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<ModellerKapittel 5 av 25

4. The influence of syncretic decision-making on purchase intentions: A study of children’s furniture in India



Department of International Business, Norwegian University of Science and Technology (NTNU in Ålesund)

MARK PASQUINE er førsteamanuensis ved NTNU ved Institutt for internasjonal forretningsdrift (IIF). Han har doktorgrad fra NHH og MBA fra Babson College. Pasquine har flere års praktisk erfaring innenfor «Account Management» i markedsføringsselskap.



bChannels Ltd., Oxford, United Kingdom

ANDREJS BOGDANOVS har arbeidet som «Market Intelligence Specialist» i bChannels Ltd. Oxford (United Kingdom) siden 2016. Han har mastergrad i Internasjonal Business og Markedsføring fra NTNU.



Department of International Business, Norwegian University of Science and Technology (NTNU in Ålesund)

AKARSH KAINTH er stipendiat ved NTNU i Ålesund. Han har mastergrad i Internasjonal Business og Markedsføring fra NTNU i Ålesund.

Kjøpsbeslutninger i familier er ofte en felles beslutning som inkluderer begge ektefeller. Dette tyder på et behov for å forstå hvordan felles beslutningsprosesser påvirker kjøpsbeslutninger. En viktig teori i forbrukeratferd er teorien om planlagt adferd «Theory of planned behavior (TPB)». Innenfor denne teorien er det utforsket hvordan subjektive normer påvirker kjøpsintensjonen. Selv om ektefellens innflytelse på kjøpsintensjoner vanligvis betraktes som en del av subjektive normer, finner vi at det må behandles separat i noen sammenhenger. Vi foreslår at felles beslutningsprosesser påvirker flere aspekter av Theory of Planned Behavior (TPB). Tidligere forskning gir konkurrerende argumenter for ektefellens rolle (Spousal Involvement) i beslutningsprosessen, som at økt involvering enten bør føre til lavere intensjoner for kjøp eller høyere intensjoner for å kjøpe eksklusive produkter. Vi finner støtte for sistnevnte. Vi tester ektefellens påvirkning som et spesifikt tilfelle av subjektive normer når det gjelder kjøp av produkter til felles forbruk. En modell er testet i en indisk kontekst, og resultater fra en estimert strukturmodell viser at felles kjøpsbeslutninger (dvs. felles beslutningsprosess) har en positiv innflytelse på kjøpsintensjonen for eksklusive produkter.

Purchase decisions in families are often a joint decision including both spouses, suggesting a need to understand how joint, or syncretic, decision-making influences purchase decisions. A major theory in consumer behavior is the Theory of Planned Behavior (TPB). Within the TPB, subjective norms is explored to influence purchase intention. Although a spouse’s influence on purchase intentions is usually considered a part of subjective norms, we find that it needs to be treated separately in at least some contexts. We propose that the degree of syncretic decision-making influences several aspects of the Theory of Planned Behavior (TPB). Specifically, prior research suggests competing arguments for the role of spousal involvement, that is either increased spousal involvement should lead to lower intentions to purchase or higher intentions to purchase high-end products. We find support for the latter. We test spousal influence as a special case of subjective norms in the context of purchasing products meant for joint consumption. A model is tested in an Indian context and results from an estimated structural equation model reveal that purchase decisions made jointly (i.e syncretic decision-making) have a positive influence on purchase intention for high-end products.

KEYWORDS : spousal influence, syncretic decision-making, purchase intention, Theory of Planned Behavior, structural equation modeling

Merknad

Forfatterne har ingen interessekonflikter.

REMARKS

The authors of this book chapters have no conflicts of interest

4.1 Introduction

Consumer purchasing behavior is dependent on purchase intentions. For example, that marketing managers measure purchase intention in order to make strategic decisions with respect to both new and existing products, and create programs that support them, is not new (Urban & Hauser, 1993). According to Whitlark, Geurts, and Swanson (1993), purchase intention is a purchase probability associated with the percentage of individuals that will actually buy the product. Complementarily, Samin et al. (2012) argue that “intention is the person’s motivation in the sense of his or her intention to perform behavior”. A further description of purchase intention is “what we think we will buy” (Samin et al., 2012). When a company wants to create a new product, they use measurements of purchase intention in concept testing. This helps managers to define whether their product concept needs further development and whether it is worth it to launch in the market. Moreover, in planning the launch of a new product, purchase intention measurements help the firm to conduct demand forecasts and find in which markets and towards which consumer segments the product should be targeted. The marketing manager must understand her product’s potential market acceptance and must therefore be careful in predicting consumers’ future purchase behavior (Barber, Kuo, Bishop, & Goodman, 2012; Juster, 1966; Seawall, 1978; Silk & Urban, 1978; Sun & Morwitz, 2010; Urban & Hauser, 1993; Zhao, Geng, Liu, Tao, & Xue, 2018). Hence, it is important to understand the theoretical foundation and key drivers of purchase intention.

In exploring consumer’s intentions and usage behavior, researchers often adopt behavior theories from psychology and marketing. The theory of planned behavior (TPB) was proposed by Ajzen in 1985 as an extension of the Theory of Reasoned Action (TRA); formulated in 1975 by Fishbein and Ajzen. According to the TRA, individuals evaluate the outcome of a particular behavior and create intentions to act in ways consistent with their evaluations, whereas the TPB also attempts to take into account situations in which individuals do not have a complete control over their behavior. The theory of planned behavior has been used extensively in marketing research and is widely accepted as a model to predict purchase intentions in many contexts (e.g. online purchasing, organic food, real estate market) (Al-Nahdi et al., 2015; Arvola et al., 2008; George, 2004; Ketabi, Ranjbarian, & Ansari, 2014; Omar, Nazri, Osman, & Ahmad, 2016; Yazdanpanaha & Forouzania, 2015). However, to our knowledge the TPB has not been tested with the inclusion of familial relationship complexity when it comes to joint decisions. Several researchers argue that this complex interplay between family and relational identity bundles has generally been neglected (Cotte & Wood, 2004; Parkinson, Gallegos, & Russel-Banette, 2016). We have tested spousal influence which represents the degree of spousal influence in decision-making, as a special case of subjective norms in the TPB. The research question is thus: does increased spousal influence positively influence purchase intentions?

Hence, the current article investigates spousal influence in decision-making as a special influence on purchase intentions in the TPB. We find that spousal influence is a significant predictor of purchase intentions, however it has lower predictive power than attitudes and perceived behavior control.

This paper contributes to the existing literature in the following ways: we address the complex interplay between family and relational identity bundles and second, we test the theory of planned behavior with spousal influence, which to our knowledge has not been tested before.

