Consumer behavior applications to real estate education. Journal of Real Estate Practice and Education, 6(1), 63-83.
Gilbert, F.W. and Warren, W.E. (1995). Psychographic constructs and demographic segments. Psychology and Marketing (1986-1998), 12(3), 223-237.
Hansen, F. (1972). Consumer Choice Theory: A Cognitive Theory. The Free Press.
Hansen, T. (2005). Perspectives on consumer decision making: an integrated approach. Journal of Consumer Behavior, 4(6), 420-437.
Huffman, C., Ratneshwar, S. and Mick, D.G. (2000). Consumer goal structures and goal-determination process: an integrative framework. Why of Consumption: Contemporary Perspectives on Consumer Motives, Goals and Desires (Ratnewshwar, S. ed). London, UK: Routledge. 9-32.
Huneke, M.E., Cole, C. and Levin, I.P. (2004). How varying levels of knowledge and motivation affect search and confidence during consideration and choice. Marketing Letters, 15(2-3), 67-79.
Kahle, L.R., Beatty, S.E. and Homer, P. (1986). Alternative measurement approaches to consumer values: the list of values (lov) and values and life style (vals). The Journal of Consumer Research, 13(3), 405-409.
Kim, J. and Morris, J.D. (2007). The power of affective response and cognitive structure in product-trial attitude formation. Journal of Advertising, 36(1), 95-106.
Lane, D.M. HyperStat Online, 2006.
Lee, H.J. (2005). Influence of lifestyle in housing preferences of multifamily housing residents. Virginia Polytechnic Institute and State University. Available from Proquest Digital Dissertations. Accessed September 1, 2006. UMI Number 3197977.
Lee, J. and Cho, J. (2005). Consumers’ use of information intermediaries and the impact on their information search behavior in the financial market. The Journal of Consumer Affairs, 39(1), 95-120.
MacInnes, D.J., Moorman, C. and Jaworski, B.J.(1991). Enhancing and measuring consumers’ motivation, opportunity and ability to process brand information from ads. Journal of Marketing, 55(4), 32-53.
Maheswaran, D. and Sternthal, B. (1990). The effects of knowledge, motivation and type of message on ad processing and product judgments. The Journal of Consumer Research, 17(1), 66-73.
Malhotra, N.K. and McCort, J.D. (2001). A cross-cultural comparison of behavioral intention models: theoretical consideration and an empirical investigation. International Marketing Review, 18(3), 235-252.
Mandrik, C.A. (1999). An information processing perspective on between-brand price premiums: antecedents and consequences of motivation. Virginia Polytechnic Institute and State University. Available from Proquest Digital Dissertations. Accessed April 20, 2007. UMI Number 3089086. .
Mason, K., Burton, T.J.S., and Roach, D. (2001). The accuracy of brand and attribute judgments: the role of information relevancy, product experience, and attribute-relationship schemata. Academy of Marketing Science Journal, 29(3), 307-317.
Mitchell, A. (1983). The Nine American Lifestyles. Warner Publishing. New York:NY.
Mitchell,M. and Jolley,J. (1992). Research Design Explained 2nd Edition. Harcourt-Brace. Fort Worth:TX.
Mooy, S.C. and Robben, H.S.J. (2002). Managing consumers’ product evaluations through direct product experience. The Journal of Product and Brand Management, 11(6/7), 432-442.
Muhanna, W.A. (2000). E-commerce in the real estate brokerage industry. Journal of Real Estate Practice and Education, 3(1), 1-16.
Muhanna, W.A. and Wolf, J.R. (2002). The impact of e-commerce on the real estate industry: Baen and Guttery revisited. Journal of Real Estate Portfolio Management, 8(2), 141-152.
Novak, T.P. and MacEvoy, B. (1990). On comparing alternative segmentation schemes: the list of values (lov) and values and lifestyles (vals). The Journal of Consumer Research, 17(1), 105-109
Okada, E.M. and Hoch, S.J. (2004). Spending time versus spending money. Journal of Consumer Research, 31(2), 313-323.
Plummer, J.T. (1974). The concept and application of lifestyle segmentation. Journal of Marketing (pre 1986), 38(1), 33-37.
Ryan, M.J. (1982). Behavioral intention formation: the interdependency of attitudinal and social influence variables. Journal of Consumer Research (pre-1986),9(3), 263-279.
Seiler, M.J., Seiler, V. L., and Bond, M.T. (2001). Uses of information technology in the real estate brokerage industry. Real Estate Issues, 6(1), 43-53.
US Census Bureau, 2000 Census.
Weinstein, A. (1994). Market Segmentation. Irwin Professional Publishing, Chicago.
Wells, W.D. (1974). Life Style and Psychographics. American Marketing Association: NY.
Wells, W.D. (1975). Psychographics: a critical review. Journal of Marketing Research (pre 1986), 12(2), 196-213.
Wuensch, K. (2006). Binary Logistic Regression with SPSS. Kurt Wuensch’s Statistics Lessons.
Zeithaml, V.A. (1988). Consumer perceptions of price, quality and value: a means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22