In the following sections, we will discuss the Theory of Planned Behavior as it relates to purchase intentions and generate hypotheses regarding how purchase intentions may be influenced by spouses in decision-making. This will be followed by an analysis of results and a general discussion.

4.2 Theory

4.2.1 Purchase intentions and the Theory of Planned Behavior

According to the TPB, perceived behavioral control, together with behavioral intention, can be used directly to predict behavioral action. Behavioral intentions capture the motivational factors that influence the behavior of an individual. These factors consist of indicators such as to what extent people are willing to try or how much of an effort they are planning to make in order to perform the behavior. Ajzen (1985) identified perceived behavioral control as people’s perception of the complexity or difficulty of performing the behavior of interest. A person may believe that, in general, her outcomes are determined by her own behavior (internal locus of control), but at the same time she may also believe that her chances of taking certain roles are very low (low perceived behavioral control) (Ajzen, 1991). Perceived behavioral control is believed to moderate the effect of intention on behavior. For instance, if a consumer has favorable intentions with respect to a particular product, he will produce behavior only when he perceives strong behavioral control (Ajzen, 1985, 1991; Ajzen & Fishbein, 2005).

Two other key antecedents of purchase intentions in the TPB are subjective norms and attitude toward the behavior. Subjective norms represent the interpersonal power of referents on an individual and are dependent on normative beliefs that result in perceived social pressure (Fishbein & Ajzen, 2010). Social pressure usually is expressed through reference groups. Beliefs have been defined as convictions or opinions a consumer has about something (Rani, 2014) and are developed through experience and other external influences such as family, friends or other reference groups. Attitudes are a result of beliefs and are developed as a part of a learning process. It can be referred to consumer’s evaluation of the products or a predisposition to react negatively or positively towards a certain brand or product (Hawkins, Best, & Coney, 2001). The attitude with respect to a certain behavior is determined by the person’s individual perceptions of positive outcomes or outstanding results associated with the behavior and by the strength of these associations. This in turn contributes to the attitude in proportion to the person’s subjective probability that the behavior will produce the outcome in question (Ajzen, 2005). Behaviors are linked to a certain outcome or to some other feature, such as the cost incurred by performing the behavior through each of the behavioral beliefs (Ajzen, 2005). Hence, attitude toward behavior can be explained as a function of the salient (behavioral) beliefs about the perceived consequences of performing the behavior and their evaluation (outcome) (Ajzen & Fishbein, 1980). This estimate is based on the person’s accessible beliefs about the behavior. The expectancy-value model is thus described in Equation 1.1, where A B stands for attitude toward behavior B; b i is the behavioral belief that performing behavior B will lead to an outcome i; e i is the evaluation of an outcome i; and the sum is over the number of behavioral beliefs accessible at the time (Ajzen, 2005):

(1.1)

Many researchers have used the expectancy-value model of attitude as described in Equation 1.1 (Ajzen, 2005; King, 1975). In the current research both direct measurement and the expectancy-value model of attitude were applied. However, in model testing process it was found that direct measurements of attitudes towards purchase of high end furniture have a high degree of multicollinearity with respect to purchase intention measurements, thus in later stages of research we selected the expectancy-value model.

The TPB has been found to be a robust and powerful predictor of behavior and behavioral intentions for individuals (Giles, McClenahan, & Mallet, 2004; Mausbach et al., 2013; Sommer, 2011). However, to our knowledge it has not been used to explain decisions in which more than one person is involved in the purchase decision, such as with purchases for a family.

4.2.2 Autocratic and syncretic influences on family decision-making

For family purchases, all family members may be directly or indirectly involved in the decision-making process (Chrisman, Chua, pearson, & Barnett, 2010; Harcar, Spillan, & Kucukemiroglu, 2005). In addition to decisions made between spouses (Bednarik & Kovats, 2010; David, 1970; Ford, LaTour, & Henthorne, 1995; Szybillo & Sosanie, 1977), research regarding the role of children in decision-making has increased over the past several decades (Ashraf & Khan, 2016; Chavda, Haley, & Dunn, 2006; Ekstrom, Tansuhaj, & Foxman, 1987; Conway Lackman & Lanasa, 1993; C. Lackman & Lansa, 1993; Levy & Lee, 2004; Schiffman, Kanuk, & Hansen, 2012; Sondhi & Basu, 2014; Valkenburg & Cantor, 2001), as has the research related to the influence of elderly parents on their children (Bedway, 1996; Dunifon, 2012; Dunifon & Bajracharya, 2012; Levy & Lee, 2004; Ward, 1974). Decisions in families can be autonomous, where decisions are made by a single member, or joint, involving more than one family member (spouses or for non-married couples, partners, elderly parents, children, etc.). Traditionally, some autonomous purchase decisions, termed autocratic decisions, were predominantly made by one spouse. Men, for instance, often had responsibility for selecting a car, while most decorating choices fell to women. Other decisions, such as holiday destinations, were made jointly; these decisions are known as syncretic. Syncretic decisions are common for cars, homes, appliances, furniture, home electronics, interior design and long-distance phone services (Solomon, Bamossy, Askegaard, & Hogg, 2007). As the couple’s education increases, more decisions are likely to be made jointly (i.e. syncretic decisions) (Crispell, 1995). Joint (syncretic) decision-making in families is likely to happen in the case of purchasing products meant for joint consumption, such as furniture. However, a likely outcome of syncretic decision-making is that conflicts may need to be resolved.

Family decisions consist of several specific factors determining the degree of conflict, including interpersonal needs, product involvement and utility, responsibility and power distribution within the family. Interpersonal need is defined as an individual level of investment in the group. For example, a spouse will likely care more about what her family will buy for her children, than for her neighbor’s or friend’s children. Product involvement and utility is a degree to which the discussed product will be in use or will satisfy a need of each particular family member (H. L. Davis, 1976; Seymour & Lessne, 1984; Smith, McArdle, & Willis, 2010). For example, a parent who is a fashionable design lover will be more motivated to purchase new designer furniture for his kids rather than buy a more generic product that is cheaper. Another factor is responsibility with respect to procurement, maintenance, payment, etc. When it comes to purchases, family members are more likely to have disagreements about a decision if it requires long-term consequences and commitments. For example, a family’s decision about getting a new house, car or furniture may involve conflict regarding who will be responsible for payments. Finally, the last factor is power, or in other words, the degree to which one family member expresses influence over others in making decisions. In many traditional families, husbands are perceived to have more power than wives, who have more power than the oldest child, who has more power than his younger siblings and etc. (Seymour & Lessne, 1984). Overall, decisions often involve conflict among family members to the extent of their importance or newness and/or if people have strong opinions with respect to what should be considered as good and bad alternatives. The degrees to which these factors generate conflict can determine the type of decision the family will make (Beatty & Talpade, 1994; Chrisman et al., 2010; Foxman, Tanushaj, & Ekstrom, 1989; Smith et al., 2010). The current research intends to identify to what extent spousal influence in decision-making influences purchase intentions.

4.2.3 Family decision-making and the Theory of Planned Behavior

Research has focused on decision-making shifting from the individual to joint level (Ruth & Commuri, 1998). An early study on family decision-making taking into account the influence of both a husband and wife was performed by David (1970). Since then several other studies have looked closely at several components of marital roles and family decision-making (Bednarik & Kovats, 2010; Ford et al., 1995; Szybillo & Sosanie, 1977). Researchers also looked at households as a relevant unit of analysis rather than the individual consumer (H. L. Davis, 1976; Granbois, 1971). Strong organized modern families make more syncretic decisions on products for joint consumption than traditional families (Nelson & Jenny, 2005). Findings from the literature suggest that modern society has a trend toward syncretic decision-making and it usually involves the spouse and in some cases children (Litvin, Xu, & Soo, 2004; Ruth & Commuri, 1998; Wang, Hsieh, Yeh, & Tsai, 2004). Moreover, prior research has found that for some product categories the spouse may have dominant influence over a purchase decision (Levy & Lee, 2004). Further support of this is provided in a study by Qualls (1984), where he compares spousal influence in old and modern concepts. In modern concept families (sex-role moderns), the results revealed that there will be a compromise or dialogue regardless of whether the husband has more influence than wife (Qualls, 1984). Thus, we assume that for some product categories it would be appropriate to observe the consumer decision-making process from a joint rather than individual perspective. Although spousal influence is a normally considered as a dimension of subjective norms, we find it important to examine spousal influence as a specific special case of subjective norms in consumer decision-making. Hence, the current study measures spousal influence instead of subjective norms in the TPB. Based on previous research findings (Levy & Lee, 2004; Qualls, 1984; Wang et al., 2004), we use a construct we term “spousal influence”. We assume that this construct will have a direct influence on purchase intentions in specific product categories where the decision-making process is syncretic (e.g. furniture for children). In the current study, spousal influence is defined as the degree of both spouse’s participation in the decision-making process (from autocratic to syncretic decision-making). The focus is on how families reach a purchase decision.

4.3 Hypotheses

Prior research has generally neglected the complex interplay between individual, relational and family identity bundles in decision making (Parkinson et al., 2016). This interplay was seen at the aggregate level within the reference group as a whole, where the spouse was considered as a part of this group. However, research suggests that close family members’ influence is much higher than other reference groups’ (Levy & Lee, 2004; Qualls, 1984) because families form an identity which is different from a relational identity (Epp & Price, 2008). Moreover, relational units differ within the family in terms of relational characteristics. Spousal relation will be different from that of other referent groups as spouses interact with each other throughout the day than with the other reference groups (Epp & Price, 2008). In the current research, we looked at spousal influence from a syncretic rather than individual perspective and separately from the reference groups as a special case of subjective norms, due to its significant influence on decision-making.

Spousal influence is normally assumed to be simply one aspect of subjective norms in TPB. However, in situations when the decision-making process is shared jointly between spouses, the influence of other individual’s (normative) beliefs regarding what each referent thinks the spouses should do and the motivation to comply with them will be weak, as spouses in syncratic decision situation can be considered as the main referents to each other.

4.3.1 Spousal influence and purchase intentions

Syncretic decision-making is a complex process and may have influence on behavioral intentions in various ways. The syncretic decision-making process begins when one of the family members recognizes a need and if the outcome (result) of need recognition has an impact on other family members and he/she has influence in the decision-making process. Depending on factors such as sex-role stereotypes, spousal resources, experience, and socio-economic status, the decision-making process in families can be autocratic or syncretic (Burgoyne, 1995; Kirchler, 1993; Lavin, 1993; Pahl, 1995; Sullivan & Connor, 1988; Timmins, 1996). If the process is syncretic, then a family member individually or jointly with others will start looking for information about products that can satisfy their member’s needs. In the information search process family members try to reach individually what they perceive as consensus. According to Kahle and Close (2006) the nature of influence of friends and family members in the information search stage and consumer decision-making process in general depends on a range of factors such as the nature of relationships, the degree of individual influence and the extent of “opinion leadership” with respect to specific individuals.

Evaluation of alternatives can involve all family members. Parties’ involvement in this process can be predicted based on relative distribution of power among family members’ in the decision making process. The final stage in the syncretic decision-making process is conflict resolution. Sheth (1973) argues that joint decision-making in industrial organizations includes conflict resolution among the parties who must jointly decide, in addition to need recognition, information search and evaluation of alternatives. Conflict resolution in organizations comprises of problem solving, persuasion, bargaining and politicking (Sheth, 1973). Similarly according to findings from past research, it be can concluded that in family decision-making, bargaining, enforcement, compromise and the expression of power are likely to be used in order to achieve agreement on purchase and its exploitation of terms and conditions (Dortch, 1994). Based on these similarities between industrial organizations and family decision-making processes, we propose that just as in industrial organizations, syncretic decision-making can involve a conflict resolution stage.

The experience of each individual in family is an important factor when it comes to making a purchase decision because consumers tend to rely more on information from close family members and friends before seeking other external sources (Rani, 2014). Thus, family members with more experience in particular product category may have an advantage over other members with lack of such experience. According to H. L. Davis (1976), the higher the motivation and interest of individuals in the purchase of a particular product/service, the more influence they have on the decision. For instance, a person that perceives a particular product category as less important has lower motivation to protect their position in the bargaining process than other family members and will likely delegate the decision to other family members who find the purchase more important.

Syncretic decision-making prevails when there is a great deal of perceived risk with regard to product attributes (Moutinho, Ballantyne, & Rate, 2011). According to Sheth (1974) the higher the perceived risk for buying a product the greater the need for joint decision-making. In general, a negative relationship between perceived risk and purchase intentions has been identified (L. H. Kim, D. J. Kim, & J. K. Leong, 2005; Mitchell, Davies, Moutinho, & Vassos, 1999; Sweeney, Soutar, & Johnson, 1999). Perceived risk theory suggests that buyers tend to minimize the perceived risk first, rather than to maximize the expected positive outcome (Sheth & Vankatesan, 1968). Moreover, in situations when consumers’ perceived risks have been identified in a buying situation, there seems to be some evidence to suggest that subsequent consumer behavior is in accordance with such a risk reducing strategy (Taylor, 1974). G. B. Davis and Olsen (1985) argue that perceived risk is a significant source of psychological stress that results in poor decision-making. Consumers tend to be unwilling to make a purchase in situations when they perceive high risk about the quality of the product, new payment methods, delivery options, and information content (GVU, 1998). One of the main sources of the risk reduction are information from friends/family and past experience. In joint decision-making spouses share their experience and have more information regarding product (Samadi & Yaghoob-Nejadi, 2009). Thus in syncretic decision-making, the consumer should have more information, less physiological stress and consequently higher intentions to purchase products of joint consumption compared with situations in which they decide alone.

However, there is contradictory evidence in the literature with regard to syncretic decision-making. Bateman and Munro (2005) found that in a situation involving monetary payoffs from a lottery, couples making joint decisions are more risk averse than spouses making individual decisions, meaning syncretic decisions tend to favor the less risky option. Alternatively, Palma, Picard, and Ziegelmeyer (2008) assert that couples making syncretic decisions are less risk averse than each spouse on their own and that a spouse’s autocratic decision favors the less risky choice in a context similar to that in Bateman and Munro (2005). In the case of products meant for joint consumption such as children furniture, consumers spend time in gathering information and making comparisons as opposed to choosing between lotteries where uncertainty hinders gathering information. The inclusion of the other spouse in the decision should thus reduce the risk of making a poor decision and psychological stress level, and make customer more informed. This decision partnership also spreads the risk of making an incorrect choice over more than one person; thus reducing risk for each individual. Hence, the following hypothesis is offered:

H1: Increased influence of the spouse in decision-making (i.e. higher syncretic decision making) has a positive influence on purchase intentions for jointly consumed products.

4.3.2 Perceived behavioral control and syncretic decision-making

As noted earlier, perceived behavioral control is a person’s perception of the complexity or difficulty of performing the behavior of interest (Ajzen, 1991). This in turn is influenced by the ability to control barriers to behavior and presence of adequate resources (Hardin-Fanning & Ricks, 2017). The current study focuses on the decision-making process in product categories where decision-making is expected to be joint. In such cases, consumers might expect to have advice from their spouse and do not expect to have full control over their purchase behavior. If the involvement of spouse has negative influence on perceived behavioral control then it is also logical to assume that in certain product categories where decision-making process is expected to be joint spousal influence has negative impact on perceived behavior control. A high degree of perceived behavioral control thus also implies independent and self-confident behavior. Thus, being very confident in one’s ability to perform a behavior should lead to increased perceived behavioral control (Kidwell & Jewell, 2003). This should reduce the probability of syncretic decisions between the spouses and vice-versa and indirectly reduce intention to buy. Thus, the following hypothesis is offered:

H2: There is a negative relationship between perceived behavioral control and increased influence of the spouses.

Figure 4.1

Conceptual Model (adapted from Ajzen (1985)).

The conceptual model shown in figure 4.1 includes our addition of spousal influence as an alternative measure of subjective norms to predict purchase intentions. This emphasizes the influence of a syncretic decision perspective in consumer behavior. Moreover, other components that are shown in the model from TPB can be also considered as variables that are dependent on each individual’s decision-making process. Purchase intention is the main dependent variable in the conceptual model and is expected to be predicted by the following independent variables: perceived behavioral control, spousal influence, and attitudes towards purchase.

4.4 Methodology

This research uses a self-administrated online questionnaire in English to identify relationships among purchase intentions and other influential factors such as attitude toward the purchase, spousal influence and perceived behavioral control based on the TPB. Participants are spouses with children from Indian nuclear families living in a metropolitan city or couples about to become parents with income level above 60000 INR per person. Heterosexual married couples were specifically chosen as they represent over 70% of family types with children in the Indian context (Raja, 2014) and should allow for a more controlled test of our hypotheses. The married couples sample chosen belongs to the upper middle class and is considered to be the main consumer group that can afford to buy high-end children furniture. The Indian children’s furniture market was selected for the context of this study as it represents a large customer base, has wide variation in the amount of syncretic decision-making in families, and India being an emerging economy with a booming market and rapidly increasing economic growth. Research by Ruth and Commuri (1998) also suggests that the Indian society is trending towards decision-making where both the partners are involved. Therefore, more product categories in modern Indian society should involve some degree of collaboration between husbands and wives, rather than the single spouse dominated decision. The increasing use of syncretic decision-making in India makes it an appropriate context for our study.

142 respondents from a web panel participated in the survey in 2016. As we were interested in more affluent consumers, criteria included married people with children in Mumbai, aged 21+, with an income level of Rs. 60,000+. 39% of the sample is female and the mean age of respondent is 34 years. 42% of respondents have 1 child and 52% have 2. 27% of respondents are expecting a child. The survey was designed in Lighthouse Studio 9.0.1 (formerly SSI Web) software. This software was also used to collect and analyze data, along with SPSS and AMOS 25 for estimating structural models.

All indicators in the proposed research model are measured with sliders based on semantic differential scales (see Appendix for scale items). Due to a later need to recode some variables, which will be discussed below, scales from 0 to 100 were used. The numerical value of these scales was hidden from respondents to reduce the complexity associated with indicating a number between 0 and 100. Respondents were only shown value labels; such as very unlikely, unlikely, somewhat unlikely, neutral, etc.; at evenly spaced intervals along the slider. According to the survey instructions, respondents were free to set values at any point on the slider scales.

4.4.1 Attitude towards behavior

Attitudes towards behavior are often highly correlated with purchase intentions leading to problems of multicollinearity. On common way to deal with this issue is to use a calculated estimate of attitudes, rather than stated measures. Attitudes towards behavior in current study are estimated by multiplying belief strength and outcome evaluation. The estimation outcome is standardized back to a scale of 0 to 100 for further estimation purposes (Cronbach’s α = .910, n = 142). Belief strength and outcome evaluation are each measured on 3-item scales as per Fishbein and Ajzen (2010).

4.4.2 Perceived behavioral control

Perceived behavioral control was measured by a 4-item scale adopted from Ajzen (2013); Fishbein and Ajzen (2010) and Pavlou and Fygenson (2006). Respondents indicated their confidence in purchasing high-end children’s furniture by themselves, if the decision is up to them, if the decision is entirely within their control and their confidence that if they wanted to, they could purchase such furniture (from almost never true to almost always true) (Cronbach’s α = .861, n = 142).

4.4.3 Spousal influence

A common scale of spousal influence (used as a latent variable) was developed by H. L. Davis and Rigaux (1974). We adopted this scale to be anchored by autocratic and syncretic decision-making. Respondents were asked who recognizes the need to purchase furniture for their children, who obtains most of the information about the purchase, who provides and collects information about possible alternatives and who makes final choice when it comes to purchase of furniture for children (from only me to only spouse). In order to define whether decision is autocratic or syncretic, scores were recoded based on following procedure: scores from 0 to 50 remains same, scores from 51 to 100 were reversed and recoded as 0–49. For example, 100 was recoded as 0.99 was recoded as 1, etc. A score of 0 indicates purely autocratic decision-making and a score of 50 indicates entirely syncretic. Finally, scale values were multiplied by 2 to recreate a scale of 0–100 to be consistent with all other variables (Cronbach’s α = .839, n = 142).

4.4.4 Purchase intentions

Purchase intentions were measured by a 3-item scale adopted from: Ajzen (2013). Respondents were asked about their intentions, plans and amount of effort they would make to purchase high-end furniture for their children (Cronbach’s α = .899, n = 142).

4.5 Results

A structural model was estimated using AMOS 25 to test hypotheses 1 and 2. Purchase intention is the dependent variable, along with the following independent variables: perceived behavioral control, spousal influence, and attitudes towards purchase. Overall fit of the model was quite good (c2(70, n = 142) = 100.23, p > .01, CFI = .978, RMSEA = .055 [90% CI = 0.028, 0.079]).

The results presented in figure 4.2 and support the relationships proposed in the hypotheses. To test the hypothesis 2 we constrained the paths between spousal influence and perceived behavioral control between perceived behavioral control and spousal influence both from spousal influence to perceived behavioral control (H2: β= –0.34, p < 0.001) and from perceived behavioral control to spousal influence (H2: β= –0.10, p < 0.001).

Explanatory power of the model in predicting purchase intentions was very good (R2 = 0.85). The results presented in figure 4.2 show that the estimated structural model is able to explain 85% percent of the variance in purchase intentions for high-end children’s furniture. Overall, it can be concluded that in the current research, variance of the main dependent variable is highly predicted by the independent variables.

Figure 4.2

Final Estimated Model (Standardized coefficients).

4.6 Discussion and conclusion

The influence of the spouse on decision-making is the focus of the current research. To our knowledge, this construct has not been tested as a part of the TPB and we believe it needs to be treated as special case of subjective norms, in some contexts. The authors proposed a positive relationship between spousal influence on purchase intentions, and negative relationships between spousal influence and perceived behavioral control. These relationships were supported by findings from a survey.

4.6.1 Theoretical Implications

In families characterized by higher levels of syncretic decision-making, individuals had higher intention to purchase high-end furniture for their children. Past research findings presented by Bateman and Munro (2005) shows that consumers tend to be more risk averse if the purchase decision is syncretic. Other studies by and Liang and Wei (2011) found that a reduction of perceived risk leads to an increase in purchase intention. Our findings support the latter. Syncretic decision-making may reduce perceived risks resulting in increased purchase intentions.

The findings suggest that relationship between joint spousal influence and perceived behavioral control is negative. Hence, increases in spousal influence are associated with individuals perceiving less control over purchase decisions. Or in other words a high degree of spousal influence is related to increased perceptions of the complexity of performing the behavior of interest. We argue that increased spousal influence lead to lesser feelings of self-control and independence that make up behavioral control. Hence, it appears that joint spousal influence has a negative influence on perceived behavioral control.

4.6.2 Managerial implications

The findings in this study suggest that spousal influence can complement traditional purchase intention measurements derived from the theory of planned behavior. It has positive direct effect on purchase intention and is negatively associated perceived behavioral control. Hence, it seems that in situations where spouses jointly make decisions, purchase intentions for high-end children’s furniture will be higher due a reduction in perceived risk. An implication of this is that marketing managers should design promotions in a way that will lead the spouses to make purchase decisions jointly. For example, in advertisements the “consumer” could be represented by a couple who discuss opinions and intentions jointly before making their purchase together.

4.6.3 Limitations and recommendations for future research

This study explored factors affecting purchase intention of high-end children furniture. In doing this, we substituted spousal influence for subjective norms in the TPB. We were interested in explicitly testing the influence of spousal influence on purchase intentions. This can be considered a weakness in that we have deviated from the traditional full model of the TPB. Future research could include a measure of subjective norms that explicitly asks respondents to not consider their spouse in questions assessing the subjective norms construct in addition to the specific measure of spousal influence we used here. We could then test a model including attitude towards the behavior, perceived behavioral control, spousal influence and non-spouse subjective norms as predictors of purchase intentions. This might provide a model with even more explanatory power than observed here.

The measurements of purchase intention were based on self-report online survey. Thus, the actual preferences might be different than their real choices. This could present some degree of bias (apart from cultural bias) in the responses provided by the respondents (Aadland & Caplan, 2003; Johnston, 2006; Stevens, Tabatabaei, & Lass, 2013). The preference of products is mainly dependent on how positive or negative the buyers are with a particular product. The magnitude of hypothetical bias can vary from product to product (Weisser, 2014). Prior research argues that it is mainly dependent on the type of product and the design of the survey instrument used (List & Gallet, 2001; Little & Berrens, 2004; Loomis, 2013; Murphy, Allen, Stevens, & Weatherhead, 2005; Weisser, 2014). No conclusion can be made on the fact that the size of hypothetical bias can be dependent on the type of product, nevertheless there is still some degree of uncertainty whether the choices made by consumers are close to actual. Consequently, the self-reporting methods have disadvantages related to the accuracy of the information provided by the participants.

To avoid weaknesses of self-reporting methods and get more accurate information of the actual behavior it would be useful to conduct longitudinal studies or lab experiments. These types of studies could be conducted with the same sample or subjects repeatedly over a specific period of time. This will provide insight into how the respondent’s preferences change over a time period. Future research should be extensively conducted in the children furniture market in India. The research should continue to identify the social changes and link them to the global trends to identify several marketing strategies. This could be of interest to the foreign entrants, willing to expand and launch new products in the market. Furthermore, it could assist in validating the findings of this study. In addition, further theoretical research should also be carried out in the similar field to enable cross-comparisons.

Future research should also look at the influence of children and syncretic decision-making in non-traditional family types. The role of children in decision-making has increased over the past several decades (Schiffman et al., 2012; Tinson & Nancarrow, 2005). Thus, it would be also useful to test how influence of children in decision-making could influence purchase intention in product categories where he or she has impact on parent’s decision-making. Other types of families, e.g. homosexual couples (Schneider, Schönenberg, & Ferié, 2013), non-married couples (Razzouk, Seitz, & Prodigalidad Capo, 2007), etc., should be explored in future research.

Literature

Aadland, D., & Caplan, A. J. (2003). Willingness to pay for curbside recycling with detection and mitigation of hypothetical bias. American Journal of Agricultural Economics, 85, 492–502.

Ajzen, I. (1985). From intentions to actions: a theory of planned behavior. In P. D. J. Kuhl & D. J. Backmann (Eds.), Action Control: From Cognition to Behavior (pp. 11–39). Berlin, Germany: Springer Berlin Heidelberg.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Process, 50(2), 179–211. doi: 10.1016/0749-5978(91)90020-T

Ajzen, I. (2005). Attitudes, Personality and Behavior (2 ed.). Maidenhead: Open University Press.

Ajzen, I. (2013). Theory of planned behavior questionnaire. Retrieved from www.midss.org

Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior (1 ed.). Englewood Cliffs New York: Prentice-Hall.

Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracin, B. T. Jonson, & M. P. Zenna (Eds.), The Handbook of Attitudes (pp. 173–221). Erlbaum: NJ Mahwah.

Al-Nahdi, T. S., Habib, S. A., Bakar, A. H. A., Bahklah, M. S., Ghazzawi, O. H., & Al-Attas, H. A. (2015). The effect of attitude, dimensions of subjective norm, and perceived behavior control, on the intention to purchase real estate in saudi arabia. International Journal of Marketing Studies, 7(5), 120–131.

Arvola, A., Vassallo, M., Dean, M., Lampila, R., Saba, A., Lahteenmaki, L., & Shepherd, R. (2008). Predicting intentions to purchase organic food: the role of affective and moral attitudes in the Theory of Planned Behavior. Appetite, 50, 443–454.

Ashraf, M., & Khan, K. M. (2016). Adolescents’ Role in Family Decision Making for Services in India. Young Consumers, 17(4).

Barber, N., Kuo, P. J., Bishop, M., & Goodman, R. (2012). Measuring psychographics to assess purchase intention and willingness to pay. Journal of Consumer Marketing, 29(4), 280–292.

Bateman, I., & Munro, A. (2005). An experiment on risky choice amongst households. Economic Journal, 115(502), C176-C189.

Beatty, S. E., & Talpade, S. (1994). Adolescent influence in family decision making: a replication with extension. Journal of Consumer Research, 21(2), 332–341.

Bednarik, E., & Kovats, J. P. (2010). Consumer behaviour model on furniture market. Acta Silvatica & Lignaria Hungarica, 6, 75–88.

Bedway, B. (1996). How to help your kids buy a house. Money, 25(11).

Burgoyne, C. B. (1995). Financial organization and decision-making within western “households”. Journal of Economic Psychology, 16, 421–430.

Chavda, H., Haley, M., & Dunn, C. (2006). Adolescents’ influence on family decisionmaking. Young Consumers, 6(3), 68–78.

Chrisman, J. J., Chua, J. H., pearson, A. W., & Barnett, T. (2010). Family Involvement, Family Influence, and Family-Centered Non-Economic Goals in Small Firms. Entrepreneurship Theory and Practice, 36(2), 267–293.

Cotte, J., & Wood, S. L. (2004). Families and innovative consumer behavior: a triadic analysis of sibling and parental influence. Journal of Consumer Research, 31(1), 78–86.

Crispell, D. (1995). Dual – earner diversity. American Demographics, 32–37.

David, H. L. (1970). Dimensions of marital roles in consumer decision making. Journal of Marketing Research, 7, 168–177.

Davis, G. B., & Olsen, M. H. (1985). Management Information System: Conceptual Foundations, Structure, and Development (2 ed.). New York: McGraw Hill.

Davis, H. L. (1976). Decision making within household. Journal of Consumer Research, 241–260.

Davis, H. L., & Rigaux, B. P. (1974). Perception of marital roles in decision processes. Journal of Consumer Research, 1(1), 51–62.

Dortch, S. (1994). Money and marital discord. American Demographics, 11(3).

Dunifon, R. (2012). The Influence of Grandparents on the lives of Children and Adolescents. Child Developement Perspectives, 7(1), 55–60.

Dunifon, R., & Bajracharya, A. (2012). The Role of Grandparents in the Lives of Youth. Journal of Family Issues, 33(9), 1168–1194.

Ekstrom, K. M., Tansuhaj, P. S., & Foxman, E. R. (1987). Children's influence in family decisions and consumer socialization: a reciprocal view. Advances in Consumer Research, 14(1), 283–287.

Epp, A. M., & Price, L. L. (2008). Family Identity: A framework of Identity Interplay in Consumption Practices. Journal of Consumer Research, 35(1), 50–70.

Fishbein, M., & Ajzen, I. (2010). Predicting and Changing Behavior: The Reasoned Action Approach. New York: Psychology Press.

Ford, J. B., LaTour, M. S., & Henthorne, T. L. (1995). Perception of marital roles in purchase decision processes: A cross-cultural study. Journal of the Academy of Marketing Science, 23(120). doi: 10.1177/0092070395232004

Foxman, E., Tanushaj, P., & Ekstrom, K. M. (1989). Family members' perceptions of adolescents' influence in family decision making. Journal of Consumer Research, 15(4), 482–491.

George, J. F. (2004). The theory of planned behavior and internet purchasing Internet Research, 14(3), 198-212. doi: 10.1108/10662240410542634

Giles, C. M., McClenahan, C. E., & Mallet, J. (2004). An application of the Theory of Planned Behaviour to blood donation: the importance of self-efficacy. Health Education Research, 19(4), 380-391. doi: 10.1093/her/cyg063

Granbois, D. H. (1971). A multi-level approach to family role research. Paper presented at the Second Annual Conference of the Association for Consumer Research.

GVU. (1998). GVU’s Tenth WWW user survey. Retrieved from https://www.cc.gatech.edu/gvu/user_surveys/

Harcar, T., Spillan, J. E., & Kucukemiroglu, O. (2005). A multinational study of family decision making. Multinational Business Review, 13(2), 3–21. doi: 10.1108/1525383X200500006

Hardin-Fanning, F., & Ricks, J. M. (2017). Attitudes, social norms and perceived behavioral control factors influencing participation in a cooking skills program in rural Central Appalachia. Global Health Promotion, 24(4), 43–52.

Hawkins, D. L., Best, R. J., & Coney, K. A. (2001). Consumer Behavior: Building Marketing Strategy (9 ed.). New York: McGraw Hill.

Johnston, R. (2006). Is hypothetical bias universal? validating contingent valuation responses using a binding public referendum. Journal of Environmental Economics and Management, 52, 469–481.

Juster, F. T. (1966). Consumer buying intentions and purchase probability: An experiment in survey design. Journal of American Statistical Association, 61, 658–696.

Kahle, L. R., & Close, A. (2006). Consumer Behaviour Knowledge for Effective Sports and Event Marketing. New York, USA: Taylor & Francis.

Ketabi, S. N., Ranjbarian, B., & Ansari, A. (2014). Analysis of the effective factors on online purchase intention through theory of planned behavior. International Journal of Academic Research in Business and Social Sciences, 4(4), 374–382.

Kidwell, B., & Jewell, R. D. (2003). An examination of perceived behavioral control: Internal and external influence on intention. Psychology and Marketing, 20(7), 625–645.

Kim, L. H., Kim, D.-J., & Leong, J. K. (2005). The effect of perceived risk on purchase intention in purchasing airline tickets online. Journal of Hospitality & Leisure Marketing, 13(2), 33–53.

King, W. G. (1975). An analysis of attitudinal and normative variables as predictors of intentions and behavior. Speech Monographs, 42, 237–244.

Kirchler, E. (1993). Spouse joint purchase decision: determinants of influence tactics for muddling through the process. Journal of Economic Psychology, 14, 405–438.

Lackman, C., & Lanasa, J. M. (1993). Family decision-making theory: An overview and assessment Psychology and Marketing, 10(2), 81–93.

Lackman, C., & Lansa, J. M. (1993). Family decision-making theory: an overview and assessment. Psychology and Marketing, 10(2), 81–93.

Lavin, M. (1993). Husband-dominant, wife-dominant, joint. Journal of Consumer Marketing, 10(3), 33–42.

Levy, D. S., & Lee, C. K. C. (2004). The Influence of family members on housing purchase decisions. Journal of Property Investment and Finance, 22(4), 320–338.

Liang, J.-m., & Wei, H.-y. (2011). Impact of perceived risk on purchase intention in product-harm crisis. Paper presented at the International Conference on Information Systems for Crisis Response and Management (ISCRAM), Harbin, Heilongjiang.

List, A. J., & Gallet, A. C. (2001). What experimental protocol influence disparities between actual and hypothetical stated values. Environmental and Resource Economics, 20, 241–254.

Little, J., & Berrens, R. (2004). Explaining disparities between actual and hypothetical stated values: further investigation using meta-analysis. Economics Bulletin, 3, 1–13.

Litvin, S. W., Xu, G., & Soo, K. K. (2004). Spousal vacation-buying decision making revisited across time and place. Journal of Travel Research, 43(2), 193–198.

Loomis, B. J. (2013). WAEA keynote address: strategies for overcoming hypothetical bias in stated preference surveys. Journal of Agricultural and Resource Economics, 39(1), 34–46.

Mausbach, B. T., Moore, R. C., Davine, T., Cardenas, V., Bowie, C. R., Ho, J., & Patterson, T. L. (2013). The use of the theory of planned behavior to predict engagement in functional behaviors in schizophrenia. Psychiatry Research, 205(1), 36–42.

Mitchell, V.-W., Davies, F., Moutinho, L., & Vassos, V. (1999). Using neural networks to understand service risk in the holiday product. Journal of Business Research, 46(2), 167–180.

Moutinho, L., Ballantyne, R., & Rate, S. (2011). Consumer behavior in Tourism. In L. Moutinho (Ed.), Strategic Management in Tourism (2 ed.). Cambridge: CAB International.

Murphy, J. J., Allen, P. G., Stevens, T. H., & Weatherhead, D. (2005). A meta-analysis of hypothetical bias in stated preference valuation. Environmental and Resource Economics, 30, 313–325.

Nejdet, D. (1994). Religious contrasts in consumer decision behaviour patterns: their dimensions and marketing implications. European Journal of Marketing, 28(5), 36–45.

Nelson, O. N., & Jenny, K. (2005). Family structure and joint purchase decisions Management Research News, 29(1/2), 53–64.

Omar, N. A., Nazri, M. A., Osman, L. H., & Ahmad, M. S. (2016). The effect of demographic factors on consumer intention to purchase organic products in the Klang Valley: an empirical study. Malaysian Journal of Society and Space, 12(2), 68–82.

Pahl, J. (1995). His money, her money: recent research on financial organization in marriage. Journal of Economic Psychology, 16, 361–376.

Palma, A. d., Picard, N., & Ziegelmeyer, A. (2008). Individual and couple decision behavior under risk: The power of ultimate control. Papers on Strategic Interaction.

Parkinson, J., Gallegos, D., & Russel-Banette, R. (2016). Transforming beyond self: fluidity of parent identity in family decision-making. Journal of Business Research, 69(1), 110–119.

Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior. MIS Quarterly, 30, 115–143.

Qualls, W. J. (1984). Sex roles, husband-wife influence, and family decision behavior. Advances in Consumer Research, 11(1), 270–275.

Raja, J. S. (2014). Actually, the nuclear family is on the decline in India. Retrieved from http://qz.com/228405/india-is-urbanizing-rapidly-but-the-nuclear-family-is-actually-shrinking/

Rani, P. (2014). Factors influencing consumer behavior. International Journal of Current Research and Academic Review, 2(9), 52–61.

Razzouk, N., Seitz, V., & Prodigalidad Capo, K. (2007). A comparison of consumer decision-making behavior of married and cohabiting couples. Journal of Consumer Marketing, 24(5), 264–274.

Ruth, J., & Commuri, S. R. (1998). Shifting roles in family decision making. In J. W. Alba & J. W. Hutchinson (Eds.), Advances in Consumer Research (Vol. 25, pp. 400–406). Provo, Utah: Association for Consumer Research.

Samadi, M., & Yaghoob-Nejadi, A. (2009). A Survey of the Effect of Consumers’ Perceived Risk on Purchase Intention in E-Shopping. Business Intelligence Journal, 2(2).

Samin, R., Goodarz, J. D., Muhammad, S. R., Firoozeh, F., Mahsa, H., & Sanaz, E. (2012). Conceptual study on the country of origin effect on consumer purchase intention. Asian Social Science, 8(12), 205–215.

Schiffman, L. G., Kanuk, L. L., & Hansen, H. (2012). Consumer behaviour – A European Outlook (2 ed.). Essex, England: Pearson Education Ltd.

Schneider, H., Schönenberg, I., & Ferié, F. (2013). The distribution of influence in purchase decisions by male homosexual couples. Journal of Consumer Behaviour, 12(5), 345–357. doi: 10.1002/cb.1432

Seawall, M. A. (1978). Market segmentation based on consumer ratings of proposed product design. Journal of Marketing Research, 15, 557–564.

Seymour, D., & Lessne, G. (1984). Spousal conflict arousal: scale development. Journal of Consumer Research, 11, 810–821.

Sheth, J. N. (1973). A model of industrial buyer behavior. Journal of Marketing, 37(4), 50–56. doi: 10.2307/1250358

Sheth, J. N. (1974). Models of Buyer Behavior: Conceptual, Quantitative and Empirical. New York: Harper & Row.

Sheth, J. N., & Vankatesan, M. (1968). Risk reduction process in repetitive consumer behavior. Journal of Marketing Research, 5(3), 307–310.

Silk, A. J., & Urban, G. L. (1978). Pre-test market evalualtion of new product goods: a model and measurement methodology. Journal of Marketing Research, 15, 171–191.

Smith, J. P., McArdle, J. J., & Willis, R. (2010). Family Decision Making and Cognition in a Family Context. The Economic Journal, 120(548), 363–380.

Solomon, M., Bamossy, G., Askegaard, S., & Hogg, M. K. (2007). Consumer Behavior: A European Perspective. (3 Ed. Vol. 29). Harlow: Financial Times/ Prentice Hall.

Sommer, L. (2011). The Theory Of Planned Behaviour And The Impact Of Past Behaviour. International Business & Economics Research Journal, 10(1).

Sondhi, N., & Basu, R. (2014). Role of children in family purchase across Indian clusters. International Journal of Advertising and Marketing to Children, 15(4), 365–379.

Stevens, T. H., Tabatabaei, M., & Lass, D. (2013). Oaths and hypothetical bias. Journal of Environmental Management, 127, 135–141.

Sullivan, G. L., & Connor, P. J. O. (1988). The family purchase decision process: a cross-cultural review and framework for research. Southwest Journal of Business and Economics, 43.

Sun, B., & Morwitz, V. G. (2010). Stated intentions and purchase behavior: A unified model. International Journal of Research in Marketing, 27(4), 356–366.

Sweeney, J. C., Soutar, G. N., & Johnson, L. W. (1999). The role of perceived risk in the quality-value relationship: a study in a retail environment. Journal of retailing, 75(1), 77–105.

Szybillo, G. J., & Sosanie, A. (1977). Family Decision Making: Husband, Wife and Children. Paper presented at the Advances in Consumer Research, Atlanta.

Taylor, J. W. (1974). The role of risk in consumer behaviour. Journal of Marketing, 38, 54–60.

Timmins, N. (1996). New Man fails to make it into the Nineties; Social Trends: Women still do most housework. The Independent. https://www.independent.co.uk/news/new-man-fails-to-make-it-into-the-nineties-social-trends-women-still-do-most-housework-1325635.html

Tinson, J., & Nancarrow, C. (2005). The influence of children on purchases. International Journal of Market Research, 47(1), 5–27.

Urban, G. L., & Hauser, J. R. (1993). Design and Marketing of New Products (2 ed.). Englewood.

Valkenburg, P. M., & Cantor, J. (2001). The development of a child into a consumer. Journal of Applied Developmental Psychology, 22(1), 61–72.

Wang, K. C., Hsieh, A. T., Yeh, C. Y., & Tsai, C. W. (2004). Who is the decision-maker: the parents or the child in group package tours? Tourism Management, 25(2), 183–194. doi: 10.1016/S0261-5177(03)00093-1

Ward, S. (1974). Consumer socialization. Journal of Consumer Research, 1(2), 1–14. https://doi.org/10.1086/208584

Weisser, R. A. (2014). How real is hypothetical bias in the context of risk and time preference elicitation. Journal of Economic Literature, 18(80), 1–35.

Whitlark, D. B., Geurts, M. D., & Swanson, M. J. (1993). New product forecasting with a purchase intention survey. Journal of Business Forecasting: methods and systems, 12(3).

Yazdanpanaha, M., & Forouzania, M. (2015). Application of the theory of planned behaviour to predict Iranian students intention to purchase organic food. Journal of Cleaner Production, 107(16), 342–352.

Zhao, R., Geng, Y., Liu, Y., Tao, X., & Xue, B. (2018). Consumers' perception, purchase intention and willingness to pay for carbon-labeled products: A case study of Chengdu in China. Journal of Cleaner Production, 171, 1664–1671.

Appendix

Table 1:

Questionnaire measures and sources

IDIndicatorsReferences
Purchase intention
Int 1 I would intend to purchase high-end furniture for my children (from very unlikely to very likely)Adopted from: Ajzen (2013)
Int 2 I would plan purchase high-end Scandinavian furniture for my children (from very unlikely to very likely)
Int 3 I would make an effort to purchase high-end furniture for my children (from I definitely wouldn't to I definitely would)
Perceived behavioral control
Con 1 I am confident that I can buy high-end furniture for my children by myself (from almost never true to almost always true)Adopted from: Fishbein and Ajzen (2010)
Con 3 Purchase of high-end furniture for my children is up to me(from almost never true to almost always true)
Con 2 Purchase of high-end furniture for my children is entirely within my control (from almost never true to almost always true)Adopted from: Pavlou and Fygenson (2006)
Spousal influence
Sp 1 Between you and your spouse who recognizes the need to purchase furniture for your children? (from only me to only spouse)Adopted from: H. L. Davis and Rigaux (1974)
Sp 2 Between you and your spouse who obtains most of the information with respect to purchase of furniture? (from only me to only my spouse)
Sp 3 Between you and your spouse who provides and collects information about possible alternatives? (from only me to only spouse)
Sp 4 Between you and your spouse who makes final choice when it comes to purchase of furniture for children? (from only me to only spouse)Adopted from: H. L. Davis and Rigaux (1974), Nejdet (1994)
Beliefs about expected outcomes (used in the calculation of attitudes toward the behavior)
Bel 1 Purchase of high-end furniture for my children will result in improvement of his/her life quality (from very unlikely to very likely)Adopted from: Fishbein and Ajzen (2010)
Bel 2 Purchase of high-end furniture for my children could result in improvement of his/her comfort (from very unlikely to very likely)
Bel 3 I believe that purchase of high-end furniture from Scandinavia for my children will help to improve his/her performance in daily activities (from very unlikely to very likely)
Evaluation of expected outcomes (used in the caclulation of attitudes toward the behavior)
Eva 1 My having high-end furniture for my children is… (from very bad to very good)Adopted from: Fishbein and Ajzen (2010)
Eva 2 Spending money on purchase of high-end furniture for my children is… (from extremely bad to extremely good)
Eva 3 My having high-end Scandinavian furniture for my children is…(from very bad to very good)
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